{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"medRxiv"}],"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T13:42:02Z","timestamp":1768570922809,"version":"3.49.0"},"posted":{"date-parts":[[2025,2,10]]},"group-title":"Infectious Diseases (except HIV\/AIDS)","reference-count":30,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2025,2,10]],"date-time":"2025-02-10T00:00:00Z","timestamp":1739145600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2025,2,10]]},"abstract":"<jats:title>SUMMARY<\/jats:title>\n                <jats:sec>\n                  <jats:title>Background<\/jats:title>\n                  <jats:p>Rift Valley fever (RVF) is a zoonotic disease that causes sporadic, multi-country epidemics. However, RVF virus (RVFV) also circulates during inter-epidemic periods. There is limited understanding of how climate change will affect inter-epidemic RVF. Here, we project inter-epidemic RVF risk under future climate scenarios, focusing on the East African countries of Kenya, Tanzania, and Uganda.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>We combined data on inter-epidemic RVF outbreaks and spatially-explicit predictor variables to build a predictive model of inter-epidemic RVF risk. We validated our model using RVFV serological data from humans. We then projected inter-epidemic RVF risk for three future time periods (2021-2040, 2041-2060, 2061-208) under three climate scenarios (SSP126, SSP245, SSP370). Finally, we combined risk projections with human population projections to estimate the future population at risk of inter-epidemic RVF across the study region.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Findings<\/jats:title>\n                  <jats:p>Our model showed seasonality in inter-epidemic RVF, with risk peaking May-July following the long rains (March-May). Projections for future climate scenarios suggested that disease risk will increase January-March, with the present-day hotspots of east Kenya, southeast Tanzania, and southwest Uganda remaining high-risk. By 2061-2080, &gt; 117 million people in the study region may be at risk from inter-epidemic RVF, a fourfold increase relative to the historical (1970-2000) estimate of \u223c25 million people.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Interpretation<\/jats:title>\n                  <jats:p>Climate change will shift the inter-epidemic RVF risk landscape, with increasing short rains (October-December) driving increased risk January-March. Mitigating the future health impacts of RVF will require increased disease surveillance, prevention, and control effort in risk hotspots.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Funding<\/jats:title>\n                  <jats:p>US National Institutes of Health.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>RESEARCH IN CONTEXT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Evidence before this study<\/jats:title>\n                    <jats:p>As for arboviruses generally, global climate change may shift the geographic distribution, timing, and severity of disease burden imposed by Rift Valley fever (RVF). To review the existing evidence for climate change impacts on RVF while adopting a specific geographic focus on East Africa, we searched Web of Science using the query string \u201cALL=(climate change) AND ALL=(Rift Valley fever) AND ALL=(\u2018East Africa\u2019 OR Kenya OR Tanzania OR Uganda)\u201d. Our search returned 39 results published between 2007 and 2024. We reviewed these contents to identify work with a substantive focus on understanding how future climate change will affect RVF (as opposed to investigations discussing any connection between climatic factors and RVF occurrence). We identified a total of ten papers that met this criterion: two were reviews, four analyzed mechanistic compartmental models, and four were empirical papers adopting other modeling and analysis methods. These studies highlighted the key role of precipitation and temperature in RVF epidemiology. Heavy precipitation is a well- known RVF driver given that rainfall can cause large amounts of surface water to become available for mosquito vector breeding. As such, expected wetting trends in East Africa under climate change scenarios could fuel increased frequency and severity of RVF. Temperature influences on RVF are underappreciated relative to precipitation, but climate-driven compartmental models emphasize that RVFV transmission is likely to be highest in areas that maintain optimal temperatures for mosquito development (\u223c22-26\u00b0C).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Added value of this study<\/jats:title>\n                    <jats:p>Adopting a machine learning approach, we found that precipitation, goat density, soil silt, and elevation were among the most important predictors of inter-epidemic RVF. Our model, which was trained on monthly climatic data, showed seasonally-varying RVF risk, with risk peaking following the long rains season (March-May) and, to a lesser degree, following the short rains (October-December). We used our trained model, which was validated against serological data from humans, to project RVF risk for three future time periods (2021- 2040, 2041-2060, 2061-208) under three climate scenarios (SSP126, SSP245, SSP370) using 11 climate models from the Coupled Model Intercomparison Project (CMIP6). As a result, our multi-model projections of inter-epidemic RVF risk explicitly incorporate multiple sources of climate uncertainty. Projections suggested that RVF risk will increase January-March across the study region, particularly under high-emissions scenarios (i.e., SSP370). Although there are some anticipated changes in the spatial distribution of RVF risk, future risk hotspots largely mirror the present-day, with high risk in east Kenya, southeast Tanzania, and southwest Uganda.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Implications of all the available evidence<\/jats:title>\n                    <jats:p>Precipitation, the major driver of RVF, shapes both the temporal and spatial patterning of RVF risk across East Africa. Projections of future RVF risk are also strongly influenced by precipitation, with projected increases in disease risk January-March arising from projected increases in short rains (October-December) precipitation under future climate scenarios. Accurate projections of future precipitation, including a better understanding of potential changes to climatic linkages like the El Ni\u00f1o-Southern Oscillation and Indian Ocean Dipole, will enable meaningful prediction of future RVF risk that can inform disease interventions. Greater consideration of population-level host immunity and climate adaptation behaviors in East Africa (i.e., changing livestock management practices) would also allow for more realistic RVF risk projections.<\/jats:p>\n                  <\/jats:sec>\n                <\/jats:sec>","DOI":"10.1101\/2025.02.08.25321747","type":"posted-content","created":{"date-parts":[[2025,2,10]],"date-time":"2025-02-10T11:00:28Z","timestamp":1739185228000},"source":"Crossref","is-referenced-by-count":1,"title":["Projecting climate change impacts on inter-epidemic risk of Rift Valley fever across East Africa"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1153-5356","authenticated-orcid":false,"given":"Evan A.","family":"Eskew","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4728-4023","authenticated-orcid":false,"given":"Erin","family":"Clancey","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6568-435X","authenticated-orcid":false,"given":"Deepti","family":"Singh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9257-4647","authenticated-orcid":false,"given":"Silvia","family":"Situma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2944-9157","authenticated-orcid":false,"given":"Luke","family":"Nyakarahuka","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7629-7002","authenticated-orcid":false,"given":"M. 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