The Complex Epidemiological Relationship between Flooding Events and Human Outbreaks of Mosquito-Borne Diseases: A Scoping Review
Publication: Environmental Health Perspectives
Volume 129, Issue 9
CID: 096002
Abstract
Background:
Climate change is expected to increase the frequency of flooding events. Although rainfall is highly correlated with mosquito-borne diseases (MBD) in humans, less research focuses on understanding the impact of flooding events on disease incidence. This lack of research presents a significant gap in climate change–driven disease forecasting.
Objectives:
We conducted a scoping review to assess the strength of evidence regarding the potential relationship between flooding and MBD and to determine knowledge gaps.
Methods:
PubMed, Embase, and Web of Science were searched through 31 December 2020 and supplemented with review of citations in relevant publications. Studies on rainfall were included only if the operationalization allowed for distinction of unusually heavy rainfall events. Data were abstracted by disease (dengue, malaria, or other) and stratified by post-event timing of disease assessment. Studies that conducted statistical testing were summarized in detail.
Results:
From 3,008 initial results, we included 131 relevant studies (dengue , malaria , other MBD ). Dengue studies indicated short-term () decreases and subsequent (1–4 month) increases in incidence. Malaria studies indicated post-event incidence increases, but the results were mixed, and the temporal pattern was less clear. Statistical evidence was limited for other MBD, though findings suggest that human outbreaks of Murray Valley encephalitis, Ross River virus, Barmah Forest virus, Rift Valley fever, and Japanese encephalitis may follow flooding.
Discussion:
Flooding is generally associated with increased incidence of MBD, potentially following a brief decrease in incidence for some diseases. Methodological inconsistencies significantly limit direct comparison and generalizability of study results. Regions with established MBD and weather surveillance should be leveraged to conduct multisite research to a) standardize the quantification of relevant flooding, b) study nonlinear relationships between rainfall and disease, c) report outcomes at multiple lag periods, and d) investigate interacting factors that modify the likelihood and severity of outbreaks across different settings. https://doi.org/10.1289/EHP8887
Introduction
Mosquito-borne diseases (MBD) cause a significant proportion of infectious disease morbidity and mortality globally. Although declines in the disease burden of some, chiefly malaria (Cibulskis et al. 2016), have been achieved in the past several decades, others such as dengue (Messina et al. 2019) and West Nile virus (WNV; Chancey et al. 2015) have increased substantially in geographic range and incidence. Newly emerging MBD such as Zika and chikungunya viruses have become pandemics within just the last decade (Chang et al. 2016a), whereas others like Rift Valley fever (RVF) virus and WNV remain persistent threats. In the coming decades, models predict that global warming will cause regional shifts in the distribution of key vector species, leading to an overall global increase in the risks of MBD that will be more severe at 2°C than 1.5°C of warming (IPCC 2018). Changes in average temperature and rainfall patterns are also anticipated to be accompanied by increased frequency and intensity of extreme weather events, such as tropical cyclones (cyclones, hurricanes, and typhoons), floods, and unusually heavy rainfall events (IPCC 2012; U.S. Global Change Research Program 2018). Increasing sea level rise is further linked to increasing risk of tsunami-related flooding (Li et al. 2018; Shao et al. 2019). How these extreme weather events will influence the distribution and level of MBD has not been consistently evaluated and may differ by disease and for the same disease in different geographic, social, or temporal contexts.
Floods are described in myriad ways worldwide depending on location (land surface, geomorphology, hydrology) and duration. For example, in the United States, the National Flood Insurance Program (NFIP) describes a flood as “A general and temporary condition of partial or complete inundation of two or more acres of normally dry land area or of two or more properties … from: overflow of inland or tidal waters; unusual and rapid accumulation or runoff of surface waters from any source; mudflow; or collapse or subsidence of land along the shore of a lake or similar body of water as a result of erosion or undermining caused by waves or currents of water exceeding anticipated cyclical levels that result in a flood as defined above” (NFIP 2015). The U.S. National Weather Service issues a flood warning for “flooding that lasts 6 h or more over a general area and/or affects mainstream rivers that is a threat to life/property,” whereas a flash flood warning is for flooding that produces a life or property threat over a period of 0–6 h (National Weather Service n.d.). The World Meteorological Organization recognizes the challenges of forecasting and defining flash floods worldwide, and the organization promotes a focus on intensity of rainfall and the impact of rainfall on saturated soils (World Meteorological Organization 2021). The Flash Flood Guidance System with Global Coverage (FFGS) is designed to assist with forecasting implementation worldwide (https://public.wmo.int/en/projects/ffgs). The inherent variability in defining floods in different global contexts presents challenges for standardization and generalization in studies of their impacts, including impacts on MBD.
MBD transmission is regulated by a complex set of environmental and social determinants that affect the proliferation, survival, and interactions of pathogens, mosquito species, and host(s) (Parham et al. 2015). The dynamic of each specific pathogen–vector–host relationship is unique. One commonality, however, is the need for an aquatic habitat for immature life stages of the mosquito (Foster and Walker 2019). Rainfall, humidity, and temperature affect habitat availability, mosquito development time, fecundity, feeding behaviors, and survival (Christophers 1960; Clements 1992; Morin et al. 2013). Recently, much attention has been directed toward predicting how climate change–associated shifts in these factors will drive temporal and geographic changes in MBD risk (Akpan et al. 2019; Caminade et al. 2014a; Hertig 2019; Kamal et al. 2018; Kraemer et al. 2019; Liu-Helmersson et al. 2019; Messina et al. 2019; Monaghan et al. 2018; Ryan et al. 2019; Samy et al. 2016). Often these predictions focus on how projected average temperature and, less commonly, rainfall patterns may change temporal and geographic risk. Many studies have compared long-term rainfall and disease surveillance data but modeled precipitation as a linear predictor variable without exploring potential nonlinearity after extreme rainfall events. However, some evidence suggests that heavy rainfall can actually flush out oviposition sites, particularly in the immediate time periods following these events (Benedum et al. 2018). Models that fit rainfall as a linear predictor may miss such nonlinear impacts of flood events linked to heavy rainfall.
In fields that study MBD, we characterize vectorial capacity, a measure that quantifies transmission risk, as comprising multiple parameters, including a) mosquito density, b) the human-biting rate, c) survival of the mosquito, d) the extrinsic incubation period, and e) vector competence. As illustrated in Figure 1, we hypothesize many direct and indirect influences on these parameters during flooding and inundation events that could contribute to variation of the effects of flooding on different pathogens and for the same pathogen in different spatial or temporal settings. Mosquito density may be positively or negatively influenced, depending on characteristics of the local mosquito vectors and other contextual factors. For instance, although higher frequency or intensity of extreme flooding events may increase available immature-stage mosquito habitat by creating more stagnant water pools or spreading detritus that may serve as a habitat for container-breeding mosquitoes, the relationship may be neither linear nor temporally consistent if breeding sites are flushed out in the short term. These relationships would not necessarily generalize to other MBDs transmitted by mosquito species that breed in larger pools. Another example is coastal flooding from storm surges that may leave large areas of land inundated with flood waters, but many important vector species cannot breed easily in high-salinity water (Kengne et al. 2019); thus the context of flooding events is critical. Overall, the impacts of flooding events on mosquito density may be mediated by factors such as the typical climate (e.g., rainfall, temperature, humidity) patterns of the region, the salinity of the water, local water storage practices, lag period of consideration after the event, and human mitigation/vector control efforts.
Survival of the adult mosquito and the extrinsic incubation period for the disease link to a singular epidemiologically relevant concept, survival past the extrinsic incubation period. Higher humidity conditions are associated with increased mosquito survival at high temperatures (Schmidt et al. 2018). Following rainfall and flooding events, humidity would be anticipated to be high, though in tropical environments, the increase may not be large. If coupled with limited temperature changes, it is theoretically possible that more mosquitoes could survive past the extrinsic incubation period after flooding than before it. Alternately, although adult mosquitoes can avoid being killed by raindrops in flight, they can be killed by them when flying too close to the ground (Dickerson et al. 2012). Finally, vector competence for transmission of a specific pathogen should remain unaffected, but as noted above, flooding and inundation events could alter the species composition in an area toward or away from a more competent vector species.
Furthermore, much research on rainfall has focused on changes in mosquito population distributions as the primary outcome rather than human disease incidence, but mosquito abundance does not always directly correlate with intensity of pathogen transmission (Barrera et al. 2019; Ernst et al. 2017; Mori et al. 2018), because other parameters of vectorial capacity may influence the disease dynamics. For instance, the human-biting rate may also be influenced by flooding. Vector–human contact is mediated by the built environment. Flooding and inundation events, particularly those associated with hurricanes, may degrade housing quality, leading to more penetrable housing and increased vector–human contact (Crandell et al. 1993; Massarra et al. 2021). Such events may also displace individuals to outdoor, semipermanent locations, depending on the extent of damage, and further reduce barriers to vector–human contact (Loebach and Korinek 2019). A complex constellation of social and socioeconomic factors influence the frequency of mosquito contacts with humans and potential zoonotic hosts, such as housing construction quality (Tusting et al. 2015), population density (Schmidt et al. 2011), land use (Norris 2004; Steiger et al. 2016), and access to interventions, such as mosquito repellent and breeding site management (McCall et al. 2009). We hypothesize that such factors will also mitigate the impacts of flooding on incidence of different diseases in different social/geographic contexts (Figure 1).
Given the challenges in predicting the specific timing and location of weather disaster events, we argue that strong epidemiological evidence regarding a causal relationship between extreme events and MBD frequencies requires study sites with systematic disease surveillance, enabling testing of trends over time, rather than solely descriptive or reactionary reports of perceived outbreaks following extreme events. However, in the wake of a disaster, infrastructure damages can make it particularly challenging to monitor resulting infectious disease burdens, even in places where predisaster data are regularly collected (Iwata et al. 2013). Thus, although a direct relationship between flooding and mosquito-borne disease transmission has often been assumed, data to support generalizable causal inferences have been limited, and there has been no systematic aggregation of the full body of evidence to this point. Understanding the potential strength, direction, and timing of these causal relationships is critical for disaster response preparedness and prediction of disease frequencies in the context of climate change. Therefore, we undertook a scoping review of the associations between extreme rainfall or flooding events and MBD frequencies in human populations. The goals of the review were to characterize the potential relationship, to determine the gaps in knowledge, and to better inform future projections of disease risk.
Methods
Search Strategy
We undertook a scoping review of the literature to identify all primary published data relating to the occurrence of MBD in humans after flooding events. To better understand how inundation events influence MBD, we took a broad view, including flooding due to heavy rainfall, flooding associated with the El Niño–Southern Oscillation (ENSO) or Pacific Decadal Oscillation (PDO), and flooding of land due to other disasters like tsunamis. The methods were guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses–Extension for Scoping Reviews (PRISMA-ScR) statement (Tricco et al. 2018). Anticipating high variability in study designs and available data, we planned a qualitative summary without a specific meta-analysis. We searched PubMed, Embase, and Web of Science for all studies included through 31 December 2020, with no lower limit on publication date. The search used flood-related terms as the exposure and mosquito-borne disease terms as the outcome, as follows:
Exposure: (flood OR tsunami OR typhoon OR “tropical storm” OR hurricane OR “El Nino” OR “La Nina” OR ((extreme OR heavy OR unseasonal) AND (rain OR rainfall OR precipitation)) OR monsoon OR “storm surge” OR (disaster AND (climate OR weather)) OR ENSO OR PDO)
AND
Outcome: (((mosquito OR mosquitos OR mosquitoes OR mosquito-borne OR mosquitoborne OR “mosquito borne” OR vector OR vectors OR vectorborne OR vector-borne OR “vector borne” OR aedes OR anopheles OR culex) AND (disease OR infection)) OR “west nile virus” OR chikungunya OR dengue OR zika OR “yellow fever” OR “lymphatic filariasis” OR elephantiasis OR malaria OR Plasmodium OR “rift valley” OR encephalitis)
Throughout the course of screening and data abstraction, we regularly scanned bibliographies of relevant studies to identify and screen additional potentially relevant citations that had not appeared in the initial search results.
Study Selection
This review aimed to gather and assess the data available on any effects of flooding events on MBD rates in human populations, so the main inclusion criteria were a) primary reports of human health data relating to one or more MBD (even if the reported case count was zero) that b) evaluated flood disasters or very high rainfall events as an exposure. Retrospective outbreak reports were expected, given that many studies are conducted only following a disaster because of the short time frame for forecasting the event, and we anticipated a lack of consistency or formality across reports of potentially relevant data. Thus, the search included nearly all study designs, and we planned for a primarily qualitative summary of the literature. We also elected not to restrict the review to certain dates or geographies.
Exclusion criteria were developed before reviewing search results (Table 1). One of the challenges of this review was the large variety in how flooding and heavy rainfall were operationalized as exposures. Some studies reported on the impacts of specific extreme events (meteorological and other) that could lead to excess water presence on land, e.g., specific locally designated floods, tropical cyclones (hurricanes, typhoons, or cyclones), and tsunamis; others compared disease surveillance data to climatological monitoring data like daily, weekly, or monthly rainfall. Because heavy rainfall is a common cause of flooding, we included the latter for this review if the study operationalized rainfall variables in a way that allowed for nonlinear effects at high thresholds (e.g., using splines), which could potentially distinguish effects of flooding from those of average rainfall conditions. For example, included studies of heavy rainfall could quantify disease impacts when rainfall amount exceeded a study-specific threshold per time period or a threshold of deviation from local averages. Studies modeling rainfall only as a continuous linear predictor of disease and those describing typical seasonal variability were excluded. This selection method could potentially have excluded pertinent results from areas where flooding is common at all rainfall amounts due to poor drainage conditions, but in most cases the reporting of an average increase in cases per unit rainfall does not enable a distinction of rainfall amounts that would be sufficient to cause flooding events where water covers atypical land areas.
| Category | Exclusion criteria |
|---|---|
| Exposures | No flood-related exposure/study of regular annual rainfall variation |
| El Niño Southern Oscillation without assessment of flood events | |
| Outcomes | No vector-borne disease outcomes |
| Vector-borne disease with a non-mosquito vector | |
| Mosquito surveillance only (i.e., no human health data) | |
| Systems/response studies without human health outcome data | |
| Design | Nonhuman species/in vitro study |
| Case reports | |
| Reviews without primary data | |
| Corrections/revisions to other articles | |
| Opinion pieces/editorials/news articles | |
| Conference abstracts | |
| Predictive simulations without primary data | |
| Logistics | Duplicates |
| Full text not available | |
| Untranslated foreign language (i.e., not in English, French, Spanish, Italian, or Portuguese) |
Titles and abstracts of identified studies were each scanned for eligibility by two reviewers, with any discrepancies resolved by a third reviewer. Full texts were not accessible for 16 citations (see Excel Table S3). All remaining full-text documents that could not be excluded based on the title and abstract alone were obtained for further consideration. All full texts in English, French, Spanish, Italian, or Portuguese were then reviewed by two reviewers for final inclusion in the qualitative summary using the same inclusion/exclusion criteria, with a third reviewer again resolving any discrepancies.
Data Abstraction
Data were abstracted from relevant studies into preestablished templates in two phases. The first phase involved only key elements to help broadly categorize available data. These key elements included study continent, country/geographic region, years of data collection, mosquito genus/species, disease(s), and brief characterization of flooding operationalization. Given the specificity of the ecological niches of mosquito species and the social-cultural context of public health and disaster response systems, we elected to aggregate studies by disease for the next phase of data abstraction. This aggregation has limitations, because many pathogens can be vectored by multiple mosquito species. For example, dozens of mosquito species are competent vectors of human malaria parasites, each of which has different vector competence, behaviors, and interactions with humans (Foster and Walker 2019). The initial abstraction revealed that studies could be divided into three general disease categories for the second phase of data abstraction: a) dengue, b) malaria, and c) other.
Three teams of researchers then completed the second phase of detailed, narrative data abstraction within each disease category, including additional fields on the specific study location, typical climate, study design, source of disease data, details on the definition of the exposure, comments on the preflood disease frequency, and the postflood disease frequency. Latency time to postflood impacts, if reported, was abstracted in five broad windows of time: acute (within 1 wk), subacute (1 wk–1 month), medium term (1–4 months), long term (4 months–1 y), and subsequent years. Flooding exposure groups were retroactively classified as a) heavy rainfall metrics (above average rainfall, rainfall exceeding a threshold, flooded land/waterlogging/“flushing”); b) major flooding events [specific “flood” events, tropical cyclones (cyclones, hurricanes, or typhoons), tsunami]; or c) other/unspecified (see Table 2 for details). Studies in which a statistical test was performed and reported for the association between flood and MBD were considered higher-quality evidence and were evaluated in detail for this paper. Team leaders then reviewed all abstracted data and compiled final summary tables and narratives. Full details of the screening and abstraction on all citations are available in Excel Tables S1 (detailed exclusion criteria), S2 (title/abstract screening), S3 (full text screening), S4 (initial data abstraction), S5 (dengue detailed data abstraction), S6 (malaria detailed data abstraction), S7 (detailed data abstraction on other diseases), and S8 (details on studies screened from the reference lists of relevant studies).
| Category assigned in this review | Description of flood operationalization | Example studies |
|---|---|---|
| Major flooding events | ||
| Specific flood event(s) | Single events described as floods, e.g., Heavy rains and flash flooding that were the “worst in 25 years” | (Elsanousi et al. 2018; Gao et al. 2016) |
| Presence/absence of flood dichotomized weekly based on times when news and development authorities reported “nearly all major city roads impassable due to flood” | (Carvajal et al. 2018) | |
| Presence/absence of flood dichotomized for each 10-d period based on recording in the “Yearbooks of Meteorological Disasters Information Data set” | (Ding et al. 2019) | |
| Number of floods in a country in a year as recorded by the Emergency Events Database (EM-DAT) of the Center for Research on the Epidemiology of Disasters (CRED) | (Kaur et al. 2020) | |
| Tropical cyclones (cyclone, hurricane, or typhoon) | Single events described as a type of tropical cyclone (e.g., Hurricane Mitch, Hurricane Katrina) | (Caillouët et al. 2008, Campanella, 1999) |
| Exposure period was days that a city experienced a tropical cyclone with a level-7 wind circle and above, satisfying one of three additional rainfall or windspeed criteria | (Zheng et al. 2017) | |
| Tsunami | Single events described as tsunamis | (Briët et al. 2006; Manimunda et al. 2011) |
| Heavy rainfall metrics | ||
| Above average rainfall | Deviation (in millimeters) from the average or median cumulative rainfall over a time period (i.e., monthly, weekly, seasonal) | (Arcari et al. 2007; Kakarla et al. 2019; Pramanik et al. 2020) |
| 4-month cumulative rainfall anomaly (difference from average each month, summed for 4-month exposure periods) | (Anyamba et al. 2012) | |
| Heavy rainfall years (where monthly average total rainfall was 183% or 205% higher than previous 3 y) | (Himeidan et al. 2007) | |
| Number of very heavy precipitation days in a year (annual count of days with precipitation ) | (Méndez-Lázaro et al. 2014) | |
| Total rainfall from extremely wet days (days with rainfall percentile) | (Méndez-Lázaro et al. 2014) | |
| Maximum 24-h rainfall that occurred during a month | (Yuan et al. 2019) | |
| Rainfall exceeding a threshold | Daily, weekly, or monthly cumulative rainfall exceeding study-specific thresholds | (Cabrera and Taylor 2019; Chen et al. 2012; Chien and Yu 2014; Liyanage et al. 2016; Zhang et al. 2016) |
| A week of heavy rainfall defined as weeks with days of rainfall | (Soverow et al. 2009) | |
| Cumulative rainfall over a time period, evaluated for non-linearity (shifts in slope at high values) | (Ramadona et al. 2016; Singh et al. 2020; Xiao et al. 2018) | |
| Monthly rainfall over 6 months that was standard deviations above average | (Lowe et al. 2018) | |
| Flooded land/waterlogging/“flushing” | River gage height, river level | (Mori et al. 2018; Tall and Gatton 2020; Wolfarth-Couto et al. 2020) |
| Flooding and waterlogging events (dichotomized). of surface runoff that submerges towns and farmland, which is often caused by long-lasting heavy storms.” , wet damage, moisture damage, and is often caused by long lasting rainfall without a heavy precipitation intensity.” | (Ding et al. 2014) | |
| Monthly flood discharge (in mm3/month) | (Chirebvu et al. 2016) | |
| Flood extent measured as area covered by water that is not normally covered by water based on satellite images (e.g., in square kilometers, or calculating “normalized flooding index”) | (Chirebvu et al. 2016; Rattanavong et al. 2020; Saulnier et al. 2018) | |
| Dichotomized at the household level based on whether or not residents reported “stagnant water around households for 3-5 days” following a flood | (Nsereko et al. 2020) | |
Results
The search resulted in 2,876 de-duplicated citations, with an additional 132 potentially relevant novel citations identified through a review of the reference lists of screened papers, for a total of 3,008 publications screened (Figure 2). Of these, 131 studies were deemed relevant and included in the overall qualitative synthesis.
There was considerable variability in the operationalization of “flood” exposure. We categorized included studies based on whether they evaluated flooding from specific events (“Major flooding events”), which tended to be dichotomous identification of presence or absence of an event during a specific window of time, or whether they operationalized the exposure as heavy rainfall deviating from average or exceeding study-specific thresholds (“Heavy rainfall metrics”), which tended to involve analysis of a continuous variable in the form of rainfall, river height, or area of land covered by water. Details on some of the specific metrics found in the literature are presented in Table 2.
Forty-five studies reported on dengue, 61 reported on malaria, and 49 reported on other MBD, primarily RVF, WNV, Japanese encephalitis (JE), and Murray Valley encephalitis (MVE) (Table 3), with some studies reporting on multiple disease categories. Dengue and malaria studies predominantly used data from Asian countries, with a considerable number of malaria studies also reporting data from African countries. Studies reporting on other diseases were more evenly distributed across continents, though there were few studies on flood and any MBD from Europe or South America. At least one study on flooding and MBD from 52 countries or major geographic entities (Figure 3) was included, though only 9 of these countries were the subject of 5 or more published studies on the topic: India (16 studies), the United States (12 studies for the continental United States and 5 from Puerto Rico alone), Australia (11 studies), Sudan (8 studies), Kenya (7 studies), China (7 studies), Pakistan (6 studies), Indonesia (5 studies), and Tanzania (5 studies). Because our goal was to capture all published evidence related to extreme rainfall and flooding events, there was a large variety in study design and quality of included studies. Studies reporting potential outbreak occurrence that coincided with floods but lacking a comparison group (either from before the flood or in a comparable geographic area) for hypothesis testing were common. Only 51 studies from 24 nations/geographic entities had any studies that provided a statistical analysis quantifying the impact of flooding on MBD frequencies, with the majority of these reporting on dengue (Table 3; Figure 3D).
a
Other diseases included: Japanese encephalitis (8 studies (Balaraman et al. 2005; Chen et al. 2012; Ding et al. 2019; Gao et al. 2016; Knope et al. 2013; Rattanavong et al. 2020; Singh et al. 2020; Zhang et al. 2016)), Murray Valley Encephalitis (8 studies (Anderson, 1954; Anderson et al. 1958; Anyamba et al. 2014; Broom et al. 2003; Cordova et al. 2000; Doggett et al. 2001; Knope et al. 2013; Selvey et al. 2014)), Barmah Forest virus (2 studies (Doggett et al. 2001; Knope et al. 2013)), Ross River virus (4 studies (Doggett et al. 2001; Knope et al. 2013; McDonnell et al. 1994; Tall and Gatton 2020)), Rift Valley Fever (12 studies (Anyamba et al. 2009, 2012, 2014; Caminade et al. 2014b; Chretien et al. 2008; El Mamy et al. 2011; Gudo et al. 2016; Linthicum et al. 1999, 2010; McCarthy et al. 1996; Nderitu et al. 2011; Sow et al. 2014)), West Nile (or Kunjin) virus (13 studies (Beatty et al. 2007; Broom et al. 2003; Caillouët et al. 2008; Cordova et al. 2000; Doggett et al. 2001; Harrison et al. 2009; Hubálek et al. 2000, 2005; Knope et al. 2013; Lehman et al. 2007; McCarthy et al. 1996; Mori et al. 2018; Soverow et al. 2009)), Western Equine Encephalitis (4 studies (Anders et al. 1994; Gilliland et al. 1995; Nasci and Moore 1998; Reeves et al. 1964)), Eastern Equine Encephalitis (2 studies (Gilliland et al. 1995; Nasci and Moore 1998)), St. Louis Encephalitis (7 studies (Anders et al. 1994; Day and Curtis 1999; Gilliland et al. 1995; Hopkins et al. 1975; Lehman et al. 2007; Nasci and Moore 1998; Reeves et al. 1964)), Yellow fever (2 studies (Chretien et al. 2008; Knope et al. 2013)), Chikungunya (6 studies (Giribabu et al. 2020; Grossi-Soyster et al. 2017; Knope et al. 2013; McCarthy et al. 1996; Roiz et al. 2015; Ruiz et al. 2018)), Zika virus (3 studies (Barrera et al. 2019; Ruiz et al. 2018; Sorensen et al. 2017)), and one study each of Tahyna virus (Hubálek et al. 2005), Sindbis virus (Hubálek et al. 2005), Batai virus (Hubálek et al. 2005), La Crosse encephalitis (Gilliland et al. 1995), and lymphatic filariasis (Nielsen et al. 2002).
b
Counts may not sum to expected totals as studies could count toward multiple categories.
c
One study that included a global analysis of 79 nations was not reported in the section by continent (Kaur et al. 2020).
There were also differences in the time frames studied for potential effects. As described in the methods, timing of effects on disease incidence post flooding were grouped for this review as acute (within 1 wk), subacute (1 wk–1 month), medium term (1–4 months), long term (), subsequent years (), or unspecified. The largest body of evidence was reported for the medium term (24 dengue publications, 26 malaria publications, and 22 other disease publications), closely followed by the subacute term (17 dengue publications, 24 malaria publications, and 19 other disease publications); however, 14 dengue publications, 20 malaria publications, and 42 publications for other diseases reported no results that could be linked to a specific lag period after the flooding. Most studies that specified time from event reported data for only one of the timing windows used in this review (Excel Table S9). Only 22 studies reported disease frequency results for at least three of our defined post-flood lag periods: 8 dengue studies, 11 malaria studies, and 3 studies on other diseases.
Dengue
Overview.
The 45 publications that reported on dengue were predominantly from Asian countries or territories (Table 3); all nations or territories that had three publications or more were located in Asia, with the exception of Puerto Rico, which had four dengue-related publications (Figure 3). Most studies that reported the local vector species indicated the presence of Aedes aegypti () with four reporting the co-occurrence of Ae. albopictus and one reporting the co-occurrence of Ae. taeniorhynchus (Excel Table S9). Of all publications, regardless of whether a statistical analysis was performed, 16 (36%) reported no clear impact of flooding on dengue, 13 (29%) reported an increase, 5 (11%) reported a decrease, and 11 (24%) reported both decreases and increases, depending on the specific flooding metric or the window of lag time reported (Excel Table S9). A summary of the findings of studies that included statistical analyses of these associations is presented below and shown in Table 4; the summary findings of all dengue studies can be found in Excel Table S9.
| Study/reference | Country | Reported vector(s) | Flood exposure category | Lag time between flooding and disease results | ||||
|---|---|---|---|---|---|---|---|---|
| Acute | Subacute | Medium term | Long term | Unspecified time | ||||
| Major flooding events | ||||||||
| Zheng et al. 2017 | China | NR | TC-CY | — | — | — | — | Increase* |
| Carvajal et al. 2018 | Philippines | Ae.ae | FE | — | — | Increase | — | — |
| Sudaryo et al. 2020 | Indonesia | NR | FE | — | Increase* | — | — | — |
| Natuzzi et al. 2016 | Solomon Is. | NR | FE | Decrease | No impact | Decrease | Decrease | — |
| Jury 2008 | U.S. (PR) | Aedes sp. | AR, TC-HR | — | — | — | — | Decrease |
| Heavy rainfall metrics | ||||||||
| Kakarla et al. 2019 | India | Ae.ae, Ae.al | RT | Decrease | Decrease | Increase* | — | — |
| Roy et al. 2018 | India | Ae.ae, Ae.al | AR | Decrease | — | Increase | — | — |
| Pramanik et al. 2020 | India | Aedes sp. | AR | — | Increase/Decreaseb | Increase/Decreaseb | — | — |
| Liyanage et al. 2016 | Sri Lanka | Ae.ae, Ae.al | RT | Decrease | Decrease | Increase* | — | — |
| Saulnier et al. 2018a | Cambodia | NR | FL | No impact | No impact | No impact | — | — |
| Benedum et al. 2018 | Singapore | Ae.ae | FL | Decrease* | Decrease* | Increase* | — | — |
| Ramadona et al. 2016 | Indonesia | Ae.ae | RT | Decrease | Increase | Increase* | — | — |
| Arcari et al. 2007 | Indonesia | Ae.ae, Ae.al | AR | — | — | Increase* | — | — |
| Xiao et al. 2018 | China | Aedes sp. | RT | — | — | Increase/Decreaseb | — | — |
| Chen et al. 2012 | Taiwan | Ae.ae | RT | — | — | Increase*/Decrease*b | — | — |
| Chien and Yu 2014 | Taiwan | Aedes sp. | RT | — | Decrease* | Increase* | — | — |
| Yuan et al. 2019 | Taiwan | Ae.ae | AR | — | — | — | — | Decrease* |
| Méndez-Lázaro et al. 2014 | U.S. (PR) | Aedes sp. | AR | — | — | — | — | No impact |
| Lowe et al. 2018 | Barbados | Ae.ae | RT | Increase | Increase* | Increase | Decrease* | — |
| Cabrera and Taylor 2019 | Venezuela | Ae.ae | RT | — | Decrease | — | — | — |
Note: Exposure categories: —, No results reported; AR, above average rainfall; ER, extreme monsoon/rainy season; FE, flood events (specific); FL, flooded land, waterlogging, or “flushing”; RT, rainfall exceeding a specified threshold; TC, tropical cyclones (CY, cyclone; HR, hurricane; TY, typhoon); TS, tsunami.
*
Indicated by study authors as a statistically significant difference at . Other: Ae.ae, Aedes aegypti; Ae.al, Aedes albopictus; NR, not reported; PR, Puerto Rico; U.S., United States.
a
Saulnier (2018) reported malaria and dengue cases in combination as “vector-borne diseases.”
b
Mixed results may stem from the use of different study sites, differences across time within the window, or differences in results by flooding metric.
Major flooding events.
Five studies reported statistical analyses of the impacts of major flooding events on dengue, with inconsistent results (Table 4). Tropical cyclones were reported to statistically significantly () increase risk for dengue fever in one large study of 65 such storms in southeastern China between 2005 and 2011, though the time frame of the effects was not specified (Zheng et al. 2017). A study from the Philippines tracked 5 years of dengue cases in relation to major flooding events as identified through social media warnings issued by the Metropolitan Manila Development Authority when “nearly all major roads … are impassable due to flood” and found the highest positive correlation between flood weeks and dengue cases reported after 6 wk (), but the primary goal of the paper was a methodological comparison of modeling techniques for prediction of dengue cases, and it did not present details on quantification of the association between flood and dengue for each model or at other studied lag periods (Carvajal et al. 2018). A study from Indonesia found that the prevalence of dengue hemorrhagic fever was statistically significantly higher () 1 month after a major flood at 0.7% in comparison with a “preflood” prevalence of 0.2% (Sudaryo et al. 2020). However, two studies of major flooding events reported decreases in subsequent dengue cases: one that compared case counts before and after a major flood in the Solomon Islands (Natuzzi et al. 2016) and one that compared dengue counts to hurricane frequency and above average rainfall data from 26 y in Puerto Rico (Jury 2008). Both studies reported that major dengue outbreak years did not consistently occur in years with major floods or frequent hurricanes, and the Puerto Rican study additionally noted that cases peaked with average July–December rainfall and tended to decrease rapidly with increases in above-average rainfall, though neither identified these decreases as statistically significant.
Heavy rainfall metrics.
Fifteen dengue publications reported statistical analyses on the impacts of flooding as approximated by heavy rainfall metrics (e.g., rainfall exceeding a specific threshold, above average cumulative rainfall) (Table 4). These studies differed dramatically in their methods for defining heavy rainfall, for performing statistical analyses, and for reporting results. For instance, two studies from Taiwan reported on dengue responses following “torrential” rainfall in the range of in a single day (Chen et al. 2012; Chien and Yu 2014), whereas studies from India (Kakarla et al. 2019) and Sri Lanka (Liyanage et al. 2016) used rainfall thresholds in the range of per week. Others investigated cases after months of “exceptionally wet” weather based on total rainfall more than 2.5 standard deviations from average (Lowe et al. 2018), during cumulative 3-month seasons based on an index of deviation from the seasonal average (Pramanik et al. 2020), after weeks with increasing numbers of predicted “flushing” events (Benedum et al. 2018), or compared years based on the maximum 24-h rainfall within different months (Yuan et al. 2019). Such methodological variability limited our ability to quantitatively compare study results. Nonetheless, we qualitatively observed a pattern of decreases in rates of dengue in the acute () and subacute term (1 wk–1 month) after heavy rainfall but increases in the medium term (1–4 months). Only one study reported results on dengue incidence in the long term () and found a statistically significant decrease above a threshold of heavy rainfall in Barbados (Lowe et al. 2018).
Malaria
Overview.
Though sub-Saharan Africa experiences the greatest malaria burden globally, 18 of 61 total malaria studies reported on malaria risk following heavy rainfall and flooding in sub-Saharan Africa, whereas 25 studies were conducted in Asia (Table 3). Most studies (, 61%) did not indicate species or subspecies of Anopheles mosquitoes. Of all 61 publications, 18 (30%) reported no clear impact of flooding on malaria cases, 34 (56%) reported increases in malaria cases, 4 (7%) reported decreases, and 5 (8%) reported both increases and decreases depending on the specific flooding metric or the window of lag time (Excel Table S9). A summary of the findings of studies that included statistical analyses is described below and presented in Table 5; the summary findings of all malaria studies can be found in Excel Table S9.
| Citation | Country | Reported vector(s) | Flood exposure category | Lag time between flood and disease results | |||||
|---|---|---|---|---|---|---|---|---|---|
| Acute | Subacute 1 wk–1 month | Medium term | Long term | Very long | Unspecified time | ||||
| Major flooding events | |||||||||
| Kaur et al. 2020 | Global (79 nations) | Anopheles sp. | FE | — | — | — | — | — | Increase* |
| Boyce et al. 2016 | Uganda | Anopheles sp. | FE | — | Increase | Increase* | Increase* | — | Increase* |
| Elsanousi et al. 2018 | Sudan | An. arabiensis | FE | — | — | Increase* | — | — | — |
| Lindsay et al. 2000 | Tanzania | An. gambiae s.l. | FE | — | — | — | Increase / Decrease*d | — | — |
| Campanella 1999 | Nicaragua | Anopheles sp. | TC-HR | — | Increase* | — | — | — | — |
| Natuzzi et al. 2016 | Solomon Is. | NR | FE | Decrease* | Decrease* | Decrease* | Increase | — | — |
| Zheng et al. 2017 | China | NR | TC-CY | — | Decrease* | — | — | — | — |
| Ding et al. 2014a | China | An. sinensis | FE | Increase* | Increase* | No impact | — | — | — |
| Gao et al. 2016a | China | NR | FE | — | Increase* | — | — | — | — |
| Ding et al. 2019 | China | NR | FE | No impact | No impact | No impact | No impact | — | — |
| Manimunda et al. 2011 | India | An. sundaicus | TS | — | — | — | Increase* | Increase | — |
| Briët et al. 2006 | Sri Lanka | Multipleb | TS | — | — | Decrease | Decrease | — | — |
| Heavy rainfall metrics | |||||||||
| Chirebvu et al. 2016 | Botswana | Anopheles sp. | FL, AR | Increase*/Decrease*d | Increase / Decreased | Decrease* | Increase*/Decrease*d | — | — |
| Nsereko et al. 2020 | Uganda | An. gambiae s.l. | FL | — | — | Increase | — | — | Increase* |
| Himeidan et al. 2007 | Sudan | An. arabiensis | AR | — | — | — | — | — | Increase*/Decrease*d |
| Cissoko et al. 2020 | Mali | NR | FL | — | — | Increase | — | — | Increase* |
| Saulnier et al. 2018c | Cambodia | NR | FL | No impact | No impact | No impact | — | — | — |
Note: Exposure categories: —, No results reported; AR, above average rainfall; ER, Extreme monsoon/rainy season; FE, flood events (specific); FL, flooded land, waterlogging, or “flushing”; NR, not reported; RT, rainfall exceeding a specified threshold; TC, tropical cyclones (CY, cyclone; HR, hurricane; TY, typhoon); TS, tsunami.
*
Indicated by study authors as a statistically significant difference at .
a
These two studies reported different analyses for the same case data source from a major flooding event in July 2007 in Anhui Province.
b
Reported species included: Anopheles subpictus, An. culicifacies, An. vagus, and An. varuna.
c
Saulnier (2018) reported malaria and dengue cases in combination as “Vector-borne diseases”.
d
Mixed results may stem from the use of different study sites, differences across time within the window, or differences in results by flooding metric.
Major flooding events.
Of studies with relevant statistical analyses, a greater proportion of the malaria studies than dengue studies analyzed the relationship with major flooding events (as opposed to heavy rainfall metrics), with eight reporting on malaria after specific flood events/periods (Boyce et al. 2016; Ding et al. 2014, 2019; Elsanousi et al. 2018; Gao et al. 2016; Kaur et al. 2020; Lindsay et al. 2000; Natuzzi et al. 2016), two reporting after tropical cyclones (Campanella 1999; Zheng et al. 2017), and two reporting after tsunamis (Briët et al. 2006; Manimunda et al. 2011). The reported impacts on malaria were inconsistent, with a greater number of publications reporting statistically significant () increases than decreases in case numbers, especially in African studies, but no obvious temporal lag pattern in studies that reported timing of impacts as was observed for dengue. Mediating factors like disaster response efforts, landscape, water salinity, and mosquito species were suggested by the authors to play important roles in some context-specific outcomes in some of these studies, though impacts of these mediating factors were not quantified in statistical models.
A study of disease patterns following 65 cyclones in China that reported a statistically significant increase in dengue fever found a statistically significant decrease in risk for Plasmodium vivax malaria in the subacute period after cyclones (1 wk–1 month) (Zheng et al. 2017). This decrease corresponded with findings in a study from the Solomon Islands that reported a decrease in the number of malaria cases in the months following a flash flood in the capital city when compared with the previous year (Natuzzi et al. 2016) and a study that found significantly decreased prevalence of parasitemia and splenomegaly overall in a Tanzanian area 10 months after major ENSO-related flooding (Lindsay et al. 2000). Another study in Guangxi, China, investigated changes in 39 diseases in a surveillance system following 10-d periods in which a flood was determined to be present or absent by the Yearbooks of Meteorological Disasters in China and Chinese Agrimeteorological Disasters Information Database; floods were not found to have an impact on malaria incidence at any lag period, but the incidence in the area was relatively low at an annual average of 0.219 per 100,000 (Ding et al. 2019). However, five publications reported a statistically significant increase in malaria incidence in the subacute or medium term after singular flooding events: one following Hurricane Mitch in Nicaragua (Campanella 1999), two following an extreme flash flood in Anhui Province, China (Ding et al. 2014; Gao et al. 2016), and one each following major flash floods in Uganda (Boyce et al. 2016) and Sudan (Elsanousi et al. 2018). Campanella et al. noted that aggressive surveillance efforts after the hurricane may have contributed to improved case detection post flood in Nicaragua, rather than true case increases (Campanella 1999), whereas Natuzzi et al. suggested that the aggressive surveillance and vector-control efforts post flood may have helped to explain their findings of decreased incidence in the Solomon Islands (Natuzzi et al. 2016).
In addition to differences based on flood surveillance and human intervention responses, the nature of the specific flooding events and geographic context may also contribute to variability in reported findings. For instance, tsunamis are associated with high salinity flooding from ocean water (Villholth et al. 2005). Two studies following the December 2004 tsunami reported opposing long-term findings. One study from Sri Lanka reported lower incidence of malaria in 2005 than in 2004, suggesting that the tsunami did not negatively affect the downward trend in incidence that began in the year 2000 (Briët et al. 2006). The other reported a statistically significant increase in malaria incidence in the Nicobar Islands of India (Manimunda et al. 2011). These studies noted context-specific factors that could have contributed to the differences in the reported impact of the tsunami (or lack thereof) on malaria incidence, including differences in local vector species and pre-tsunami parasite endemicity. Briët et al. suggested that the lack of impact of the tsunami on malaria incidence may have been attributable to the absence of Anopheles sundaicus, a species that breeds successfully in brackish waters like those brought by the tsunami, in Sri Lanka and the fact that the affected area generally had low and unstable incidence (Briët et al. 2006). On the other hand, Manimunda et al. noted that An. sundaicus was the predominant vector in the Nicobar Islands and that malaria was highly endemic to the area prior to the arrival of the tsunami (Manimunda et al. 2011). They also hypothesized that cases increased due to widespread destruction of homes, which drove the local populations inland and into temporary housing structures, increasing their exposure to mosquito bites, and which prompted an influx of vulnerable workers from mainland areas with lower endemicity who had less immune protection from the parasite (Manimunda et al. 2011).
Heavy rainfall metrics.
Evidence regarding the effects of other heavy rainfall metrics on malaria was even more limited and inconsistent than the evidence following specific major disaster events, with only five studies presenting statistical analyses. Of these, interpretation was severely limited for one, which combined dengue and malaria cases together as “vector-borne diseases” (Saulnier et al. 2018). Evidence from two of the other studies (Chirebvu et al. 2016; Himeidan et al. 2007) reported mixed results, with statistically significant decreases and increases in malaria after heavy rainfall depending on the metrics, lag period, and analytical methods. For instance, Chirebvu et al. reported the association between clinical malaria cases diagnosed at a village health center in Botswana and flood measured as both cumulative monthly millimeters of rain and maximum extent of area flooded by annual river overflow in square kilometers (Chirebvu et al. 2016). Rain contributing to stream/river discharge was associated with a significant increase in malaria cases in the acute terms (lag of 0 months) but with a significant decrease after a lag of 3–5 months. Flood extent had nearly the opposite pattern, with a significant decrease in the short term (decreased for lags of 0–3 months) but significant increase in the long term (after lags of 5 and 6 months) (Chirebvu et al. 2016). Such complex findings from a single study highlight the challenges of comparing results across different studies and contexts. Of the remaining two studies, Cissoko et al. reported statistically significantly () higher malaria incidence for people who lived in the proximity of a river delta in Mali that tended to flood in comparison with those living in other areas (Cissoko et al. 2020). Similarly, during an outbreak investigation following a major flood in Uganda, Nsereko et al. found cases had significantly () higher odds of exposure to stagnant water around the home for 3–5 d after flooding in comparison with controls (Nsereko et al. 2020). Neither study reported their statistical results in a way that could be linked to a specific lag period, though the Uganda outbreak investigation indicated that the outbreak began approximately 40 d after the flood (Nsereko et al. 2020).
Other Diseases
In addition to malaria and dengue, we identified 17 other MBD that were investigated across 49 studies that reported on flooding: 12 reporting on RVF; 13 on WNV or Kunjin virus; 8 each on MVE and JE; 7 on St. Louis encephalitis (SLE); 6 on chikungunya; 4 each on Western equine encephalitis (WEE) and Ross River virus (RRV); 3 on Zika virus; 2 each on Barmah Forest virus (BFV), Eastern equine encephalitis (EEE), and yellow fever; and 1 each on Tahyna virus, Sindbis virus, Batai virus, La Crosse encephalitis, and lymphatic filariasis (Table 3). These reports tended to be highly geographically specific. For instance, MVE, RRV, and BFV occur only in Australia and the Pacific Islands (Reed 2018; Smith et al. 2011) and were correspondingly reported only in studies from Australia. RVF was reported only in studies from Africa and the Middle East, where it is endemic (Reed 2018; Samy et al. 2017). Finally, WWW, EEE, and SLE were reported only in flood-related studies from the United States, though they occur more broadly in the Americas and the Caribbean (Corrin et al. 2021; Kopp et al. 2013; Kumar et al. 2018).
Summarizing the results for this group of studies is challenging due to the variability in the epidemiology of each disease, the limited number of studies/cases, and the general lack of comparison group data and statistical analyses. As of 2019, the published literature indicates little to no evidence supporting an association between flood and human infections with WEE (Anders et al. 1994; Gilliland et al. 1995; Nasci and Moore 1998; Reeves et al. 1964), EEE (Gilliland et al. 1995; Nasci and Moore 1998), La Crosse virus (Gilliland et al. 1995), Tahyna virus (Hubálek et al. 2000), Sindbis virus (Hubálek et al. 2005), Batai virus (Hubálek et al. 2005), or lymphatic filariasis (Nielsen et al. 2002), though the possibility cannot be excluded due to the limitations of the evidence. Studies weakly and inconsistently indicated possible associations between floods and chikungunya (Giribabu et al. 2020; Grossi-Soyster et al. 2017; Knope et al. 2013; McCarthy et al. 1996; Roiz et al. 2015; Ruiz et al. 2018), Zika (Barrera et al. 2019; Ruiz et al. 2018; Sorensen et al. 2017), yellow fever (Chretien et al. 2008; Knope et al. 2013), and SLE viruses (Anders et al. 1994; Day and Curtis 1999; Gilliland et al. 1995; Hopkins et al. 1975; Lehman et al. 2007; Nasci and Moore 1998; Reeves et al. 1964), but the amount of evidence was limited, statistical analyses were generally absent or failed to achieve significance, and strength of evidence was therefore low. Though only one study reported the result of a statistical significance test (Anyamba et al. 2012), flooding seems to be particularly notable as a cause of human outbreaks of MVE, RRV, BFV, and RVF. All but one study reported that outbreaks of these diseases occurred following flooding or unusually heavy rainfall, though heavy rainfall did not guarantee an outbreak (Anderson 1954; Anderson et al. 1958; Anyamba et al. 2009, 2012, 2014; Broom et al. 2003; Caminade et al. 2014b; Chretien et al. 2008; Cordova et al. 2000; Doggett et al. 2001; El Mamy et al. 2011; Gudo et al. 2016; Knope et al. 2013; Linthicum et al. 1999, 2010; McCarthy et al. 1996; McDonnell et al. 1994; Nderitu et al. 2011; Selvey et al. 2014; Sow et al. 2014). Key findings of all studies are summarized in Excel Table S9.
Most studies with statistical testing of “other” MBD were reported for JE and WNV, though there were two for RRV and one each for RVF, chikungunya, and BFV. All studies on diseases other than malaria or dengue that reported statistical testing are summarized in Table 6. The three analytical studies on WNV were all from U.S. data. Two of the three studies reported statistically significant () increases in WNV cases. One was based on rapid increases in case counts following Hurricane Katrina in “hurricane-affected” counties/parishes in comparison with previous years (Caillouët et al. 2008). The second, a case-crossover study using data from 17 states, found statistically significant () increases in WNV for the first 3 wk after a week with at least 1 day of heavy rainfall (Soverow et al. 2009). The third study found that recent rainfall was not a significant predictor of WNV cases in North Dakota, but that river gage height (corresponding to overflow) was negatively associated with Culex tarsalis mosquito counts at lags of zero weeks and at 1 y, suggesting potential washout of breeding sites (Mori et al. 2018).
| Citation | Country | Reported vector(s) | Flood exposure category | Lag time between flood and disease results | ||||
|---|---|---|---|---|---|---|---|---|
| Acute | Subacute 1 wk–1 month | Medium term | Long term | Unspecified time | ||||
| Japanese encephalitis | ||||||||
| Gao et al. 2016 | China | NR | FE | — | Increase*/Decrease*a | — | — | — |
| Ding et al. 2019 | China | NR | FE | Increase* | — | — | — | — |
| Zhang et al. 2016 | China | Cx. tritaeniorhynchus | RT | — | Increase* | — | — | — |
| Chen et al. 2012 | Taiwan | Ae. aegypti | RT | — | Increase*/Decrease*a | — | — | — |
| Rattanavong et al. 2020 | Lao PDR | NR | FL | — | Increase* | No impact | No impact | Increase |
| Singh et al. 2020 | India | NR | RT | — | — | — | — | Decrease |
| West Nile virus | ||||||||
| Caillouët et al. 2008 | U.S. (LA, MS) | NR | TC-HR | — | Increase | — | Increase* | — |
| Soverow et al. 2009 | U.S. | NR | RT | Increase* | Increase*/no impact | — | — | — |
| Mori et al. 2018 | U.S. (ND) | Cx. tarsalis | AR, FL | — | — | — | — | No impact |
| Barmah Forest virus | ||||||||
| Knope et al. 2013 | Australia, Pacific Islands | NR | FE, TC-CY | — | — | — | — | Increase |
| Rift Valley fever | ||||||||
| Anyamba et al. 2012 | East Africa, Sudan, S. Africa, Madagascar | NR | AR | — | — | Increase* | — | — |
| Ross River virus | ||||||||
| Knope et al. 2013 | Australia | Multiple | FE, TC-CY | — | — | — | — | Increase |
| Tall and Gatton 2020 | Australia | NR | FL | — | — | Increase* | — | — |
| Chikungunya | ||||||||
| Grossi-Soyster et al. 2017 | Kenya | Ae. aegypti | NS | — | — | — | — | Increase* |
Note: Studies were found for other mosquito-borne diseases, but none that reported a statistical analysis of the flood and mosquito-borne disease relationship for inclusion in this table: Murray Valley encephalitis, St. Louis encephalitis, Western equine encephalitis, Eastern equine encephalitis, lymphatic filariasis, Tahyna bunyavirus, Sindbis virus, Batai virus, Zika virus, La Crosse virus, or yellow fever. Details on these studies can be found in Excel Table S9. Exposure categories: —, No results reported; AR, above average rainfall; ER, extreme monsoon/rainy season; FE, flood events (specific); FL, flooded land, waterlogging, or “flushing”; RT, rainfall exceeding a specified threshold; TC, tropical cyclones (CY, cyclone; HR, hurricane; TY, typhoon); TS, tsunami. Other: LA, Louisiana; MS, Mississippi; ND, North Dakota; NR, not reported; U.S., United States.
*
Indicated by study authors as a statistically significant difference at .
a
Mixed results may stem from the use of different study sites, differences across time within the window, or differences in results by flooding metric.
Most of the six studies of JE presented results for the subacute term (1 wk – 1 month after flooding). In their case-crossover analysis comparing dates after a major flood in Anhui Province, China, to the same dates in other years, Gao et al. reported a maximum odds ratio (OR) of 4.50 [95% confidence interval (CI): 1.40, 7.60] at an 11-d lag after the flooding began but reported an overall statistically significant decrease in JE associated with the flood in the analysis of the first month post flooding (, 95% CI: 0.47, 0.71) (Gao et al. 2016). Zhang et al. reported a statistically significant increase in JE from a case-crossover analysis using nine specific flood events in Sichuan, China, but found that the strongest effect was at a lag of 23–24 d (, 95% CI: 1.14, 3.52) (Zhang et al. 2016). A third case-crossover analysis studying JE cases after floods in Guangxi, China, reported that the strongest relationship overlapped with the 10-d blocks identified dichotomously as having a flood or not (classified as the acute period) and reported a statistically significant increase in case incidence (multivariate adjusted , 95% CI: 1.119, 4.865) (Ding et al. 2019). The reason for the discrepancies is not clear, given the methodological similarities of these three studies. A study in Taiwan reported that there was a statistically significant positive association with torrential amounts of rainfall () and incidence of JE after 14 d (), but they found no cases occurring after extreme torrential rain () (Chen et al. 2012). This finding led the authors to hypothesize a positive association between rainfall and JE but with a threshold above which rainfall washes out breeding sites and decreases risk, echoing the hypothesis from the Mori et al. study of WNV (Mori et al. 2018). A recent study from Lao PDR performed two separate analyses of JE and flooding, both using satellite images to calculate a normalized flooding index (NFI) for villages and for , , and buffers around individual residences (Rattanavong et al. 2020). Their ecological analysis comparing the incidence rates of hospitalizations for JE from villages by quartile of NFI for the 8-y study period found a slight elevation in the OR for the two highest quartiles, but the differences were not statistically significant (OR for fourth quartile vs. first , 95% CI: 0.54, 2.95) (Rattanavong et al. 2020). They also conducted a retrospective epidemiological analysis comparing the NFI quartile for buffers around the residences of hospitalized JE cases to control patients hospitalized for central nervous system disease of a different etiology, which we identified as an example of a hospital-based case–control analysis. They reported statistically significantly increasing ORs by quartile of NFI for JE during the month of case admission at a buffer of (OR for fourth quartile vs. first , 95% CI: 1.04, 8.96) (Rattanavong et al. 2020). Finally, a study from India reported that an overall positive association between rainfall and JE hospitalizations and deaths appeared to reverse, becoming negatively associated with heavy rainfall greater than in a day (Singh et al. 2020).
Discussion
The results of our scoping review provide some evidence that flooding and extreme rainfall can increase MBD, but the dearth of high-quality evidence undermines the strength of the conclusions. Event-to-transmission timing differed among disease systems. Incidence of malaria generally increased in all reported time frames (acute, mid, and long term), whereas dengue incidence declined during the acute phase but increased approximately 1 month after the extreme weather event. The overall low epidemiological rigor of included studies indicates that extreme caution is warranted in interpreting these relationships. Our work underscores the complex impacts climate change may have on MBD through the pathway of extreme flooding events, though the lack of methodological consistency precluded quantitative analysis and exploration of potential mediating factors, such as other local climate and weather conditions, socioeconomic characteristics of the human population, extent of flood damages, vector species, and other entomological factors. We recommend that future infectious disease risk projections consider the impacts of changing climate variability, including increasing frequency of flooding and more severe tsunamis triggered by rising sea levels, in addition to projected changes in average temperature and rainfall that are already commonly examined. More rigorous studies will need to be conducted to identify parameters for inclusion in transmission predictions under climate change scenarios.
As more research in this area is conducted, we believe it is critical to address three fundamental limitations to the current body of literature on this topic. First, there must be investment in surveillance and research capacity where the impact is likely to be the greatest. Research is underrepresented in the low-income, low-resource regions where MBD pose the greatest threats. For instance, although sub-Saharan Africa accounts for 94% of malaria burden and deaths (WHO 2020), only 40% of malaria articles included in this review were focused on countries in sub-Saharan Africa. Further, the literature on dengue had very little representation from Latin American countries, which suffer a significant dengue burden (Zeng et al. 2021), and none from sub-Saharan Africa, where reporting and detection have been limited (Buchwald et al. 2020) but where models predict an increasing burden as the climate changes (Messina et al. 2019). Second, this review highlights the need for standards to ensure systematic reporting of relevant and accurate epidemiological data in papers to facilitate integration and comparisons among regions. The goal of many of these studies was often a locally specific predictive model, so even those that did perform statistical analyses were not undertaken or presented in a way that supported generalizable causal inferences. Finally, disease outcome end points, such as case numbers, should be presented in the context of surveillance systems or consistent data collection processes. Outbreaks identified by the included studies may be biased by the reactionary nature of much of the included research—namely, that outbreaks were identified because surveillance efforts increased following a natural disaster. Relatively few studies reporting on flood events were based on analyses comparing data collected consistently during nonflood conditions. Studies that did use longer-term data to assess trends between rainfall and disease incidence, especially for dengue, often reported the association with rainfall only as a linear predictor, making it impossible to delineate the impact of discrete flooding events. Strengthening public health surveillance systems to consistently monitor disease incidence before and after acute events is needed to better quantify flood impacts.
Dengue
Although it was one of the most widely studied diseases of interest, the identified relationship between flooding and dengue incidence was inconclusive. Overall, we observed a trend toward decreased incidence in the first month following the extreme rainfall/flooding event, followed by a statistically significant increase in cases between 1 and 4 months after the event. These trends are biologically plausible. The primary vector reported in the studies with species information available was Ae. aegypti, which is known to be the primary vector globally (Foster and Walker 2019). As container breeders, Ae. aegypti exploits artificial habitats that may overflow in acute rainfall events, effectively washing out maturing larvae and pupae and reducing transmission in the immediate aftermath (Benedum et al. 2018; Seidahmed and Eltahir 2016), though laboratory experiments have not confirmed the hypothesis (Koenraadt and Harrington 2008). However, increased debris and more numerous water-holding containers following the acute stage after a flood could lead to later increases in breeding sites and vector abundance. Even where study designs and epidemiological rigor were strong, a consistent issue was lack of reported surveillance data or accounting for vector species and the circulating dengue serotype(s). It is unclear whether there could have been increases related to the introduction of a new serotype that was unrelated to the weather event. Natuzzi et al. suggested that previous outbreaks of a particular dengue serotype may preclude new flood-related outbreaks due to high levels of population immunity (Natuzzi et al. 2016). The serotypes of cases were not reported in most of the identified literature. Reporting of serotypes should become standardized to help disentangle these precursors to increasing transmission. Many studies did not specify the vector subspecies and/or presumed the major vector was Ae. aegypti, though distribution of other Aedes vectors has increased dramatically in the past few decades (Benedict et al. 2007). Dengue surveillance is also a prime example of the critical need to consider multiple time points following an extreme rainfall/flooding event to study the effect on both mosquitoes and humans. Study designs do not necessarily allow enough time to determine virus circulation in the mosquito (extrinsic incubation period) or in humans (intrinsic incubation period) (e.g., Zheng et al. 2017). Further, some studies we identified relied on the same sources of data and therefore were not independent contributors to the overall strength of evidence. Finally, few studies examined linkages between extreme weather events and other viruses vectored by Aedes, including Zika and chikungunya, so it was difficult to determine whether the biological pathway of fluctuating immature habitat availability may influence dengue incidence.
Malaria
First, in studies from malaria endemic regions (i.e., areas where malaria transmission is typically stable or seasonal), there was more evidence for outbreaks after a flooding or rainfall related disaster in comparison with areas with low and unstable transmission prior to flooding. Second, water salinity (i.e., salt water vs. fresh water), modified by events like tsunamis or storm surges that bring salt water inland, might result in no change or decreased malaria incidence post event than heavy rainfall, which leads to freshwater flooding events. It is possibly for this reason that results were heterogenous following cyclones, tsunamis, and hurricanes, whereas heavy rainfall and general flooding events tended to be associated with higher malaria risk.
Salinity of water is a possible moderator, especially where seawater is brought farther inland by storm surge associated with extreme weather events such as hurricanes and cyclones. This effect could be mediated by the species of malaria vector in the area. For example, An. sundaicus is known as a brackish-water mosquito and reported to breed in a wide range of habitats, including saline water and water with organic pollution (Sinka et al. 2011; Syafruddin et al. 2020), whereas water salinity limits the breeding success for other vector species (Kengne et al. 2019). However, most of the studies we included did not identify which vector species were responsible for transmission in the study area.
The aftermath of the flood event may be more important for mosquito abundance than the event itself because initial flooding might flush out vector breeding grounds. However, if floodwaters do not recede quickly, the provision of more breeding areas may be a modifying factor in the occurrence of increased malaria in addition to population displacement and widespread malnutrition following natural disasters. Overcrowding conditions and malnutrition both increase malaria risk (Alton and Rattanavong 2004; Bannister-Tyrrell et al. 2017; Das et al. 2018), and the combination of increased mosquito breeding with these demographic and social changes could increase likelihood of outbreaks.
Other Mosquito-Borne Diseases
We found relatively limited evidence pertaining to the relationship of flooding with MBD other than dengue and malaria. Studies that performed statistical hypothesis testing on the relationship were found mainly for JE and WNV, from which no clear patterns emerged, though results tended to be suggestive of increases in incidence after flooding. Most studies on flood and other MBD reported descriptively on human disease outbreaks without surveillance data for statistical testing in comparison with unaffected areas or to time periods without floods. Furthermore, outbreak reports have been inconsistent in providing details to quantify flood extent or define the timeline of impacts on human disease incidence.
Despite a lack of statistical testing, flooding has been commonly linked to several human outbreaks of the zoonotic diseases MVE, RRV, BFV, and RVF. As the key mosquito vectors that transmit these viruses are floodwater mosquitoes, laying their eggs adjacent to water on wet soil to later emerge during a flooded state (Webb 2013), it is not surprising that they are noted to be associated with zoonotic outbreaks. However, flooding events are not universally associated with human outbreaks of these diseases, and other factors may be critical to regulating whether zoonotic outbreaks spill over into human populations. For example, RVF research indicates that seroprevalence and severe disease may be associated with occupational exposures to blood of infected animals, instead of direct transmission from mosquitoes (Anyangu et al. 2010; Nyakarahuka et al. 2018). For RRV, population dynamics among kangaroo hosts may significantly influence human outbreak potential (Carver et al. 2010). Although more research on mediating factors is needed, flooding in areas endemic for RRV, MVE, RFV, and BFV should prompt increased surveillance or prevention efforts to detect and interrupt epidemics in both humans and animal reservoirs.
Data Quality and Generalizability: Recommendations
We identified relatively few studies that could be considered truly comparable for a quantitative synthesis or meta-analysis due to wide variability in methodology and reporting. This finding indicates a wider problem with only collecting post-event data (vs. constant surveillance), especially for regions at highest risk for a flooding disaster event. Even papers that did report statistical hypothesis testing lacked consistency in the modeling methods, methods of defining and reporting weather and climate variables (particularly for extreme events like flooding), and methods of reporting the results for disease–flood associations. This methodological variability challenged our attempts to systematically summarize and interpret results, even for studies that investigated the same events or geographic locations. A primary example of this phenomenon was that many studies compared rainfall to weekly dengue incidence, and yet no two were alike in operationalization of the key variables or in their statistical approaches; for instance, some measured the extent of flooding in square kilometers of land covered by water from satellite images, others as rainfall exceeding varying thresholds in varying time periods, and others as rainfall significantly deviating from average, etc. Another review of climate-driven malaria epidemics also found imprecise incidence measurements and highly varying statistical analysis techniques in the literature, complicating the potential for valid generalizations (Mabaso and Ndlovu 2012). These methodological differences may exist because the goal of most of the studies was not to achieve broader causal inferences about the relationships but rather to build locally useful predictive models. However, such an approach limits the generalizability of these studies to support predictions of flood impacts in other settings, which is especially problematic for low-resource regions where populations are most vulnerable to climate change and infectious disease but little local research has been conducted to date.
Based on gaps identified in this scoping review, we have several recommendations to further investigate causal associations between weather-related flooding and other natural disaster events on MBD risk:
•
Design: Studies should use a design that involves a comparison group, enabling statistical testing rather than just descriptive outbreak reports. Example designs can include a) ecological studies that compare disease incidences in flood-affected areas to similar flood-unaffected areas or from preflood to postflood dates; b) ecological case-crossover designs that compare the frequency or intensity of flood exposures preceding major disease outbreaks to the frequency/intensity of flood exposures in previous weeks, months, or the same weeks/months in previous years; c) individual-level cross-sectional, case–control, or case-crossover studies that compare exposure to floods in those with disease to those without disease (case–control) or to their own exposure to flood at other time points (case-crossover); or d) individual-level cohort studies that leverage either ongoing research studies or demographic surveillance systems data to compare disease risks in flooded vs. nonflood time points or between similar geographic areas affected vs. unaffected by flooding.
•
Case detection: Studies should rely on a well-defined, consistent case detection and enrollment procedure, preferably using an existing active or passive disease surveillance system. Any necessary changes in methodology for confirming cases should be detailed clearly, including laboratory methods performed, to adjust for variation in sensitivity and specificity of the test and the system.
•
Investment in surveillance and research capacity: Further investment is critically needed to build local research capacity and surveillance systems in the geographic areas currently most at risk for transmission of MBD. Health inequities are stark and will increase under the current climate change trajectory (Ebi et al. 2018; Ebi and Hess 2020; Smith et al. 2014). Knowledge base and capacity must be built to mitigate these disparities.
•
Flooding definitions: It is not possible to proscribe a standardized definition for flooding. The World Meteorological Organization encourages the use of locally specific guidelines as to what constitutes a flood, because this depends on terrain, drainage, typical weather, and other local conditions (World Meteorological Organization 2021). For studies reporting on the occurrence of distinct flooding events, these locally relevant definitions should be clearly reported, preferably with reference to typical weather patterns in the area. Furthermore, most studies rely on secondary analysis of meteorological variables collected by other sources, limiting available options. Flood severity metrics should either be described for a singular event or included in analyses comparing multiple flood events. These can include the duration of persistent flood waters, measures of population affected, or extent of land area affected. When the only available secondary measure of flooding is cumulative daily or monthly rainfall amounts, studies should consider using splines or a categorical analysis to fit potential nonlinear effects in the case of heavy rainfall that leads to flooding that may flush out typical breeding sites. With increasing availability of remotely sensed satellite data, such results could be enhanced by the quantification of land areas covered by water during flood periods where water is not normally present, such as calculating a normalized differential water index (NDWI) (Sivanpillai et al. 2021).
•
Vectors: Studies should comment on the known endemic vector species, even when vectors are not being captured directly and, when possible, incorporate vector monitoring or entomological indices into study.
•
Lag periods: Studies should quantify effects at various lag periods following flooding events, to include acute (within 1 wk), sub-acute (1 wk–1 month), medium term (1–4 months), long term (), and subsequent years, or more detailed time points if possible. In particular, studies that examine long-term effects due to lags in recovery or changes in vector ecology and human susceptibility are currently lacking in the literature.
•
Bias assessments: Future analyses should comment on or adjust for potential biases that may be introduced by major population or systems changes subsequent to the occurrence of major disasters. For instance, human migration patterns following flood may have significant impacts on population denominators for disease incidence calculations; disaster events may reduce the accuracy of case detection and reporting; or, conversely, emergency relief systems may increase screening and identification of infected individuals, particularly if the flooding leads to informal housing settlements for displaced individuals.
•
Collaboration/generalizability: We recommend attempts to systematically collect and analyze data across multiple sites and diseases using standard methods. We also recommend that studies attempt to quantify socioenvironmental and human behavioral factors that may confound or modify relationships between flooding and disease risk—an influence that is assumed but not quantified in the literature (Banu et al. 2011). Important potential mediating variables may include socioeconomic status of the population, especially things like population density and housing structure quality, which regulate the extent of human–mosquito contact; proximity to zoonotic host species where relevant; baseline endemicity of different MBDs; extent of structural damage and population displacement; extent of vector control efforts; and other climate and weather factors like season, typical rainfall patterns, temperature, and humidity. Investigating these potential mediating factors across multiple sites will improve our ability to generalize such results to areas with limited local data availability and refine our strategies for disaster response.
•
Data integration: There is an urgent need to have routine integration of disparate data systems to facilitate investigations into extreme weather events and infectious disease (and other public health) outcomes. Although this review focuses on primarily precipitation-driven events and MBD outcomes, it also highlights current limitations more broadly in integration of data related to biodiversity of vectors and zoonotic health species, weather and climate data, social systems and behaviors, public health and medical infrastructure and support, and disease monitoring and surveillance (both human and animal). Questions regarding the role of extreme heat events on disease systems and influence of shocks to food systems on pathogen transmission and human susceptibility, etc., will all require an integrative One Health approach to data that harmonizes spatial and temporal scales.
Strengths and Limitations
Although there have been numerous partial reviews of this topic, to our knowledge this is the first systematic attempt to review the quantitative and qualitative data available on flooding and MBD incidence. Key strengths of this review include the comprehensive search effort and breadth of languages considered (), although exclusion of other languages may have led to missed information. Our search strategy was broadly inclusive and had good specificity (21.9% of initial results for title/abstracts went to full text review). However, the literature we identified had limited interpretability pertaining to our research question due to inconsistencies in study design, analysis, and reporting, especially regarding temporality of measured disease end points, which indicates a strong level of detection bias. Individual publications that presented results as outbreak case studies in the absence of any comparison group, metrics on the event, or statistical testing made little if any contribution to the overall strength of evidence presented in this review.
Additionally, the effects of human activity/response and other mediating variables are likely important but were not addressed consistently and comparably in the identified literature, e.g., vector control measures, rescue workers introducing a disease to a previously isolated population, human migration, and immunity. Finally, we did not include studies of ENSO impacts unless flooding was specifically indicated; therefore, ENSO-related variability may merit additional review.
The inclusion of tsunami events may not seem directly relevant to climate-related natural disasters. However, with climate change–induced sea level rise and the decline of protective reef ecosystems, there are indirect associations between climate change and increased tsunami risk (Shao et al. 2019). Given that 40% of the world’s population lives within of coastal areas (Office for Coastal Management, National Oceanic and Atmospheric Administration 2020), vector-borne disease dynamics following inundation with brackish water merit further exploration.
Public Health Implications
Our scoping review highlights a general need for improved surveillance systems in the most vulnerable global areas. Strong foundational knowledge of which types of flooding events lead to increases for particular diseases under which temporal and geographic contexts will be helpful in disaster planning, surveillance, and management. For example, prioritizing measuring malaria risk and implementing prevention and control strategies following flooding events after heavy rainfall may be more important than after tsunamis, cyclones, and hurricanes. There may be a brief respite in dengue case incidence immediately following flooding, though vector-control measures should be intensified in the first week or two following floods to prevent a subsequent spike in cases. However, gaps in the quality of the evidence found in this review highlight key domains where surveillance and research can focus to obtain more consistent pre-event numbers for reliable comparisons. First, population denominators should consider predisaster measurements for comparison after the event, confirming the importance of disease surveillance systems. Second, clearer reporting of specific timing of disease or outcome incidence post flooding will help clarify the complex and dynamic trends in risk at different latency periods in the aftermath of these events. Third, attempts should be made to share data and pursue methodological consistency to improve generalizability of study findings about flood and MBD associations. As the effects of climate change amplify, it is likely that competency differences between vector species will become more apparent, meaning that species and subspecies identification is critical in future work.
Conclusions
An overall positive relationship between flooding and incidence of malaria, dengue, and other MBD likely exists, but the current literature on the topic is insufficient to make interpretations about the strength and precise timing of these relationships. This review emphasizes the need for rigor and consistency in research methods for improved natural disaster planning and preparation regarding MBD and other disease systems susceptible to increasingly common extreme weather events.
Acknowledgments
This research was supported by National Institutes of Health–National Institute of General Medical Sciences grant no. K12 GM000708. This study was presented, in part, at the 2019 meeting of the American Society of Tropical Medicine and Hygiene.
K.C.E. and J.E.C. were responsible for proposing the hypothesis, procedure, and aims. J.E.C., E.A., E.M.S., V.M.G., J.R., B.D., D.R., A.C.L., and K.C.E. screened studies and performed data abstraction. J.E.C., E.A., E.M.S., V.M.G., J.R., and D.R. created summary tables and figures. J.E.C., E.A., E.M.S., M.H.H., and K.C.E. prepared and reviewed the manuscript.
Article Notes
The authors declare they have no actual or potential competing financial interests.
Supplementary Material
References
Afzal MF, Sultan MA. 2013. Experience of malaria in children of a flood affected area: a field hospital study. East Mediterr Health J 19(7):613–616. https://pubmed.ncbi.nlm.nih.gov/24975305/, https://doi.org/10.26719/2013.19.7.613.
Ahmed Z, Khan AA, Nisar N. 2011. Frequency of infectious diseases among flood affected people at district Rajanpur, Pakistan. Pak J Med Sci 27(4):866–869.
Akpan GE, Adepoju KA, Oladosu OR. 2019. Potential distribution of dominant malaria vector species in tropical region under climate change scenarios. PLoS One 14(6):e0218523. https://pubmed.ncbi.nlm.nih.gov/31216349/, https://doi.org/10.1371/journal.pone.0218523.
Alton C, Rattanavong H. 2004. Service delivery and resettlement: options for development planning. In: Final Report: Livelihoods Study. http://csdp.org/research/0014_final_undplao.pdf [accessed 17 December 2021].
Anders J, Shireley LA, Volmer L, Forsch K, Osterholm MT, Schell WL, et al. 1994. Rapid assessment of vectorborne diseases during the midwest flood – United States, 1993. MMWR Morb Mortal Wkly Rep 43(26):481–483. https://pubmed.ncbi.nlm.nih.gov/7911970/.
Anderson SG. 1954. Murray Valley encephalitis and Australian X disease. J Hyg (Lond) 52(4):447–468. https://pubmed.ncbi.nlm.nih.gov/13221810/.
Anderson SG, Dobrotworsky NV, Stevenson WJ. 1958. Murray Valley encephalitis in the Murray Valley, 1956 and 1957. Med J Aust 45(1):15–17. https://pubmed.ncbi.nlm.nih.gov/13565011/, https://doi.org/10.5694/j.1326-5377.1958.tb60546.x.
Ansari MAR, Nasir AS. 1958. Factors responsible for an unusual malaria epidemic in a normally health village - Hadali (district Shahpur at Sargodha) inundated after torrential local rains and floods in a hill torrent. Pak J Health 7(4):155–157. https://pubmed.ncbi.nlm.nih.gov/13566845/.
Anyamba A, Chretien J-P, Small J, Tucker CJ, Formenty PB, Richardson JH, et al. 2009. Prediction of a Rift Valley fever outbreak. Proc Natl Acad Sci USA 106(3):955–959. https://pubmed.ncbi.nlm.nih.gov/19144928/, https://doi.org/10.1073/pnas.0806490106.
Anyamba A, Linthicum KJ, Small JL, Collins KM, Tucker CJ, Pak EW, et al. 2012. Climate teleconnections and recent patterns of human and animal disease outbreaks. PLoS Negl Trop Dis 6(1):e1465. https://pubmed.ncbi.nlm.nih.gov/22292093/, https://doi.org/10.1371/journal.pntd.0001465.
Anyamba A, Small JL, Britch SC, Tucker CJ, Pak EW, Reynolds CA, et al. 2014. Recent weather extremes and impacts on agricultural production and vector-borne disease outbreak patterns. PLoS One 9(3):e92538. https://pubmed.ncbi.nlm.nih.gov/24658301/, https://doi.org/10.1371/journal.pone.0092538.
Anyangu AS, Gould LH, Sharif SK, Nguku PM, Omolo JO, Mutonga D, et al. 2010. Risk factors for severe Rift Valley fever infection in Kenya, 2007. Am J Trop Med Hyg 83(suppl 2):14–21. https://pubmed.ncbi.nlm.nih.gov/20682901/, https://doi.org/10.4269/ajtmh.2010.09-0293.
Arcari P, Tapper N, Pfueller S. 2007. Regional variability in relationships between climate and dengue/DHF in Indonesia. Singapore J Trop Geo 28(3):251–272, https://doi.org/10.1111/j.1467-9493.2007.00300.x.
Asari Y, Koido Y, Nakamura K, Yamamoto Y, Ohta M. 2000. Analysis of medical needs on day 7 after the tsunami disaster in Papua New Guinea. Prehosp Disaster Med 15(2):9–13. https://pubmed.ncbi.nlm.nih.gov/11183459/.
Aumentado C, Cerro BR, Olobia L, Suy LL, Reyes A, Kusumawathie PHD, et al. 2015. The prevention and control of dengue after Typhoon Haiyan. Western Pac Surveill Response J 6(suppl 1):60–65. https://pubmed.ncbi.nlm.nih.gov/26767138/, https://doi.org/10.5365/wpsar.2015.6.3.HYN_018.
Balaraman K, Sabesan S, Jambulingam P, Gunasekaran K, Boopathi Doss PS. 2005. Risk of outbreak of vector-borne diseases in the tsunami hit areas of southern India. Lancet Infect Dis 5(3):128–129. https://pubmed.ncbi.nlm.nih.gov/15766641/.
Bannister-Tyrrell M, Verdonck K, Hausmann-Muela S, Gryseels C, Muela Ribera J, Peeters Grietens K. 2017. Defining micro-epidemiology for malaria elimination: systematic review and meta-analysis. Malar J 16(1):164. https://pubmed.ncbi.nlm.nih.gov/28427389/, https://doi.org/10.1186/s12936-017-1792-1.
Banu S, Hu W, Hurst C, Tong S. 2011. Dengue transmission in the Asia-Pacific region: impact of climate change and socio-environmental factors. Trop Med Int Health 16(5):598–607. https://pubmed.ncbi.nlm.nih.gov/21320241/, https://doi.org/10.1111/j.1365-3156.2011.02734.x.
Barclay AJG, Coulter JBS. 1988. Floods and resistant malaria. Lancet 2(8624):1367. https://pubmed.ncbi.nlm.nih.gov/2904083/.
Barrera R, Felix G, Acevedo V, Amador M, Rodriguez D, Rivera L, et al. 2019. Impacts of Hurricanes Irma and Maria on Aedes aegypti populations, aquatic habitats, and mosquito infections with dengue, chikungunya, and Zika viruses in Puerto Rico. Am J Trop Med Hyg 100(6):1413–1420. https://pubmed.ncbi.nlm.nih.gov/30963992/, https://doi.org/10.4269/ajtmh.19-0015.
Beatty ME, Hunsperger E, Long E, Schürch J, Jain S, Colindres R, et al. 2007. Mosquitoborne infections after Hurricane Jeanne, Haiti, 2004. Emerg Infect Dis 13(2):308–310. https://pubmed.ncbi.nlm.nih.gov/17479899/, https://doi.org/10.3201/eid1302.061134.
Benedict MQ, Levine RS, Hawley WA, Lounibos LP. 2007. Spread of the tiger: global risk of invasion by the mosquito Aedes albopictus. Vector Borne Zoonotic Dis 7(1):76–85. https://pubmed.ncbi.nlm.nih.gov/17417960/.
Benedum CM, Seidahmed OME, Eltahir EAB, Markuzon N. 2018. Statistical modeling of the effect of rainfall flushing on dengue transmission in Singapore. PLoS Negl Trop Dis 12(12):e0006935. https://pubmed.ncbi.nlm.nih.gov/30521523/, https://doi.org/10.1371/journal.pntd.0006935.
Bich TH, Quang LN, Ha LTT, Hanh TTD, Guha-Sapir D. 2011. Impacts of flood on health: epidemiologic evidence from Hanoi, Vietnam. Glob Health Action 4:6356. https://pubmed.ncbi.nlm.nih.gov/21866222/, https://doi.org/10.3402/gha.v4i0.6356.
Bissell RA. 1983. Delayed-impact infectious disease after a natural disaster. J Emerg Med 1(1):59–66. https://pubmed.ncbi.nlm.nih.gov/6436364/, https://doi.org/10.1016/0736-4679(83)90010-0.
Bouma MJ, Dye C. 1997. Cycles of malaria associated with El Niño in Venezuela. JAMA 278(21):1772–1774. https://pubmed.ncbi.nlm.nih.gov/9388155/, https://doi.org/10.1001/jama.1997.03550210070041.
Boyce R, Reyes R, Matte M, Ntaro M, Mulogo E, Metlay JP, et al. 2016. Severe flooding and malaria transmission in the western Ugandan highlands: implications for disease control in an era of global climate change. J Infect Dis 214(9):1403–1410. https://pubmed.ncbi.nlm.nih.gov/27534686/, https://doi.org/10.1093/infdis/jiw363.
Briët OJT, Galappaththy GNL, Amerasinghe PH, Konradsen F. 2006. Malaria in Sri Lanka: one year post-tsunami. Malar J 4:42. https://pubmed.ncbi.nlm.nih.gov/16164748/, https://doi.org/10.1186/1475-2875-5-42.
Briët OJT, Galappaththy GNL, Konradsen F, Amerasinghe PH, Amerasinghe FP. 2005. Maps of the Sri Lanka malaria situation preceding the tsunami and key aspects to be considered in the emergency phase and beyond. Malar J 4:8. https://pubmed.ncbi.nlm.nih.gov/15676073/, https://doi.org/10.1186/1475-2875-4-8.
Broom AK, Lindsay MDA, Wright AE, Smith DW, Mackenzie JS. 2003. Epizootic activity of Murray Valley encephalitis and Kunjin viruses in an aboriginal community in the southeast Kimberley region of Western Australia: results of mosquito fauna and virus isolation studies. Am J Trop Med Hyg 69(3):277–283. https://pubmed.ncbi.nlm.nih.gov/14628944/, https://doi.org/10.4269/ajtmh.2003.69.277.
Brown V, Issak MA, Rossi M, Barboza P, Paugam A. 1998. Epidemic of malaria in north-eastern Kenya. Lancet 352(9137):1356–1357. https://pubmed.ncbi.nlm.nih.gov/9802279/, https://doi.org/10.1016/S0140-6736(05)60747-7.
Buchwald AG, Hayden MH, Dadzie SK, Paull SH, Carlton EJ. 2020. Aedes-borne disease outbreaks in west Africa: a call for enhanced surveillance. Acta Trop 209:105468. https://pubmed.ncbi.nlm.nih.gov/32416077/, https://doi.org/10.1016/j.actatropica.2020.105468.
Cabrera M, Taylor G. 2019. Modelling spatio-temporal data of dengue fever using generalized additive mixed models. Spat Spatiotemporal Epidemiol 28(2019):1–13. https://pubmed.ncbi.nlm.nih.gov/30739650/, https://doi.org/10.1016/j.sste.2018.11.006.
Caillouët KA, Michaels SR, Xiong X, Foppa I, Wesson DM. 2008. Increase in West Nile neuroinvasive disease after Hurricane Katrina. Emerg Infect Dis 14(5):804–807. https://pubmed.ncbi.nlm.nih.gov/18439367/, https://doi.org/10.3201/eid1405.071066.
Caminade C, Kovats S, Rocklov J, Tompkins AM, Morse AP, Colón-González FJ, et al. 2014a. Impact of climate change on global malaria distribution. Proc Natl Acad Sci USA 111(9):3286–3291. https://pubmed.ncbi.nlm.nih.gov/24596427/, https://doi.org/10.1073/pnas.1302089111.
Caminade C, Ndione JA, Diallo M, MacLeod DA, Faye O, Ba Y, et al. 2014b. Rift Valley fever outbreaks in Mauritania and related environmental conditions. Int J Environ Res Public Health 11(1):903–918. https://pubmed.ncbi.nlm.nih.gov/24413703/, https://doi.org/10.3390/ijerph110100903.
Campanella N. 1999. Infectious diseases and natural disasters: the effects of hurricane mitch over Villanueva municipal area, Nicaragua. Public Health Rev 27(4):311–319. https://pubmed.ncbi.nlm.nih.gov/11081356/.
Carvajal TM, Viacrusis KM, Hernandez LFT, Ho HT, Amalin DM, Watanabe K. 2018. Machine learning methods reveal the temporal pattern of dengue incidence using meteorological factors in metropolitan Manila, Philippines. BMC Infect Dis 18(1):183. https://pubmed.ncbi.nlm.nih.gov/29665781/, https://doi.org/10.1186/s12879-018-3066-0.
Carver S, Kilpatrick AM, Kuenzi A, Douglass R, Ostfeld RS, Weinstein P. 2010. Environmental monitoring to enhance comprehension and control of infectious diseases. J Environ Monit 12(11):2048–2055. https://pubmed.ncbi.nlm.nih.gov/20957286/, https://doi.org/10.1039/c0em00046a.
CDC (U.S. Centers for Disease Control and Prevention). 1989. Health assessment of the population affected by flood conditions–Khartoum, Sudan. MMWR Morb Mortal Wkly Rep 37(51–52):785–788. https://pubmed.ncbi.nlm.nih.gov/2491763/.
Cedeno JEM. 1986. Rainfall and flooding in the Guayas river basin and its effects on the incidence of malaria 1982–1985. Disasters 10(2):107–111, https://doi.org/10.1111/j.1467-7717.1986.tb00575.x.
Chakravarti A, Kumaria R. 2005. Eco-epidemiological analysis of dengue infection during an outbreak of dengue fever, India. Virol J 2(1):32. https://pubmed.ncbi.nlm.nih.gov/15831102/, https://doi.org/10.1186/1743-422X-2-32.
Chan CW, Iata H, Yaviong J, Kalkoa M, Yamar S, Taleo G, et al. 2017. Surveillance for malaria outbreak on malaria-eliminating islands in Tafea Province, Vanuatu after Tropical Cyclone Pam in 2015. Epidemiol Infect 145(1):41–45. https://pubmed.ncbi.nlm.nih.gov/27609054/, https://doi.org/10.1017/S0950268816002041.
Chancey C, Grinev A, Volkova E, Rios M. 2015. The global ecology and epidemiology of West Nile virus. BioMed Research International 2015:376230, https://doi.org/10.1155/2015/376230.
Chang C, Ortiz K, Ansari A, Gershwin ME. 2016a. The Zika outbreak of the 21st century. J Autoimmun 68:1–13. https://pubmed.ncbi.nlm.nih.gov/26925496/, https://doi.org/10.1016/j.jaut.2016.02.006.
Chang MP, Simkin DJ, de Lara ML, Kirsch TD. 2016b. Characterizing hospital admissions to a tertiary care hospital after Typhoon Haiyan. Disaster Med Public Health Prep 10(2):240–247. https://pubmed.ncbi.nlm.nih.gov/26832860/, https://doi.org/10.1017/dmp.2015.165.
Chastel C. 2007. Le tsunami du 26 decembre 2004: quel bilan epidemiologique deux ans plus tard? Bulletin de La Societe de Pathologie Exotique 100(2):139–142. https://pubmed.ncbi.nlm.nih.gov/17727040/.
Chen M-J, Lin C-Y, Wu Y-T, Wu P-C, Lung S-C, Su H-J. 2012. Effects of extreme precipitation to the distribution of infectious diseases in Taiwan, 1994–2008. PLoS One 7(6):e34651. https://pubmed.ncbi.nlm.nih.gov/22737206/, https://doi.org/10.1371/journal.pone.0034651.
Chharang N, Bhattacharyya S, Lalthazuali L. 2018. Experience of emergency medical relief work from a field area in South India. J Evolution Med Dent Sci 7(49):5330–5332, https://doi.org/10.14260/jemds/2018/1179.
Chien L-C, Yu H-L. 2014. Impact of meteorological factors on the spatiotemporal patterns of dengue fever incidence. Environ Int 73:46–56. https://pubmed.ncbi.nlm.nih.gov/25084561/, https://doi.org/10.1016/j.envint.2014.06.018.
Chirebvu E, Chimbari MJ, Ngwenya BN, Sartorius B. 2016. Clinical malaria transmission trends and its association with climatic variables in Tubu village, Botswana: a retrospective analysis. PLoS One 11(3):e0139843. https://pubmed.ncbi.nlm.nih.gov/26983035/, https://doi.org/10.1371/journal.pone.0139843.
Chretien J-P, Anyamba A, Small J, Tucker CJ, Britch SC, Linthicum KJ. 2008. Environmental Biosurveillance for Epidemic Prediction: Experience with Rift Valley Fever. In: Biosurveillance and Biosecurity. International Workshop, BioSecure 2008. Proceedings. LNCS 5354. Zeng D, Chen H, Rolka H, Lober B, eds. Berlin/Heidelberg, Germany: Springer-Verlag, 169–174.
Christophers SR. 1960. Aedes aegypti (L.) The Yellow Fever Mosquito: Its Life History, Bionomics and Structure. Cambridge, UK: Cambridge University Press, https://doi.org/10.4269/ajtmh.1961.10.112.
Cibulskis RE, Alonso P, Aponte J, Aregawi M, Barrette A, Bergeron L, et al. 2016. Malaria: Global progress 2000–2015 and future challenges. Infectious Diseases of Poverty 5(1):61, https://doi.org/10.1186/s40249-016-0151-8.
Cissoko M, Sagara I, Sankaré MH, Dieng S, Guindo A, Doumbia Z, et al. 2020. Geo-epidemiology of malaria at the health area level, Dire Health District, Mali, 2013–2017. Int J Environ Res Public Health 17(11):3982. https://pubmed.ncbi.nlm.nih.gov/32512740/, https://doi.org/10.3390/ijerph17113982.
Clements AN. 1992. The Biology of Mosquitoes. London, UK: Chapman & Hall.
Cordova SP, Smith DW, Broom AK, Lindsay MD, Dowse GK, Beers MY. 2000. Murray Valley encephalitis in Western Australia in 2000, with evidence of southerly spread. Commun Dis Intell 24(12):368–372. https://pubmed.ncbi.nlm.nih.gov/11225378/.
Corrin T, Ackford R, Mascarenhas M, Greig J, Waddell LA. 2021. Eastern equine encephalitis virus: a scoping review of the global evidence. Vector Borne Zoonotic Dis 21(5):305–320. https://pubmed.ncbi.nlm.nih.gov/33332203/, https://doi.org/10.1089/vbz.2020.2671.
Crandell JH, Nowak M, Laatsch EM, van Overeem A, Barbour CE, Dewey R, et al. 1993. Assessment of damage to single-family homes caused by Hurricanes Andrew and Iniki. Washington, DC: U.S. Department of Housing and Urban Development.
Das D, Grais RF, Okiro EA, Stepniewska K, Mansoor R, van der Kam S, et al. 2018. Complex interactions between malaria and malnutrition: a systematic literature review. BMC Med 16(1):186. https://pubmed.ncbi.nlm.nih.gov/30371344/, https://doi.org/10.1186/s12916-018-1177-5.
Day JF, Curtis GA. 1999. Blood feeding and oviposition by Culex nigripalpus (Diptera: Culicidae) before, during, and after a widespread St. Louis encephalitis virus epidemic in Florida. J Med Entomol 36(2):176–181. https://pubmed.ncbi.nlm.nih.gov/10083754/.
Dickerson AK, Shankles PG, Madhavan NM, Hu DL. 2012. Mosquitoes survive raindrop collisions by virtue of their low mass. Proc Natl Acad Sci USA 109(25):9822–9827. https://pubmed.ncbi.nlm.nih.gov/22665779/, https://doi.org/10.1073/pnas.1205446109.
Dietz VJ, Rigau-Perez JG, Sanderson L, Diaz L, Gunn RA. 1990. Health assessment of the 1985 flood disaster in Puerto Rico. Disasters 14(2):164–170, https://doi.org/10.1111/j.1467-7717.1990.tb01057.x.
Ding G, Gao L, Li X, Zhou M, Liu Q, Ren H, et al. 2014. A mixed method to evaluate burden of malaria due to flooding and waterlogging in Mengcheng County, China: a case study. PLoS One 9(5):e97520. https://pubmed.ncbi.nlm.nih.gov/24830808/, https://doi.org/10.1371/journal.pone.0097520.
Ding G, Li X, Li X, Zhang B, Jiang B, Li D, et al. 2019. A time-trend ecological study for identifying flood-sensitive infectious diseases in Guangxi, China from 2005 to 2012. Environ Res 176:108577. https://pubmed.ncbi.nlm.nih.gov/31306984/, https://doi.org/10.1016/j.envres.2019.108577.
Doggett SL, Clancy J, Haniotis J, Russell R, Hueston L, Marchetti M, Shaw C, Dwyer D. 2001. 2000–2001 Annual Report. In: The New South Wales Arbovirus Surveillance & Mosquito Monitoring Program. Arbovirus Disease Control Advisory Group, NSW Health, Westmead, New South Wales, Australia.
Ebi KL, Balbus JM, Luber G, Bole A, Crimmins A, Glass G, et al. 2018. Human health. In: Impacts, Risks, and Adaptation in the United States: Fourth National Climate Assessment, vol. II. Reidmiller DR, Avery CW, Easterling DR, Kunkel KE, Lewis KLM, Maycock TK, Stewart BC, eds. U.S. Global Change Research Program, 572–603, https://doi.org/10.7930/NCA4.2018.CH14.
Ebi KL, Hess JJ. 2020. Health risks due to climate change: inequity in causes and consequences. Health Aff (Millwood) 39(12):2056–2062. https://pubmed.ncbi.nlm.nih.gov/33284705/, https://doi.org/10.1377/hlthaff.2020.01125.
Eke RA, Chigbu LN, Nwachukwu W. 2006. High prevalence of asymptomatic plasmodium infection in a suburb of Aba town, Nigeria. Ann Afr Med 5(1):42–45.
El Mamy ABO, Baba MO, Barry Y, Isselmou K, Dia ML, Hampate B, et al. 2011. Unexpected Rift Valley fever outbreak, northern Mauritania. Emerg Infect Dis 17(10):1894–1896. https://pubmed.ncbi.nlm.nih.gov/22000364/, https://doi.org/10.3201/eid1710.110397.
Elsanousi YEA, Elmahi AS, Pereira I, Debacker M. 2018. Impact of the 2013 floods on the incidence of malaria in Almanagil Locality, Gezira State, Sudan. PLoS Curr 10. https://pubmed.ncbi.nlm.nih.gov/30443430/, https://doi.org/10.1371/currents.dis.8267b8917b47bc12ff3a712fe4589fe1.
Emmelin A, Fantahun M, Berhane Y, Wall S, Byass P. 2009. Vulnerability to episodes of extreme weather: Butajira, Ethiopia, 1998–1999. Glob Health Action 2(1):1829. https://pubmed.ncbi.nlm.nih.gov/20052373/, https://doi.org/10.3402/gha.v2i0.1829.
Ernst KC, Walker KR, Reyes-Castro P, Joy TK, Castro-Luque AL, Diaz-Caravantes RE, et al. 2017. Aedes aegypti (Diptera: Culicidae) longevity and differential emergence of dengue fever in two cities in Sonora, Mexico. J Med Entomol 54(1):204–211. https://pubmed.ncbi.nlm.nih.gov/28082648/, https://doi.org/10.1093/jme/tjw141.
Foster WA, Walker ED. 2019. Mosquitoes (Culicidae). In: Medical and Veterinary Entomology. Mullen GR, Durden LA, eds. 3rd ed. Cham, Switzerland: Elsevier Inc., 955–1178.
Gao L, Zhang Y, Ding G, Liu Q, Jiang B. 2016. Identifying flood-related infectious diseases in Anhui Province, China: a spatial and temporal analysis. Am J Trop Med Hyg 94(4):741–749. https://pubmed.ncbi.nlm.nih.gov/26903612/, https://doi.org/10.4269/ajtmh.15-0338.
Gilliland TM, Rowley WA, Swack NS, Vandyk JK, Bartoces MG. 1995. Arbovirus surveillance in Iowa, USA, during the flood of 1993. J Am Mosq Control Assoc 11(2 pt 1):157–161. https://pubmed.ncbi.nlm.nih.gov/7595438/.
Giribabu D, Muvva VR, Joshi NK, Rao SS. 2020. Assessment of epidemiological implications due to serial tropical cyclones in India: introspecting the recent sanitation interventions. Disaster Med Public Health Prep 1–10. https://pubmed.ncbi.nlm.nih.gov/31928562/, https://doi.org/10.1017/dmp.2019.129.
Grossi-Soyster EN, Cook EAJ, de Glanville WA, Thomas LF, Krystosik AR, Lee J, et al. 2017. Serological and spatial analysis of alphavirus and flavivirus prevalence and risk factors in a rural community in western Kenya. PLoS Negl Trop Dis 11(10):e0005998. https://pubmed.ncbi.nlm.nih.gov/29040262/, https://doi.org/10.1371/journal.pntd.0005998.
Gudo ES, Pinto G, Weyer J, Le Roux C, Mandlaze A, José AF, et al. 2016. Serological evidence of Rift Valley fever virus among acute febrile patients in Southern Mozambique during and after the 2013 heavy rainfall and flooding: implication for the management of febrile illness. Virol J 13:96. https://pubmed.ncbi.nlm.nih.gov/27278404/, https://doi.org/10.1186/s12985-016-0542-2.
Guha-Sapir D, van Panhuis WG. 2009. Health impact of the 2004 Andaman Nicobar earthquake and tsunami in Indonesia. Prehosp Disaster Med 24(6):493–499. https://pubmed.ncbi.nlm.nih.gov/20301065/, https://doi.org/10.1017/s1049023x00007391.
Harrison BA, Whitt PB, Roberts LF, Lehman JA, Lindsey NP, Nasci RS, et al. 2009. Rapid assessment of mosquitoes and arbovirus activity after floods in southeastern Kansas, 2007. J Am Mosq Control Assoc 25(3):265–271. https://pubmed.ncbi.nlm.nih.gov/19852215/, https://doi.org/10.2987/08-5754.1.
Hasan T, Bambrick H. 2013. The effects of climate variables on the outbreak of dengue in Queensland 2008–2009. Southeast Asian J Trop Med Public Health 44(4):613–622. https://pubmed.ncbi.nlm.nih.gov/24050094/.
Hertig E. 2019. Distribution of anopheles vectors and potential malaria transmission stability in Europe and the Mediterranean area under future climate change. Parasit Vectors 12(1):18–19. https://pubmed.ncbi.nlm.nih.gov/30621785/, https://doi.org/10.1186/s13071-018-3278-6.
Himeidan YE, Hamid EE, Thalib L, Elbashir MI, Adam I. 2007. Climatic variables and transmission of falciparum malaria in New Halfa, eastern Sudan. East Mediterr Health J 13(1):17–24. https://pubmed.ncbi.nlm.nih.gov/17546901/.
Hopkins CC, Hollinger FB, Johnson RF, Dewlett HJ, Newhouse VF, Chamberlain RW. 1975. The epidemiology of St. Louis encephalitis in Dallas, Texas, 1966. Am J Epidemiol 102(1):1–15. https://pubmed.ncbi.nlm.nih.gov/1155433/, https://doi.org/10.1093/oxfordjournals.aje.a112128.
Hubálek Z, Savage HM, Halouzka J, Juricová Z, Sanogo YO, Lusk S. 2000. West Nile virus investigations in South Moravia, Czechland. Viral Immunol 13(4):427–433. https://pubmed.ncbi.nlm.nih.gov/11192289/, https://doi.org/10.1089/vim.2000.13.427.
Hubálek Z, Zeman P, Halouzka J, Juricová Z, Štovícková E, Bálková H, et al. 2005. Mosquitoborne viruses, Czech Republic, 2002. Emerg Infect Dis 11(1):116–118. https://pubmed.ncbi.nlm.nih.gov/15705333/, https://doi.org/10.3201/eid1101.040444.
IPCC (Intergovernmental Panel on Climate Change). 2012. Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation: Special report of the Intergovernmental Panel on Climate Change. Cambridge, UK: Cambridge University Press, https://doi.org/10.1017/CBO9781139177245.009.
IPCC. 2018. Global Warming of 1.5C. Masson-Delmotte V, Zhai P, Pörtner HO, Roberts D, Skea J, Shukla PR, Pirani A, Moufouma-Okia W, Péan C, Pidcock R, Connors S, Matthews JBR, Chen Y, Zhou X, Gomis MI, Lonnoy E, Maycock T, Tignor M, and Waterfield T, eds. https://www.ipcc.ch/sr15/ [accessed 13 August 2020].
Iwata O, Oki T, Ishiki A, Shimanuki M, Fuchimukai T, Chosa T, et al. 2013. Infection surveillance after a natural disaster: lessons learnt from the Great East Japan Earthquake of 2011. Bull World Health Organ 91(10):784–789. https://pubmed.ncbi.nlm.nih.gov/24115802/, https://doi.org/10.2471/BLT.13.117945.
Jury MR. 2008. Climate influence on dengue epidemics in Puerto Rico. Int J Environ Health Res 18(5):323–334. https://pubmed.ncbi.nlm.nih.gov/18821372/, https://doi.org/10.1080/09603120701849836.
Kakarla SG, Caminade C, Mutheneni SR, Morse AP, Upadhyayula SM, Kadiri MR, et al. 2019. Lag effect of climatic variables on dengue burden in India. Epidemiol Infect 147:e170. https://pubmed.ncbi.nlm.nih.gov/31063099/, https://doi.org/10.1017/S0950268819000608.
Kamal M, Kenawy MA, Rady MH, Khaled AS, Samy AM. 2018. Mapping the global potential distributions of two arboviral vectors Aedes aegypti and Ae. Albopictus under changing climate. PLoS One 13(12):e0210122. https://pubmed.ncbi.nlm.nih.gov/30596764/, https://doi.org/10.1371/journal.pone.0210122.
Kaur H, Habibullah MS, Nagaratnam S. 2020. Malaria and natural disasters: evidence using GMM approach. Int J Bus Soc 21(2):703–716, https://doi.org/10.33736/ijbs.3284.2020.
Kengne P, Charmantier G, Blondeau-Bidet E, Costantini C, Ayala D. 2019. Tolerance of disease-vector mosquitoes to brackish water and their osmoregulatory ability. Ecosphere 10(10):e02783, https://doi.org/10.1002/ecs2.2783.
Knope K, Whelan P, Smith D, Johansen C, Moran R, Doggett S, et al. 2013. Arboviral diseases and malaria in Australia, 2010-11: annual report of the national arbovirus and malaria advisory committee. Commun Dis Intell Q Rep 37(1):E1–E20. https://pubmed.ncbi.nlm.nih.gov/23692155/.
Koenraadt CJM, Harrington LC. 2008. Flushing effect of rain on container-inhabiting mosquitoes Aedes aegypti and Culex pipiens (Diptera: Culicidae). J Med Entomol 45(1):28–35. https://pubmed.ncbi.nlm.nih.gov/18283939/, https://doi.org/10.1093/jmedent/45.1.28.
Kolachi HB, Siddiqui MI, Memon MN, Memon MS, Abro GS, Kazi A. 2013. Integration of preventive and curative health services for flood survivors by LUMHS - A successful health care model for preparedness of future floods in Pakistan (a community based research survey). J Liaquat University of Medical and Health Sciences 12(2):120–124.
Kondo H, Seo N, Yasuda T, Hasizume M, Koido Y, Ninomiya N, et al. 2002. Post-flood–infectious diseases in Mozambique. Prehosp Disaster Med 17(3):126–133. https://pubmed.ncbi.nlm.nih.gov/12627915/.
Kopp A, Gillespie TR, Hobelsberger D, Estrada A, Harper JM, Miller RA, et al. 2013. Provenance and geographic spread of St. Louis encephalitis virus. mBio 4(3):e00322-13. https://pubmed.ncbi.nlm.nih.gov/23760463/, https://doi.org/10.1128/mBio.00322-13.
Kraemer MUG, Reiner RC, Brady OJ, Messina JP, Gilbert M, Pigott DM, et al. 2019. Past and future spread of the arbovirus vectors Aedes aegypti and Aedes albopictus. Nat Microbiol 4(5):854–863. https://pubmed.ncbi.nlm.nih.gov/30833735/, https://doi.org/10.1038/s41564-019-0376-y.
Krishnamoorthy K, Jambulingam P, Natarajan R, Shriram AN, Das PK, Sehgal SC. 2005. Altered environment and risk of malaria outbreak in South Andaman, Andaman & Nicobar islands, India affected by tsunami disaster. Malar J 4:32. https://pubmed.ncbi.nlm.nih.gov/16029514/, https://doi.org/10.1186/1475-2875-4-32.
Kumar B, Manuja A, Gulati B, Virmani N, Tripathi BN. 2018. Zoonotic viral diseases of equines and their impact on human and animal health. Open Virol J 12(1):80–98. https://pubmed.ncbi.nlm.nih.gov/30288197/, https://doi.org/10.2174/1874357901812010080.
Kumari R, Joshi PL, Lal S, Shah W. 2009. Management of malaria threat following tsunami in Andaman & Nicobar islands, India and impact of altered environment created by tsunami on malaria situation of the islands. Acta Trop 112(2):204–211. https://pubmed.ncbi.nlm.nih.gov/19660429/, https://doi.org/10.1016/j.actatropica.2009.07.028.
Kuraishy NA, Haider SR-U-D. 1961. Malaria incidence in cyclone hit areas of Chittagong district, East Pakistan. Pak J Health 11:114–131. https://pubmed.ncbi.nlm.nih.gov/14460683/.
Langkulsen U, Promsakha Na Sakolnakhon K, James N. 2020. Climate change and dengue risk in Central region of Thailand. Int J Environ Health Res 30(3):327–335. https://pubmed.ncbi.nlm.nih.gov/30919662/, https://doi.org/10.1080/09603123.2019.1599100.
Lehman JA, Hinckley AF, Kniss KL, Nasci RS, Smith TL, Campbell GL, et al. 2007. Effect of Hurricane Katrina on arboviral disease transmission. Emerg Infect Dis 13(8):1273–1275. https://pubmed.ncbi.nlm.nih.gov/17953117/.
Li L, Switzer AD, Wang Y, Chan C-H, Qiu Q, Weiss R. 2018. A modest 0.5-m rise in sea level will double the tsunami hazard in Macau. Sci Adv 4(8):eaat1180. https://pubmed.ncbi.nlm.nih.gov/30116780/, https://doi.org/10.1126/sciadv.aat1180.
Lindsay SW, Bodker R, Malima R, Msangeni HA, Kisinza W. 2000. Effect of 1997 – 98 El Niño on highland malaria in Tanzania. Lancet 355(9208):989–990. https://pubmed.ncbi.nlm.nih.gov/10768443/, https://doi.org/10.1016/S0140-6736(00)90022-9.
Linthicum KJ, Anyamba A, Chretien J-P, Small J, Tucker CJ, Britch SC. 2010. The role of global climate patterns in the spatial and temporal distribution of vector-borne disease. In: Vector Biology, Ecology and Control. Atkinson PW, ed. Chester, UK: Springer Science+Business Media BV, 3–13, https://doi.org/10.1007/978-90-481-2458-9_1.
Linthicum KJ, Anyamba A, Tucker CJ, Kelley PW, Myers MF, Peters CJ. 1999. Climate and satellite indicators to forecast Rift Valley fever epidemics in Kenya. Science 285(5426):397–400. https://pubmed.ncbi.nlm.nih.gov/10411500/.
Liu-Helmersson J, Rocklöv J, Sewe M, Brännström Å. 2019. Climate change may enable Aedes aegypti infestation in major European cities by 2100. Environ Res 172:693–699. https://pubmed.ncbi.nlm.nih.gov/30884421/, https://doi.org/10.1016/j.envres.2019.02.026.
Liyanage P, Tissera H, Sewe M, Quam M, Amarasinghe A, Palihawadana P, et al. 2016. A spatial hierarchical analysis of the temporal influences of the El Niño-Southern Oscillation and weather on dengue in Kalutara District, Sri Lanka. Int J Environ Res Public Health 13:1087. https://pubmed.ncbi.nlm.nih.gov/27827943/, https://doi.org/10.3390/ijerph13111087.
Loebach P, Korinek K. 2019. Disaster vulnerability, displacement, and infectious disease: Nicaragua and Hurricane Mitch. Popul Environ 40(4):434–455, https://doi.org/10.1007/s11111-019-00319-4.
Lowe R, Gasparrini A, Van Meerbeeck CJ, Lippi CA, Mahon R, Trotman AR, et al. 2018. Nonlinear and delayed impacts of climate on dengue risk in Barbados: a modelling study. PLoS Med 15(7):e1002613. https://pubmed.ncbi.nlm.nih.gov/30016319/, https://doi.org/10.1371/journal.pmed.1002613.
Mabaso MLH, Ndlovu NC. 2012. Critical review of research literature on climate-driven malaria epidemics in sub-Saharan Africa. Public Health 126(11):909–919. https://pubmed.ncbi.nlm.nih.gov/22981043/, https://doi.org/10.1016/j.puhe.2012.07.005.
Maes P, Harries AD, Van den Bergh R, Noor A, Snow RW, Tayler-Smith K, et al. 2014. Can timely vector control interventions triggered by atypical environmental conditions prevent malaria epidemics? A case-study from Wajir County, Kenya. PLoS One 9(4):e92386. https://pubmed.ncbi.nlm.nih.gov/24699034/, https://doi.org/10.1371/journal.pone.0092386.
Manimunda SP, Sugunan AP, Sha WA, Singh SS, Shriram AN, Vijayachari P. 2011. Tsunami, post-tsunami malaria situation in Nancowry group of islands, Nicobar district, Andaman and Nicobar islands. Indian J Med Res 133:76–82. https://pubmed.ncbi.nlm.nih.gov/21321423/.
Mason J, Cavalie P. 1965. Malaria epidemic in Haiti following a hurricane. Am J Trop Med Hyg 14(4):533–539, https://doi.org/10.4269/ajtmh.1965.14.533.
Massarra C, Friedland C, Akhnoukh A. 2021. Development of wind and flood vulnerability index for residential buildings. In: JIC Smart Cities 2019: Design and Construction of Smart Cities. El Dimeery I, Baraka M, Ahmed SM, Akhnoukh A, Anwar MB, El Khafif M, eds. Cham, Switzerland: Springer, 151–159, https://doi.org/10.1007/978-3-030-64217-4_18.
Mathur KK, Harpalani G, Kalra NL, Murthy GGK, Narasimham MVVL. 1992. Epidemic of malaria in Barmer district (Thar desert) of Rajasthan during 1990. Indian J Malariol 29(1):1–10. https://pubmed.ncbi.nlm.nih.gov/1459294/.
McCall P, Lloyd L, Nathan MB. 2009. Vector management and delivery of vector control services. In: Dengue: Guidelines for Diagnosis, Treatment, Prevention and Control: New edition. Geneva, Switzerland: World Health Organization Special Programme for Research and Training in Tropical Diseases, 59–90.
McCarthy MC, Haberberger RL, Salib AW, Soliman BA, El-Tigani A, Khalid IO, et al. 1996. Evaluation of arthropod-borne viruses and other infectious disease pathogens as the causes of febrile illnesses in the Khartoum Province of Sudan. J Med Virol 48(2):141–146. https://pubmed.ncbi.nlm.nih.gov/8835346/, https://doi.org/10.1002/(SICI)1096-9071(199602)48:2%3C141::AID-JMV4%3E3.0.CO;2-9.
McDonnell L, Kolbe T, Carvan T, Gilchrist K. 1994. Outbreak of Ross River virus disease in the South West districts of NSW, summer 1993. N S W Public Health Bull 5(9):98–99, https://doi.org/10.1071/NB94037.
Memon MS, Solangi S, Lakho S, Arain ZI, Naz F, Zaki M. 2014. Morbidity and mortality of malaria during monsoon flood of 2011: south east Asia experience. Iran J Public Health 43(1):28–34. https://pubmed.ncbi.nlm.nih.gov/26060676/.
Méndez-Lázaro P, Muller-Karger FE, Otis D, McCarthy MJ, Pena-Orellana M. 2014. Assessing climate variability effects on dengue incidence in San Juan, Puerto Rico. Int J Environ Res Public Health 11(9):9409–9428. https://pubmed.ncbi.nlm.nih.gov/25216253/, https://doi.org/10.3390/ijerph110909409.
Messina JP, Brady OJ, Golding N, Kraemer MUG, Wint GRW, Ray SE, et al. 2019. The current and future global distribution and population at risk of dengue. Nat Microbiol 4(9):1508–1515. https://pubmed.ncbi.nlm.nih.gov/31182801/, https://doi.org/10.1038/s41564-019-0476-8.
Monaghan AJ, Sampson KM, Steinhoff DF, Ernst KC, Ebi KL, Jones B, et al. 2018. The potential impacts of 21st century climatic and population changes on human exposure to the virus vector mosquito Aedes aegypti. Clim Change 146(3–4):487–500. https://pubmed.ncbi.nlm.nih.gov/29610543/, https://doi.org/10.1007/s10584-016-1679-0.
Mori H, Wu J, Ibaraki M, Schwartz FW. 2018. Key factors influencing the incidence of West Nile virus in Burleigh County, North Dakota. Int J Environ Res Public Health 15(9):1928. https://pubmed.ncbi.nlm.nih.gov/30189592/, https://doi.org/10.3390/ijerph15091928.
Morin CW, Comrie AC, Ernst K. 2013. Climate and dengue transmission: evidence and implications. Environ Health Perspect 121(11–12):1264–1272. https://pubmed.ncbi.nlm.nih.gov/24058050/, https://doi.org/10.1289/ehp.1306556.
Muriuki D, Hahn S, Hexom B, Allan R. 2012. Cross-sectional survey of malaria prevalence in tsunami-affected districts of Aceh Province, Indonesia. Int J Emerg Med 5(1):11. https://pubmed.ncbi.nlm.nih.gov/22353602/, https://doi.org/10.1186/1865-1380-5-11.
Nandi J, Sharma SN. 2000. Efficacy of chloroquine in febrile Plasmodium falciparum infected children in Mewat region of Haryana. J Commun Dis 32(2):137–143. https://pubmed.ncbi.nlm.nih.gov/11198399/.
Nasci RS, Moore CG. 1998. Vector-borne disease surveillance and natural disasters. Emerg Infect Dis 4(2):333–334. https://pubmed.ncbi.nlm.nih.gov/9621210/.
National Weather Service. (n.d.). Skywarn. National Oceanic and Atmospheric Administration. https://www.weather.gov/dtx/wwa_defs [accessed 2 May 2021].
Natuzzi ES, Joshua C, Shortus M, Reubin R, Dalipanda T, Ferran K, et al. 2016. Defining population health vulnerability following an extreme weather event in an urban pacific island environment: Honiara, Solomon Islands. Am J Trop Med Hyg 95(2):307–314. https://pubmed.ncbi.nlm.nih.gov/27091867/, https://doi.org/10.4269/ajtmh.16-0177.
Nderitu L, Lee JS, Omolo J, Omulo S, O’Guinn ML, Hightower A, et al. 2011. Sequential Rift Valley fever outbreaks in eastern Africa caused by multiple lineages of the virus. J Infect Dis 203(5):655–665. https://pubmed.ncbi.nlm.nih.gov/21282193/, https://doi.org/10.1093/infdis/jiq004.
Nelms M, Opp WR, Yates MM, Stokes GM, Broussard BB, Meek CL, et al. 1993. Emergency mosquito control associated with Hurricane Andrew – Florida and Louisiana, 1992. MMWR Morb Mortal Wkly Rep 42(13):240–242. https://pubmed.ncbi.nlm.nih.gov/8459791/.
NFIP (National Flood Insurance Program). 2015. Dwelling Form: Standard Flood Insurance Policy. F-122/October 2015. Federal Emergency Management Agency (FEMA). https://www.fema.gov/sites/default/files/2020-05/F-122_Dwelling_SFIP_102015.pdf [accessed 2 May 2021].
Nielsen NO, Makaula P, Nyakuipa D, Bloch P, Nyasulu Y, Simonsen PE. 2002. Lymphatic filariasis in lower shire, southern Malawi. Trans R Soc Trop Med Hyg 96(2):133–138. https://pubmed.ncbi.nlm.nih.gov/12055799/.
Noor AM, Alegana VA, Kamwi RN, Hansford CF, Ntomwa B, Katokele S, et al. 2013. Malaria control and the intensity of Plasmodium falciparum transmission in Namibia 1969–1992. PLoS One 8(5):e63350. https://pubmed.ncbi.nlm.nih.gov/23667604/, https://doi.org/10.1371/journal.pone.0063350.
Norris DE. 2004. Mosquito-borne diseases as a consequence of land use change. EcoHealth 1(1):19–24, https://doi.org/10.1007/s10393-004-0008-7.
Nsereko G, Kadobera D, Okethwangu D, Nguna J, Rutazaana D, Kyabayinze DJ, et al. 2020. Malaria outbreak facilitated by appearance of vector-breeding sites after heavy rainfall and inadequate preventive measures: Nwoya District, Northern Uganda, February-May 2018. J Environ Public Health 2020:1–10. https://pubmed.ncbi.nlm.nih.gov/32377206/, https://doi.org/10.1155/2020/5802401.
Nyakarahuka L, de St. Maurice A, Purpura L, Ervin E, Balinandi S, Tumusiime A, et al. 2018. Prevalence and risk factors of Rift Valley fever in humans and animals from Kabale District in Southwestern Uganda, 2016. PLoS Negl Trop Dis 12(5):e0006412. https://pubmed.ncbi.nlm.nih.gov/29723189/, https://doi.org/10.1371/journal.pntd.0006412.
O’Leary DR, Rigau-Pérez JG, Hayes EB, Vorndam AV, Clark GG, Gubler DJ. 2002. Assessment of dengue risk in relief workers in Puerto Rico after Hurricane Georges, 1998. Am J Trop Med Hyg 66(1):35–39. https://pubmed.ncbi.nlm.nih.gov/12135265/, https://doi.org/10.4269/ajtmh.2002.66.35.
Office for Coastal Management, National Oceanic and Atmospheric Administration. 2020. Economics and Demographics. https://coast.noaa.gov/states/fast-facts/economics-and-demographics.html [accessed 17 December 2020].
Pan American Health Organization. 1998. Impact of Hurricane Mitch in Central America. Epidemiol Bull 19(4):1–14. https://pubmed.ncbi.nlm.nih.gov/10330785/.
Pan American Health Organization. 1999. Principales enfermedades infecciosas en centroamérica durante 1998, antes y después de Mitch. Revista Panamericana de Salud Publica/Pan Am J Public Health 6(6):440–443.
Parekh FK, Yeh KB, Olinger G, Ribeiro FA. 2020. Infectious disease risks and vulnerabilities in the aftermath of an environmental disaster in Minas Gerais, Brazil. Vector Borne Zoonotic Dis 20(5):387–389. https://pubmed.ncbi.nlm.nih.gov/31944914/, https://doi.org/10.1089/vbz.2019.2501.
Parham PE, Waldock J, Christophides GK, Hemming D, Agusto F, Evans KJ, et al. 2015. Climate, environmental and socio-economic change: weighing up the balance in vector-borne disease transmission. Philos Trans R Soc Lond B Biol Sci 370(1665):20130551. https://pubmed.ncbi.nlm.nih.gov/25688012/, https://doi.org/10.1098/rstb.2013.0551.
Pawar AB, Bansal RK, Kumar M, Jain NC, Vaishnav KG. 2008. A rapid assessment of mosquito breeding, vector control measures and treatment seeking behaviour in selected slums of Surat, Gujarat, India, during post-flood period. J Vector Borne Dis 45(4):325–327. https://pubmed.ncbi.nlm.nih.gov/19248661/.
Pradutkanchana J, Pradutkanchana S, Kemapanmanus M, Wuthipum N, Silpapojakul K. 2003. The etiology of acute pyrexia of unknown origin in children after a flood. Southeast Asian J Trop Med Public Health 34(1):175–178. https://pubmed.ncbi.nlm.nih.gov/12971532/.
Pramanik M, Singh P, Kumar G, Ojha VP, Dhiman RC. 2020. El Niño Southern Oscillation as an early warning tool for dengue outbreak in India. BMC Public Health 20(1):1498. https://pubmed.ncbi.nlm.nih.gov/33008350/, https://doi.org/10.1186/s12889-020-09609-1.
Ramadona AL, Lazuardi L, Hii YL, Holmner Å, Kusnanto H, Rocklöv J. 2016. Prediction of dengue outbreaks based on disease surveillance and meteorological data. PLoS One 11(3):e0152688. https://pubmed.ncbi.nlm.nih.gov/27031524/, https://doi.org/10.1371/journal.pone.0152688.
Ramirez IJ. 2019. Exploring tropical variability and extremes impacts on population vulnerability in Piura, Peru: The case of the 1997-98 El Niño. In: Tropical Extremes: Natural Variability and Trends. Venugopal V, Sukhatme J, Murtugudde R, Roca R, eds. Amsterdam, Netherlands: Elsevier, 263–297.
Rattanavong S, Dubot-Pérès A, Mayxay M, Vongsouvath M, Lee SJ, Cappelle J, et al. 2020. Spatial epidemiology of Japanese encephalitis virus and other infections of the central nervous system infections in Lao PDR (2003–2011): a retrospective analysis. PLoS Negl Trop Dis 14(5):e0008333. https://pubmed.ncbi.nlm.nih.gov/32453806/, https://doi.org/10.1371/journal.pntd.0008333.
Reed KD. 2018. Viral zoonoses. In: Reference Module in Biomedical Sciences. 2nd ed. Amsterdam, Netherlands: Elsevier Inc. https://doi.org/10.1016/B978-0-12-801238-3.95729-5.
Reeves WC, Bellamy RE, Geib AF, Scrivani RP. 1964. Analysis of the circumstances leading to abortion of a western equine encephalitis epidemic. Am J Hyg 80(2):205–220. https://pubmed.ncbi.nlm.nih.gov/14215831/.
Rigau-Perez JG, Ayala-Lopez A, Vorndam AV, Clark GG. 2001. Dengue activity in Puerto Rico during an interepidemic period (1995–1997). Am J Trop Med Hyg 64(1–2):75–83. https://pubmed.ncbi.nlm.nih.gov/11425167/, https://doi.org/10.4269/ajtmh.2001.64.75.
Roiz D, Boussès P, Simard F, Paupy C, Fontenille D. 2015. Autochthonous chikungunya transmission and extreme climate events in Southern France. PLoS Negl Trop Dis 9(6):e0003854. https://pubmed.ncbi.nlm.nih.gov/26079620/, https://doi.org/10.1371/journal.pntd.0003854.
Roy MP, Gupta R, Chopra N, Meena SK, Aggarwal KC. 2018. Seasonal variation and dengue burden in paediatric patients in New Delhi. J Trop Pediatr 64(4):336–341. https://pubmed.ncbi.nlm.nih.gov/29045735/, https://doi.org/10.1093/tropej/fmx077.
Ruiz EF, Vasquez-Galindo CM, Aquije-Pariona XM, Torres-Roman JS. 2018. Outbreaks caused by Aedes aegyptis due to El Niño in a coastal area of Peru. Travel Med Infect Dis 21:78–79. https://pubmed.ncbi.nlm.nih.gov/29129817/, https://doi.org/10.1016/j.tmaid.2017.11.003.
Russac PA. 1986. Epidemiological surveillance: malaria epidemic following the Niño phenomenon. Disasters 10(2):112–117. https://pubmed.ncbi.nlm.nih.gov/21664978/, https://doi.org/10.1111/j.1467-7717.1986.tb00576.x.
Ryan SJ, Carlson CJ, Mordecai EA, Johnson LR. 2019. Global expansion and redistribution of aedes-borne virus transmission risk with climate change. PLoS Negl Trop Dis 13(3):e0007213. https://pubmed.ncbi.nlm.nih.gov/30921321/, https://doi.org/10.1371/journal.pntd.0007213.
Saenz R, Bissell RA, Paniagua F. 1995. Post-disaster malaria in Costa Rica. Prehosp Disaster Med 10(3):154–160. https://pubmed.ncbi.nlm.nih.gov/10155423/, https://doi.org/10.1017/S1049023X00041935.
Samy AM, Elaagip AH, Kenawy MA, Ayres CFJ, Peterson AT, Soliman DE. 2016. Climate change influences on the global potential distribution of the mosquito Culex quinquefasciatus, vector of West Nile virus and lymphatic filariasis. PLoS One 11(10):e0163863. https://pubmed.ncbi.nlm.nih.gov/27695107/, https://doi.org/10.1371/journal.pone.0163863.
Samy AM, Peterson AT, Hall M. 2017. Phylogeography of Rift Valley fever virus in Africa and the Arabian Peninsula. PLoS Negl Trop Dis 11(1):e0005226. https://pubmed.ncbi.nlm.nih.gov/28068340/, https://doi.org/10.1371/journal.pntd.0005226.
Saulnier DD, Hanson C, Ir P, Alvesson HM, von Schreeb J. 2018. The effect of seasonal floods on health: analysis of six years of national health data and flood maps. Int J Environ Res Public Health 15(4):665. https://pubmed.ncbi.nlm.nih.gov/29614051/, https://doi.org/10.3390/ijerph15040665.
Schmidt CA, Comeau G, Monaghan AJ, Williamson DJ, Ernst KC. 2018. Effects of desiccation stress on adult female longevity in Aedes aegypti and Ae. albopictus (Diptera: Culicidae): results of a systematic review and pooled survival analysis. Parasit Vectors 11(1):267. https://pubmed.ncbi.nlm.nih.gov/29695282/, https://doi.org/10.1186/s13071-018-2808-6.
Schmidt W-P, Suzuki M, Thiem VD, White RG, Tsuzuki A, Yoshida L-M, et al. 2011. Population density, water supply, and the risk of dengue fever in Vietnam: cohort study and spatial analysis. PLoS Med 8(8):e1001082. https://pubmed.ncbi.nlm.nih.gov/21918642/, https://doi.org/10.1371/journal.pmed.1001082.
Seidahmed OME, Eltahir EAB. 2016. A sequence of flushing and drying of breeding habitats of Aedes aegypti (L.) prior to the low dengue season in Singapore. PLoS Negl Trop Dis 10(7):e0004842. https://pubmed.ncbi.nlm.nih.gov/27459322/, https://doi.org/10.1371/journal.pntd.0004842.
Selvey LA, Dailey L, Lindsay M, Armstrong P, Tobin S, Koehler AP, et al. 2014. The changing epidemiology of Murray Valley encephalitis in Australia: the 2011 outbreak and a review of the literature. PLoS Negl Trop Dis 8(1):e2656. https://pubmed.ncbi.nlm.nih.gov/24466360/, https://doi.org/10.1371/journal.pntd.0002656.
Shao K, Liu W, Gao Y, Ning Y. 2019. The influence of climate change on tsunami-like solitary wave inundation over fringing reefs. J Integr Environ Sci 16(1):71–88, https://doi.org/10.1080/1943815X.2019.1614071.
Singh H, Singh N, Mall RK. 2020. Japanese encephalitis and associated environmental risk factors in Eastern Uttar Pradesh: a time series analysis from 2001 to 2016. Acta Trop 212:105701. https://pubmed.ncbi.nlm.nih.gov/32956640/, https://doi.org/10.1016/j.actatropica.2020.105701.
Sinka ME, Bangs MJ, Manguin S, Chareonviriyaphap T, Patil AP, Temperley WH, et al. 2011. The dominant Anopheles vectors of human malaria in the Asia-Pacific region: occurrence data, distribution maps and bionomic précis. Parasit Vectors 4:89. https://pubmed.ncbi.nlm.nih.gov/21612587/, https://doi.org/10.1186/1756-3305-4-89.
Sivanpillai R, Jacobs KM, Mattilio CM, Piskorski EV. 2021. Rapid flood inundation mapping by differencing water indices from pre- and post-flood Landsat images. Front Earth Sci 15(1):1–11, https://doi.org/10.1007/s11707-020-0818-0.
Smith DW, Speers DJ, MacKenzie JS. 2011. The viruses of Australia and the risk to tourists. Travel Med Infect Dis 9(3):113–125. https://pubmed.ncbi.nlm.nih.gov/21679887/, https://doi.org/10.1016/j.tmaid.2010.05.005.
Smith KR, Woodward A, Campbell-Lendrum D, Chadee DD, Honda Y, Liu Q, et al. 2014. Human health: impacts, adaptation, and co-benefits. In: Climate Change 2014: Impacts, Adaptation and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Chang. Field CB, Barros VR, Dokken DJ, Mach KJ, Mastrandrea MD, Bilir TE, et al., eds. Cambridge, UK: Cambridge University Press, 709–754. https://www.ipcc.ch/site/assets/uploads/2018/02/WGIIAR5-Chap11_FINAL.pdf [accessed 18 August 2021].
Sorensen CJ, Borbor-Cordova MJ, Calvello-Hynes E, Diaz A, Lemery J, Stewart-Ibarra AM. 2017. Climate variability, vulnerability, and natural disasters: a case study of zika virus in Manabi, Ecuador following the 2016 earthquake. Geohealth 1(8):298–304. https://pubmed.ncbi.nlm.nih.gov/32158994/, https://doi.org/10.1002/2017GH000104.
Soverow JE, Wellenius GA, Fisman DN, Mittleman MA. 2009. Infectious disease in a warming world: how weather influenced West Nile virus in the United States (2001–2005). Environ Health Perspect 117(7):1049–1052. https://pubmed.ncbi.nlm.nih.gov/19654911/, https://doi.org/10.1289/ehp.0800487.
Sow A, Faye O, Ba Y, Ba H, Diallo D, Faye O, et al. 2014. Rift Valley fever outbreak, southern Mauritania, 2012. Emerg Infect Dis 20(2):296–299. https://pubmed.ncbi.nlm.nih.gov/24447334/, https://doi.org/10.3201/eid2002.131000.
Steiger DBM, Ritchie SA, Laurance SGW. 2016. Land use influences mosquito communities and disease risk on remote tropical islands: a case study using a novel sampling technique. Am J Trop Med Hyg 94(2):314–321. https://pubmed.ncbi.nlm.nih.gov/26711512/, https://doi.org/10.4269/ajtmh.15-0161.
Sudaryo MK, Besral Prameswari MF, Umniyati H, Guha-Sapir D. 2020. Acute infection following flood disaster: an example from Bojonegoro District, East Java, Indonesia. Indian J Public Health Res Dev 11(3):2442–2448.
Sunder Rao AR, Nasiruddin M, Achuthan C, Sitaraman NL. 1960. Study of an outbreak of epidemic malaria in Arasikere Taluk, Hassan District, Mysore State. Indian J Malariol 14(3):253–267.
Syafruddin D, Lestari YE, Permana DH, Asih PBS, St. Laurent B, Zubaidah S, et al. 2020. Anopheles sundaicus complex and the presence of anopheles epiroticus in Indonesia. PLoS Negl Trop Dis 14(7):e0008385. https://pubmed.ncbi.nlm.nih.gov/32614914/, https://doi.org/10.1371/journal.pntd.0008385.
Tall JA, Gatton ML. 2020. Flooding and arboviral disease: predicting ross river virus disease outbreaks across inland regions of south-eastern Australia. J Med Entomol 57(1):241–251. https://pubmed.ncbi.nlm.nih.gov/31310648/, https://doi.org/10.1093/jme/tjz120.
Tricco AC, Lillie E, Zarin W, O’Brien KK, Colquhoun H, Levac D, et al. 2018. PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation. Ann Intern Med 169(7):467–473. https://pubmed.ncbi.nlm.nih.gov/30178033/, https://doi.org/10.7326/M18-0850.
Tusting LS, Ippolito MM, Willey BA, Kleinschmidt I, Dorsey G, Gosling RD, et al. 2015. The evidence for improving housing to reduce malaria: a systematic review and meta-analysis. Malar J 14(1):209. https://pubmed.ncbi.nlm.nih.gov/26055986/, https://doi.org/10.1186/s12936-015-0724-1.
U.S. Global Change Research Program. 2018. Climate science special report: fourth national climate assessment. vol. I. U.S. Global Change Research Program 1:470. https://science2017.globalchange.gov/downloads/CSSR2017_FullReport.pdf%0Ascience2017.globalchange.gov [accessed 25 October 2020].
Villholth KG, Amerasinghe PH, Jeyakumar P, Panabokke CR, Woolley O, Weerasinghe MD, et al. 2005. Tsunami impacts on shallow groundwater and associated water supply on the East Coast of Sri Lanka. International Water Management Institute, Colombo, Sri Lanka.
Webb C. 2013. Managing mosquitoes in constructed freshwater wetlands. In: Workbook for Managing Urban Wetlands in Australia. Paul S, ed. 1st ed. Sydney, Australia: Sydney Olympic Park Authority, 137–160. https://www.sopa.nsw.gov.au/Resource-Centre/WET-eBook-Workbook-for-Managing-Urban-Wetlands-in-Australia [accessed 18 August 2021].
WHO (World Health Organization). 2020. World Malaria Report 2020: 20 years of global progress and challenges.
Wolfarth-Couto B, Filizola N, Durieux L. 2020. Seasonal pattern of malaria cases and the relationship with hydrologic variability in the Amazonas state, Brazil. Rev Bras Epidemiol 23:e200018. https://pubmed.ncbi.nlm.nih.gov/32159628/, https://doi.org/10.1590/1980-549720200018.
Woodruff BA, Toole MJ, Rodrigue DC, Brink EW, Mahgoub ES, Mohamed Ahmed M, et al. 1990. Disease surveillance and control after a flood: Khartoum, Sudan, 1988. Disasters 14(2):151–163, https://doi.org/10.1111/j.1467-7717.1990.tb01056.x.
World Meteorological Organization. 2021. Flash Flood Guidance System with Global Coverage (FFGS). https://public.wmo.int/en/projects/ffgs.
Xiang J, Hansen A, Liu Q, Liu X, Xiaoliang Tong M, Sun Y, et al. 2017. Association between dengue fever incidence and meteorological factors in Guangzhou, China, 2005–2014. Environ Res 153:17–26. https://pubmed.ncbi.nlm.nih.gov/27883970/, https://doi.org/10.1016/j.envres.2016.11.009.
Xiao J, Liu T, Lin H, Zhu G, Zeng W, Li X, et al. 2018. Weather variables and the El Niño Southern Oscillation may drive the epidemics of dengue in Guangdong Province, China. Sci Total Environ 624:926–934. https://pubmed.ncbi.nlm.nih.gov/29275255/, https://doi.org/10.1016/j.scitotenv.2017.12.200.
Yuan H-Y, Wen T-H, Kung Y-H, Tsou H-H, Chen C-H, Chen L-W, et al. 2019. Prediction of annual dengue incidence by hydro-climatic extremes for southern Taiwan. Int J Biometeorol 63(2):259–268. https://pubmed.ncbi.nlm.nih.gov/30680621/, https://doi.org/10.1007/s00484-018-01659-w.
Zeng Z, Zhan J, Chen L, Chen H, Cheng S. 2021. Global, regional, and national dengue burden from 1990 to 2017: a systematic analysis based on the global burden of disease study 2017. EClinicalMedicine 32:100712. https://pubmed.ncbi.nlm.nih.gov/33681736/, https://doi.org/10.1016/j.eclinm.2020.100712.
Zhang F, Liu Z, Zhang C, Jiang B. 2016. Short-term effects of floods on Japanese encephalitis in Nanchong, China, 2007–2012: a time-stratified case-crossover study. Sci Total Environ 563-564:1105–1110. https://pubmed.ncbi.nlm.nih.gov/27241207/, https://doi.org/10.1016/j.scitotenv.2016.05.162.
Zheng J, Han W, Jiang B, Ma W, Zhang Y. 2017. Infectious diseases and tropical cyclones in southeast China. Int J Environ Res Public Health 14(5):494. https://pubmed.ncbi.nlm.nih.gov/28481286/, https://doi.org/10.3390/ijerph14050494.
Information & Authors
Information
Published In
License Information
EHP is an open-access journal published with support from the National Institute of Environmental Health Sciences, National Institutes of Health. All content is public domain unless otherwise noted.
History
Received: 28 December 2020
Revision received: 10 August 2021
Accepted: 19 August 2021
Published online: 28 September 2021
Corrected: 28 December 2021
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click DOWNLOAD.
Cited by
- Peiris M, Urban system interactions during flood events: a system dynamics modeling approach, Journal of Water and Climate Change, 10.2166/wcc.2025.188, 16, 1, (51-69), (2025).
- Rosser J, Openshaw J, Lin A, Taruc R, Tela A, Tamodding N, Abdullah N, Amiruddin M, Buyukcangaz E, Barker S, Turagabeci A, Ansariadi undefined, Leder K, Wahid I, Seroprevalence, incidence estimates, and environmental risk factors for dengue, chikungunya, and Zika infection amongst children living in informal urban settlements in Indonesia and Fiji, BMC Infectious Diseases, 10.1186/s12879-024-10315-1, 25, 1, (2025).
- Hiscox A, Spencer F, Dennehy J, Dyall W, Jenkins A, Narendran A, Das A, Logan J, Jones R, The impact of adverse weather events on cockroaches and flies, and the possible effects on disease, Medical and Veterinary Entomology, 10.1111/mve.12797, (2025).
- Randive K, Godbole P, Randive K, Godbole P, Geo-Diseases: An Introduction, Medical Geology for Beginners, 10.1007/978-3-031-82765-5_4, (65-115), (2025).
- Zain A, Sadarangani S, Shek L, Vasoo S, Climate change and its impact on infectious diseases in Asia, Singapore Medical Journal, 10.4103/singaporemedj.SMJ-2023-180, 65, 4, (211-219), (2024).
- Ziliotto M, Chies J, Ellwanger J, Extreme Weather Events and Pathogen Pollution Fuel Infectious Diseases: The 2024 Flood-Related Leptospirosis Outbreak in Southern Brazil and Other Red Lights, Pollutants, 10.3390/pollutants4030028, 4, 3, (424-433), (2024).
- Mavroulis S, Mavrouli M, Lekkas E, Tsakris A, Impact of the September 2023 Storm Daniel and Subsequent Flooding in Thessaly (Greece) on the Natural and Built Environment and on Infectious Disease Emergence, Environments, 10.3390/environments11080163, 11, 8, (163), (2024).
- Ward R, Brookes V, Rahman K, Are we joining the One Health dots? A scoping review of research on the one health effects of extreme weather events in eastern Australia, Frontiers in Veterinary Science, 10.3389/fvets.2024.1423501, 11, (2024).
- Zhang Y, Wang M, Huang M, Zhao J, Innovative strategies and challenges mosquito-borne disease control amidst climate change, Frontiers in Microbiology, 10.3389/fmicb.2024.1488106, 15, (2024).
- Pitt S, Gunn A, The One Health Concept, British Journal of Biomedical Science, 10.3389/bjbs.2024.12366, 81, (2024).
- See more