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Environmental limits of Rift Valley fever revealed using ecoepidemiological mechanistic models
Edited by Burton H. Singer, University of Florida, Gainesville, FL, and approved June 19, 2018 (received for review February 23, 2018)

Significance
Vector-borne diseases represent complex infection transmission systems; previous epidemiological models have been unable to formally capture the relationship between the ecological limits of vector species and the dynamics of pathogen transmission. By making this advance for the key disease, Rift Valley fever, we are able to show how seasonally varying availability of water bodies and ambient temperatures dictate when the mosquito vector populations will persist and importantly, those sets of conditions resulting in stable oscillations of disease transmission. Importantly, under the latter scenario, short-term health control measures will likely fail, as the system quickly returns to the original configuration after the intervention stops. Our model, therefore, offers an important tool to better understand vector-borne diseases and design effective eradication programs.
Abstract
Vector-borne diseases (VBDs) of humans and domestic animals are a significant component of the global burden of disease and a key driver of poverty. The transmission cycles of VBDs are often strongly mediated by the ecological requirements of the vectors, resulting in complex transmission dynamics, including intermittent epidemics and an unclear link between environmental conditions and disease persistence. An important broader concern is the extent to which theoretical models are reliable at forecasting VBDs; infection dynamics can be complex, and the resulting systems are highly unstable. Here, we examine these problems in detail using a case study of Rift Valley fever (RVF), a high-burden disease endemic to Africa. We develop an ecoepidemiological, compartmental, mathematical model coupled to the dynamics of ambient temperature and water availability and apply it to a realistic setting using empirical environmental data from Kenya. Importantly, we identify the range of seasonally varying ambient temperatures and water-body availability that leads to either the extinction of mosquito populations and/or RVF (nonpersistent regimens) or the establishment of long-term mosquito populations and consequently, the endemicity of the RVF infection (persistent regimens). Instabilities arise when the range of the environmental variables overlaps with the threshold of persistence. The model captures the intermittent nature of RVF occurrence, which is explained as low-level circulation under the threshold of detection, with intermittent emergence sometimes after long periods. Using the approach developed here opens up the ability to improve predictions of the emergence and behaviors of epidemics of many other important VBDs.
Vector-borne diseases (VBDs) form an important class of infectious diseases, with over 1 billion human cases and 1 million human deaths per year (1), and they are a significant contributor to global poverty. Current patterns of VBD occurrence are likely to change in the future due to the accelerating rate of global climate and other environmental change that is predicted over the next century (2). Climate and land use change as well as globalization are expected to affect the geographic distribution of arthropod species (3) through a variety of mechanisms, such as changes to the variability in weather conditions that alter survival; reproduction and biting rates of the vectors; changes to the availability of water bodies via, for instance, new irrigation patterns and dam constructions, creating new habitats for disease-competent vectors; and human mobility and animal trade increasing the opportunity for vectors to reach and establish in new areas. Pathogen ecology is influenced by climate and weather as well; for instance, temperature affects both the susceptibility of vectors to infection and pathogen extrinsic incubation periods, which usually require pathogen replication at ambient temperatures (4, 5). From here on, we refer to “ambient temperature” as “temperature.”
These issues provide the basis of the work reported here. We focus on Rift Valley fever (RVF), an important mosquito-borne viral zoonosis. The causative virus is responsible for major epidemics in Africa, and its range seems to be expanding as shown by phylogeographic analysis (6) and recent epidemic occurrence in Saudi Arabia and Yemen (7⇓⇓–10). Furthermore, concern has been raised about the potential for environmental/climatic changes causing increased impact of RVF in endemic areas or facilitating its spread to new regions of the world (10⇓–12). Rift Valley fever virus (RVFV) has a significant economic impact on the livestock industry in Africa and can cause fatal disease in humans (13).
RVFV has a complex, multispecies epidemiology, and it is transmitted by biting mosquitos and occasionally, directly by animal body fluids. Infected mosquitos transmit RVFV when taking a blood meal, potentially infecting a wide range of species. The disease is most significant in domestic ruminants, although wild animals [e.g., buffalos (14) and rodents (15)] might play an important role as reservoir hosts. Although more than 40 mosquito and midge species are known to be capable of transmitting RVFV (16), Aedes sp., Mansoni sp., and Culex sp. are thought to be the most important for virus transmission to livestock and people.
Climatic drivers, such as temperature and rainfall, have a strong impact on the complex ecology of both RVFV and its vectors (17⇓⇓–20). Thus, the epidemiology of RVFV is likely to be strongly impacted by climate change (21). Other environmental, cultural, and socioeconomic factors, such as gathering of large numbers of people and domestic animals during religious festivities, have relevant implications for the infection dynamics of RVFV, including driving epidemics (22⇓⇓–25).
The complex features of RVFV infection dynamics have led to many studies. Empirical statistical approaches have identified key environmental variables (e.g., temperature and rainfall) that are associated with disease epidemics, enabling disease risk to be mapped (11, 18, 19, 22, 23, 25⇓⇓⇓⇓⇓⇓⇓⇓⇓–35). Mechanistic models have added crucial insights for understanding links between disease transmission and the environment by exploring the impact of seasonality and studying the processes leading to epidemic transmission (24, 36⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓–52). Despite progress, these approaches are still subject to important limitations: the earlier mechanistic models do not incorporate seasonality; most models tend to include either only rainfall or only temperature as a contributing factor; and if included, seasonality is usually incorporated only as an ad hoc periodic variation in the response (e.g., oviposition rate) rather than in the causative variable, undermining the realism of the approaches.
A further critical limitation of these studies is that they rely on rainfall data. In empirical statistical approaches, rainfall is often considered a “predictor variable” [with the commonly associated problem of collinearity (53)]. In mechanistic models, rainfall is usually a proxy for breeding sites. In complex hydrogeological models, rainfall is merely an input to represent water bodies; the major problem with this approach is that the dependence of RVFV on rainfall varies widely across countries and ecoregions due to, for example, different types of terrain, evaporation rates, delay between rainfall occurrence and establishment of water bodies, etc.
To overcome these limitations, we developed a unified, process-based model built on a realistic representation of how the dynamics of water bodies obtained from satellite images (rather than rainfall) and temperature influence the ecology of the primary mosquito vectors and the epidemiology of RVFV. A critical feature of using this approach is our ability to investigate the combined impact of seasonality on both water availability and temperature, allowing us to (i) capture the influence of seasonal patterns of temperature and water bodies on the quantitative transmission dynamics of RVFV, (ii) quantify the environmental drivers that lead to regional endemicity of RVFV, (iii) assess if transovarial transmission in Aedes sp. (the only species of mosquitos for which ovarian transmission is known) is necessary for RVFV persistence, (iv) isolate the mechanisms allowing virus reemergence after long periods of inactivity in endemic regions (43, 54), and (v) identify if and under which conditions the complex patterns of RVFV epidemics resemble chaotic behavior [i.e., the system being highly sensitive to initial conditions (55), rendering disease predictions difficult].
Analysis
Our analyses were conducted within two main contexts: a theoretical case, represented by a simple sinusoidal variation of the surface area of water bodies and of temperature (represented by Eqs. 5 and 6), and a realistic situation, where we used empirical data for Kenya [viz., spatially averaged temperature (56) and the total surface area of water bodies over the entire territory divided by the surface of Kenya] (SI Appendix, SI Text). Here and throughout, we refer to these two situations as “theoretical model” and “realistic model,” respectively. We first ran the theoretical model by systematically changing the mean annual temperature and mean annual surface area of water bodies (i.e., parameters
Results
Influence of the Seasonal Patterns of Temperature and Water Bodies on the Quantitative Dynamics of RVFV.
The theoretical model shows (Fig. 1; more details are in SI Appendix, Fig. S19) that different amplitudes and frequencies of fluctuations in temperature and water availability within the system result in different disease patterns. It is possible, for example, that one or both mosquito species might go extinct, that there could be stable oscillations with one or more annual peaks in the mosquito population but in an RVFV-free situation, that there could be stable mosquito populations with sporadic RVFV epidemics, or that RVFV might become endemic.
Environmental constraints leading to persistent and nonpersistent regimens mosquitos and RVFV. (A–C) Impact of mean water bodies’ surface area and mean temperature on the population of mosquitos and RVFV prevalence. Water bodies’ surface area and temperature are described by sinusoidal functions according to Eqs. 5 and 6. The x axis shows the mean water bodies’ surface area
Quantifying the Environmental Drivers Leading to Regional Endemicity of RVFV.
The theoretical model predicts the existence of a temperature-dependent threshold in mean surface area of water bodies below which mosquito populations and RVFV always fade out (gray areas in Fig. 1, which are referred to as the “nonpersistent regimen”). The model also showed the parameter space (i.e., the set of all possible combinations of values for the different parameters) resulting in a “persistent regimen” (i.e., sustained oscillations in the vectors and RVFV) (colored areas in Fig. 1). The intensity of the color reflects the yearly averaged population of the mosquitos or the yearly averaged prevalence of RVFV in livestock. The optimal conditions for mosquito occur when the mean body surface area is at its greatest and when the mean temperature is
When Does the Complexity of RVFV Dynamics Resemble Chaotic Behavior?
Stability refers to the property of an ecosystem to return to equilibrium if perturbed (55) or equivalently, that the system will always reach the equilibrium state regardless of the initial conditions. In the theoretical model, the equilibria are represented by extinction of mosquito species and/or RVFV infection (nonpersistent regimen) or more or less complex periodic oscillations (persistent regimen). For the mosquito populations, Floquet analysis (Materials and Methods and SI Appendix, SI Text) shows that the long-term mathematical solutions are stable. For RVFV infection, numerical computations show that the solutions are stable after the initial conditions (i.e., the initial number of livestock) are fixed (SI Appendix, Fig. S25). Changing the initial number of livestock has no practical effect on the overall population of mosquitos when the impact of livestock on mosquito oviposition and biting rate is assumed to be negligible (i.e., for very large values of the parameter q as in this case, but other scenarios are shown in SI Appendix, Fig. S23). The number of livestock, however, predictably impacts the temporal patterns of infected mosquitos and infected livestock (SI Appendix, Fig. S25), and the system can no longer be considered stable if the number of livestock is externally perturbed. Accordingly, animal movements, including the immigration of infected animals, might have a significant impact on the pattern of RVFV infection. Similar behavior is observed for the realistic model, where simulations show that, regardless of the initial conditions, the system approaches the same asymptotic limit, with only the initial number of livestock having a direct impact on the patterns of infections (SI Appendix, Fig. S25). The property that the system always reverts to the same asymptotic solution (after fixing the initial number of livestock) is not general. An important counterexample is shown in Fig. 2 (SI Appendix, Fig. S26). In this simulation experiment, we consider the two scenarios illustrated by path A and path B in Fig. 1C: first, when the mean temperature and mean surface area of water bodies are always within the RVFV persistent regimen, and second, when these values transit from RVFV persistence to RVFV nonpersistence and then back again. To do so, we divided the entire time (32 y) into eight cycles; each 4-y cycle (described either by path A or by path B in Fig. 1C) consists of four intervals of 1 y each (represented by the segments in the paths). For each interval, we let the mean values
During the simulation (32 y), the mean surface area of water bodies and mean temperature are cyclic changing according to paths A and B illustrated in Fig. 1C. For path A, during the first year, the mean surface area of water bodies increases according to a stepwise function in a 4-mo interval (SI Appendix, Fig. S27) from 6,500 to 7,500 m2, and the mean temperature is constant at
Is Transovarial Transmission in Aedes Necessary for RVFV Persistence?
The simulations of RVFV dynamics showed persistence in Culex sp. in the absence of Aedes mosquitos (Fig. 3) over 15 y in the realistic model. The numerical simulation shows that persistent patterns of RVFV occur in the absence of Aedes sp. In the theoretical model, the use of Floquet theory should prevent the problem of infection persistence at unrealistic low levels [“atto-fox problem” (60)], as the theory focuses on the stability of the precise zero or periodic solution (although here, the stability of RVFV was studied only numerically). In general, random extinctions of RVFV preclude persistence of infection, although one could argue that deterministic models mimic the fact that random extinctions are compensated by random immigration of infected mosquitos or livestock. Incorporating demographic stochasticity and spatial immigration would address this concern. Taking all of this into account, we cautiously conclude that the transovarial transmission of RVFV in Aedes sp. is not a prerequisite for RVFV persistence over time, although the models provide no evidence to discount this as an important (49) transmission route in reality.
Assessing the impact of transovarial transmission. Dynamics of Culex sp. population and RVFV infection in livestock in the absence of Aedes sp. population for the realistic model. The theoretical case is exemplified in SI Appendix, Fig. S19B.
Isolate the Drivers Enabling the Virus to Reemerge After Long Periods of Inactivity in Endemic Regions.
Here, we assumed that, when the mean number of infected livestock is below a certain threshold (chosen to be 50) (SI Appendix, SI Text discusses 5 infected animals and
Assessing the intermittent nature of RVFV. The histograms represent the distribution of duration of interepidemic periods for empirical data, for the model where Aedes spp. is absent (origin due only to undetected cases) and where both mosquitos species are present (origin due to undetected cases and transovarial transmission).
Discussion
We identified the range of seasonally varying temperatures and water-body extent leading either to extinction of mosquito populations and/or RVFV or to established mosquito populations and endemicity of the infection. These results allow prediction of future geographic distribution of RVFV due to changes in environmental and climatic conditions across the globe.
To achieve this, we developed a process-based mathematical model, which unifies environmental factors, the ecology of mosquitos, and the epidemiology of RVFV.
A Unified Framework for the Dynamics of VBDs.
A key advantage of this model is its conceptual simplicity, with the undeniable complexity of the system reduced to a few fundamental factors: surface area of water bodies governing mosquito oviposition rates, temperature affecting mosquito developmental rates, and their survival and biting rates as well as the extrinsic incubation period of RVFV. The impact of these parameters cascades on the dynamics of the mosquito population and thus, RVFV. The seasonality of mosquito abundance and infection prevalence is largely governed by the seasonality in water-body surface area and temperature. The resulting patterns, however, are not trivial due to the nonlinearity of the system; even in a theoretical system represented by simple sinusoidal variation of water-body surface area and temperature, the different combinations of these result in qualitatively different regimens, including one or both mosquitos becoming extinct, an RVFV-free scenario but with established mosquito populations, or sustained oscillations of mosquito abundance and RVFV prevalence (in mosquitos and livestock) with one or more annual peaks. The modular nature of the model facilitates its calibration and validation. For example, the mosquito model can be tested in an RVFV-free situation, only subsequently including the effects of the disease.
Environmental Conditions Allowing Established Mosquito Populations and Viral Persistence.
The abundance of mosquito eggs is ultimately constrained by the maximum density of eggs (i.e., number of eggs per unit surface area) and the surface area of water bodies, resulting in a carrying capacity that results in a stable mosquito population irrespective of initial conditions. In the realistic scenarios, this was shown numerically; in the theoretical systems, we proved the stability of the system by using Floquet analysis. This showed a lower threshold in mean water-body surface areas, below which the mosquito populations will go extinct; otherwise, it will result in sustained oscillation. The value of this threshold depends nonmonotonically on the mean temperature, and it is confined between lower and upper values, reflecting the fact that mosquitos do not survive in very cold or very dry hot temperatures. The analysis also showed the importance of the frequency of fluctuations in water-body dynamics, especially for Aedes sp. Similar thresholds in temperature and water bodies occur for the persistence of RVFV in livestock, reflecting the geographic distribution of the disease. Here, livestock numbers were also critical. The biophysical interpretation of stability analysis is extremely important. For example, stable oscillations in the mosquito population imply that, unless there is a permanent change in the drivers (e.g., average surface area of water bodies), any temporary measure aiming to reduce the mosquito population (e.g., chemical control) will not result in a permanent solution, as mosquito abundance is expected to return to the original values after application of control measures stops. Similarly, if mosquitos are imported into a region with temperature and water-body parameters that are in the persistent regimen, then they will become established in this new environment.
Intermittent Nature of RVFV and the Problem of Predictability.
Epidemics of RVFV are intermittent and typically are not very predictable (43, 54). Severe epidemics are provoked by flooding after protracted periods of drought. Transovarial transmission in Aedes mosquitos is a mechanism of RVFV persistence (61) and a possible explanation for the intermittent nature of RVFV epidemics, as presumably infected Aedes sp. eggs can survive for several years. Another explanation is that RVFV is always circulating in the population, perhaps in a cryptic reservoir (14, 15, 62), at very low level and is not detected. This is supported by evidence of interepidemic RVFV seropositivity among humans and animals (63, 64) and the indication of subclinical infection in livestock (65). Our model suggests that transovarial transmission is not necessary for interepidemic persistence of RVFV and that the infection may continuously circulate at low and largely undetectable levels in between irregular epidemics; change in immunity in livestock populations is playing an important role in the irregularity of the infection patterns. This result is strictly valid, however, when all animals and mosquitos are well-connected (e.g., through animal movement), as our deterministic model is based on the assumption of uniform mixing. Our theoretical model shows that, after the initial number of livestock is fixed, the solution is stable, and long-term behavior can be accurately predicted even if the initial conditions, such as the exact number of infected animals or the abundance of mosquitos at a given time, are not known. If the number of livestock, however, is perturbed, the solutions are qualitatively and quantitatively different even if all other conditions are kept identical. Thus, for reliable predictions, accurate information on the demography of livestock is necessary (the impact of the livestock size on infection is discussed in SI Appendix, SI Text). In some situations, however, this is not sufficient. The mean surface areas of water bodies and temperatures can change (as in Kenya, where mean surface area of water bodies decreased during
A Program for Future Work.
This work identified important challenges that could be addressed by further theoretical work and model-guided fieldwork. Fieldwork can be designed to test well-defined hypotheses that emerge from the model, such as the predicted larger abundance of Aedes sp. in regions where water bodies are fluctuating more frequently and the existence of thresholds in surface area of water bodies and temperature confining the domain of the persistent regimens for mosquito species and RVFV infection. Further experiments to gauge the impact of livestock density on mosquito oviposition and biting rates (66) are crucial, as this will have an important effect on the mosquito population and on patterns of RVFV infection (SI Appendix, SI Text). In most cases, we focus on one host only. Copresence of multiple hosts can dilute or amplify the disease. Further investigations on host feeding preference (67) and the relationship between mosquito abundance and host population size are critical to estimate this effect (68). A challenging point is the large uncertainty associated with many parameter values; in particular, the life history parameters of mosquitos stage are often based on laboratory conditions and inferred for different species of mosquitos. Theoretical works like this can steer future fieldwork and experimentation to reduce the knowledge gaps that emerged from the model.
The potential impacts of multiple hosts, including wildlife hosts (e.g., buffalo), also need to be investigated. We assumed uniform mixing between mosquitos and livestock. As a result, the predicted patterns of infection in Aedes sp., Culex sp., and livestock are qualitatively similar. The model should be generalized to incorporate heterogeneity occurring in nature. Furthermore, the model needs to be refined to incorporate the impact of vegetation and natural predators on the ecology of mosquitos. This could be done, for example, by allowing the birth and mortality rates to depend on such factors and calibrating the model accordingly. The presence of livestock and other animals might attract mosquitos from neighbor areas via CO2 emission, resulting in a density-dependent vector-to-host ratio relationships (68). In general, climate change is expected to cause an increase in not only the average temperature but also, rainfall intensity and frequency. Climate projections can be readily incorporated into the model for a more accurate analysis of the impact of climate change on the ecology of mosquitos and the epidemiology of RVFV. The impact of animal movement is another crucial driver of RVFV (ref. 34 and discussion in ref. 69). Future research should address, for instance, how the epidemiology of RVFV changes in the presence of livestock immigration and how this is affected by the size of these imports and the number of infected animals in each batch.
Our analysis was done using a deterministic model, but environmental stochasticity and external periodic drivers (e.g., seasonality in temperature and surface area of water bodies) can resonate with the natural frequencies of the ecosystem (70), with large effects on the ecology of mosquitos and the epidemiology of RVFV. Furthermore, patterns of the interepidemic periods should be assessed by taking into account stochastic variability in demography and diagnostics at different spatial settings. These are crucial questions to consider in future research. Extension of the model to include spatial variability is the natural progression of this work. By using high spatiotemporal resolution of water bodies (71), temperature (56), type of vegetation data, and animal census, the model could be carefully calibrated to assess whether the environmental variables are within the persistent regimens. Then, the approach could be used to generate a map of potentially endemic regions for RVFV or other VBDs to plan interventions more effectively (e.g., aiming at long-term control of environmental conditions, such as reducing the size of water bodies, in endemic areas and short-term measures, such as limiting animal movement, in nonendemic areas). If the environmental variables are at the interface between persistent and nonpersistent regimens, then more robust uncertainty and sensitivity analysis are required, exploring not only the space of parameters but also, the plausible distribution of the initial conditions, such as livestock population and its infection prevalence. This also raises important practical and theoretical questions on the reliability of statistical models based on presence/absence of cases when the epidemiology is subject to chaotic behavior.
Materials and Methods
The model combines an ecological, stage-structured population dynamics model for the Aedes sp. and Culex sp. with an epidemiological susceptible–exposed–infectious–recovered compartmental model for the livestock and a susceptible–exposed–infectious model for the two mosquito populations. For simplicity, we assume only one host, although the model can be readily extended to include multiple heterogeneous hosts (e.g., goats, cattle, sheep). The stage-structured population dynamics of the mosquitos is largely based on the model of Otero et al. (72), which includes the effect of temperature on the development rate of the mosquitos. Important additions to the model of Otero et al. (72) are (i) the dependence of the oviposition process on the water bodies’ surface, (ii) the separation of Aedes sp. eggs into mature and immature eggs, and (iii) the dependency of the number of eggs per batch on the density of livestock. Below, we emphasize aspects of the model, while a detailed formulation of the framework is presented in SI Appendix, SI Text.
Ecoepidemiological Model.
The Culex sp. populations consist of eggs (
Temperature-dependent development rates for the gonotrophic cycles, in the limit of infinitely available blood meal, were based on parametrization presented in the literature (42); the other stages were modeled according to the simplification by Schoolfield et al. (73) of the model by Sharpe and DeMichele for poikilotherm development based on data from ref. 74 (SI Appendix, SI Text and Table S6). Life stage-specific mortality rates for Culex quinquefasciatus and Aedes aegypti were extracted from data collected under standard laboratory conditions from ref. 74. Ordinary least squares regression models were fitted with mortality rate as the response variable and temperature (15 °C to 34 °C) as the explanatory variable (SI Appendix, SI Text and Figs. S17 and S18). Other than the daily mortality in the pupal stage, there is an additional mortality associated with the emergence of the adult (72).
The population dynamics of eggs is regulated by the availability and dynamics of suitable breeding sites [i.e., temporary water bodies (dambos) (SI Appendix, Figs. S13 and S14) typically formed by heavy rainfall]. In contrast with Culex sp., Aedes sp. lay their eggs in the moist soils above mean high water surrounding the water body (SI Appendix, Fig. S14). According to ref. 75, the average time for egg deposition is
Aedes sp. eggs require a minimum desiccation period
Aedes sp. eggs will hatch at the time of the first flood [e.g., at time t when
Combined Mosquito and Livestock Population Model in the Presence of Infection.
RVFV transmission in Aedes mosquitos can be transovarial or horizontal, while only horizontal transmission, mediated by biting infectious hosts, is possible for Culex sp. Both adult Culex sp. and Aedes sp. can become infected after feeding on infectious livestock
Stability Analysis for Seasonal Systems: Floquet Theory.
Floquet analysis is a well-established tool suitable to study the stability of seasonal systems (77, 78). In the simplest scenarios, temperature and water bodies can be approximated by the periodic functions
Acknowledgments
We thank Dr. Erasmus Zu Ermgassen for his help during the preliminary stages of the work. This work was mainly conducted within the Dynamic Drivers of Disease in Africa Consortium, Natural Environment Research Council (NERC) Project NE-J001570-1, which was funded with support from the Ecosystem Services for Poverty Alleviation (ESPA) program. The ESPA program is funded by the Department for International Development, the Economic and Social Research Council, and the NERC. The work was partially supported by the National Institute for Health Research (NIHR) Health Protection Research Unit in Environmental Change and Health at the London School of Hygiene and Tropical Medicine in partnership with Public Health England (PHE) and in collaboration with the University of Exeter, University College London, and the Met Office. A.A.C. and J.L.N.W. are also supported by European Union FP7 Project ANTIGONE (Contract 278976). A.A.C. was supported by a Royal Society Wolfson Research Merit Award. J.L.N.W. is supported by the Alborada Trust. D.W.R. is supported by a Medical Research Council UK Research and Innovation/Rutherford Fellowship (MR/R02491X/1). The views expressed are those of the author(s) and not necessarily those of the National Health Service, the NIHR, the Department of Health, or PHE.
Footnotes
- ↵1To whom correspondence should be addressed. Email: g.loiacono{at}surrey.ac.uk.
Author contributions: G.L.I., A.A.C., and J.L.N.W. designed research; G.L.I. performed research; G.L.I., B.B., and D.W.R. collected the data; G.L.I. analyzed data; and G.L.I., A.A.C., B.B., D.G., D.W.R., and J.L.N.W. wrote the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1803264115/-/DCSupplemental.
Published under the PNAS license.
References
- ↵
- WHO
- ↵
- Pachauri R,
- Meyer L
- IPCC
- ↵
- ↵
- ↵
- Turell MJ
- ↵
- Soumaré POL, et al.
- ↵
- ↵
- ↵
- Chevalier V,
- Pépin M,
- Plée L,
- Lancelot R
- ↵
- ↵
- Taylor D, et al.
- ↵
- Golnar AJ,
- Kading RC,
- Hamer GL
- ↵
- ↵
- Beechler BR, et al.
- ↵
- ↵
- Meegan J,
- Bailey C
- ↵
- ↵
- Linthicum KJ
- ↵
- Anyamba A, et al.
- ↵
- ↵
- ↵
- ↵
- Métras R, et al.
- ↵
- Xiao Y, et al.
- ↵
- ↵
- ↵
- ↵
- ↵
- Caminade C, et al.
- ↵
- Sindato C, et al.
- ↵
- Gikungu D, et al.
- ↵
- Tran A, et al.
- ↵
- Munyua PM, et al.
- ↵
- Lancelot R, et al.
- ↵
- Olive MM, et al.
- ↵
- ↵
- ↵
- ↵
- ↵
- ↵
- Gao D,
- Cosner C,
- Cantrell RS,
- Beier JC,
- Ruan S
- ↵
- ↵
- Manore CA,
- Beechler BR
- ↵
- ↵
- Mweya CN,
- Holst N,
- Mboera LEG,
- Kimera SI
- ↵
- Chamchod F, et al.
- ↵
- Pedro SA,
- Abelman S,
- Ndjomatchoua FT,
- Sang R,
- Tonnang HEZ
- ↵
- Mpeshe SC,
- Luboobi LS,
- Nkansah-gyekye YAW
- ↵
- Pedro SA,
- Abelman S,
- Tonnang HEZ
- ↵
- Gachohi J,
- Njenga MK,
- Kitala P,
- Bett B
- ↵
- Miron RE,
- Giordano GA,
- Kealey AD,
- Smith RJ
- ↵
- Scoglio CM, et al.
- ↵
- Lo Iacono G, et al.
- ↵
- Paweska JT,
- Jansen van Vuren P
- ↵
- May RMRM
- ↵
- ↵
- Sang R, et al.
- ↵
- Sang R, et al.
- ↵
- ↵
- ↵
- ↵
- Pretorius A,
- Oelofsen MJ,
- Smith MS,
- van der Ryst E
- ↵
- ↵
- Mroz C, et al.
- ↵
- Nanyingi M, et al.
- ↵
- ↵
- Tchouassi DP, et al.
- ↵
- Lo Iacono G,
- Robin CA,
- Newton JR,
- Gubbins S,
- Wood JLN
- ↵
- Lo Iacono G, et al.
- ↵
- ↵
- Anonymous
- ↵
- ↵
- ↵
- ↵
- Christophers R
- ↵
- ↵
- Grimshaw R
- ↵
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