Article Text

Widening geographic range of Rift Valley fever disease clusters associated with climate change in East Africa
  1. Silvia Situma1,2,3,
  2. Luke Nyakarahuka4,5,6,
  3. Evans Omondi7,8,
  4. Marianne Mureithi2,
  5. Marshal Mutinda Mweu2,
  6. Matthew Muturi9,
  7. Athman Mwatondo9,
  8. Jeanette Dawa1,
  9. Limbaso Konongoi10,
  10. Samoel Khamadi10,
  11. Erin Clancey11,
  12. Eric Lofgren11,
  13. Eric Osoro1,
  14. Isaac Ngere1,
  15. Robert F Breiman5,
  16. Barnabas Bakamutumaho4,
  17. Allan Muruta12,
  18. John Gachohi1,
  19. Samuel O Oyola13,
  20. M Kariuki Njenga1,11,
  21. Deepti Singh11
  1. 1Washington State University Global Health Program-Kenya, Nairobi, Kenya
  2. 2University of Nairobi, Faculty of Health Sciences, Nairobi, Kenya
  3. 3Department of Animal Science, Pwani University, Kilifi, Kenya
  4. 4Uganda Virus Research Institute, Entebbe, Uganda
  5. 5Emory University, Atlanta, Georgia, USA
  6. 6Department of Biosecurity, Ecosystems and Veterinary Public Health, Makerere University, Kampala, Uganda
  7. 7African Population and Health Research Center, Nairobi, Kenya
  8. 8Strathmore University, Nairobi, Kenya
  9. 9Kenya Zoonotic Disease Unit, Nairobi, Kenya
  10. 10Kenya Medical Research Institute, Nairobi, Kenya
  11. 11Washington State University, Pullman, Washington, USA
  12. 12Ministry of Health, Kampala, Uganda
  13. 13International Livestock Research Institute, Nairobi, Kenya
  1. Correspondence to Dr M Kariuki Njenga; mkariuki.njenga{at}


Background Recent epidemiology of Rift Valley fever (RVF) disease in Africa suggests growing frequency and expanding geographic range of small disease clusters in regions that previously had not reported the disease. We investigated factors associated with the phenomenon by characterising recent RVF disease events in East Africa.

Methods Data on 100 disease events (2008–2022) from Kenya, Uganda and Tanzania were obtained from public databases and institutions, and modelled against possible geoecological risk factors of occurrence including altitude, soil type, rainfall/precipitation, temperature, normalised difference vegetation index (NDVI), livestock production system, land-use change and long-term climatic variations. Decadal climatic variations between 1980 and 2022 were evaluated for association with the changing disease pattern.

Results Of 100 events, 91% were small RVF clusters with a median of one human (IQR, 1–3) and three livestock cases (IQR, 2–7). These clusters exhibited minimal human mortality (IQR, 0–1), and occurred primarily in highlands (67%), with 35% reported in areas that had never reported RVF disease. Multivariate regression analysis of geoecological variables showed a positive correlation between occurrence and increasing temperature and rainfall. A 1°C increase in temperature and a 1-unit increase in NDVI, one months prior were associated with increased RVF incidence rate ratios of 1.20 (95% CI 1.1, 1.2) and 1.93 (95% CI 1.01, 3.71), respectively. Long-term climatic trends showed a significant decadal increase in annual mean temperature (0.12–0.3°C/decade, p<0.05), associated with decreasing rainfall in arid and semi-arid lowlands but increasing rainfall trends in highlands (p<0.05). These hotter and wetter highlands showed increasing frequency of RVF clusters, accounting for 76% and 43% in Uganda and Kenya, respectively.

Conclusion These findings demonstrate the changing epidemiology of RVF disease. The widening geographic range of disease is associated with climatic variations, with the likely impact of wider dispersal of virus to new areas of endemicity and future epidemics.

  • Viral haemorrhagic fevers
  • Global Health
  • Public Health

Data availability statement

Data are available upon reasonable request. Request for data may be addressed to

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  • Rift Valley fever (RVF) is recognised for its association with heavy rainfall, flooding and El Niño rains in the East African region.

  • A growing body of recent studies has highlighted a shifting landscape of the disease, marked by an expanding geographic range and an increasing number of small RVF clusters.


  • This study challenges previous beliefs about RVF, revealing that it predominantly occurs in small clusters rather than large outbreaks, and its association with El Niño is not as pronounced as previously thought.

  • Over 65% of these clusters are concentrated in the highlands of Kenya and Uganda, with 35% occurring in previously unaffected regions, accompanied by an increase in temperature and total rainfall between 1980 and 2022, along with a rise in the annual number of rainy days.

  • Notably, the observed rainfall increases are particularly significant during the short-rains season (October to December), aligning with a secondary peak in RVF incidence.

  • In contrast, the lowlands of East Africa, where typical RVF epidemics occur, display smaller and more varied trends in annual rainfall.


  • The worldwide consequence of the expanding RVF cluster is the broader dispersion of the virus, leading to the establishment of new regions with virus endemicity.

  • This escalation heightens the risk of more extensive extreme-weather-associated RVF epidemics in the future.

  • Global public health institutions must persist in developing preparedness and response strategies for such scenarios.

  • This involves the creation and approval of human RVF vaccines and therapeutics, coupled with a rapid distribution plan through regional banks.


Rift Valley fever (RVF) virus, a mosquito-borne phlebovirus, is known for extreme weather-associated large epidemics characterised by abortions and perinatal mortalities in livestock; and fever, jaundice, encephalitis, retinitis and haemorrhagic syndrome in humans.1 2 First isolated in Kenya in 1931, the virus caused sporadic localised clusters of RVF disease during the rainy season until 1951 when a larger epidemic spread the virus to approximately 33% of the country.3 Since the 1960s Kenya has reported >10 major RVF epidemics associated with El Niño/Southern Oscillation (ENSO) heavy rainfall, including the two most extensive regional epidemics (Somalia, Kenya, Tanzania) in 1997–1998 and 2006–2007 that attracted the involvement of international partners including WHO as public health events of international concern.4 From the 1970s, periodic major epidemics have been reported in the eastern (Kenya, Somalia, Tanzania), southern (Zimbabwe, South Africa, Madagascar, Mayotte), northern (Egypt, Tunisia) and western (Senegal, Mali, Niger, Mauritania) regions of Africa, almost all associated with flooding conditions.4 In 2000, the virus crossed over to the Middle East, through livestock trade, causing a major epidemic in Saudi Arabia and Yemen that resulted in over 3500 confirmed human cases and >200 fatalities.5 6

Recurrent RVF epidemics occur in certain ecosystems where the virus is endemic, following the convergence of several factors including the presence of mosquito vectors, adequate density of naïve livestock and extreme weather conditions that cause excessive flooding to trigger large-scale vector populations.7 In East Africa, such permissive ecologies, which were responsible for up to 90% of human cases during epidemics, were characterised by low-elevation plains (<1000 m above sea level) with slow-draining clay soil, and dense bushes or acacia vegetations.8 RVF epidemics can also occur in new areas, triggered by massive livestock movement as observed in Africa to Middle East livestock trade during annual Muslim Hajj, or political unrest resulting in large-scale displacement of communities.9 10

Once the virus is introduced in a country or region, it becomes endemic through maintenance by Aedes mosquitoes via transovarial transmission.11 12 The virus can also be maintained through continuous low-level cycling in livestock, herbivore wildlife, humans and mosquitoes.13 14 During such cryptic cycles, a small number of infected mosquitoes routinely emerge to infect animals and humans; however, this cycling does not reach the epidemic threshold, either because the infected vector population is small and rapidly dies off, or a second vector responsible for the horizontal transmissions that occur in epidemics is not present in sufficient numbers.15 16

There is a growing global concern that changing climate conditions and increasing extreme weather events will transform the landscape of infectious diseases by expanding permissive ecologies for pathogens, dispersing pathogens to new regions and bringing humans closer to pathogens through land use changes.17 Recent RVF studies pointed to an expanding geographic range and scope of the disease, likely associated with increasing rainfall intensity and or warming temperatures that perhaps broaden ecological areas supportive of virus maintenance.18 For example, a modelling study conducted in 2017–2018 in the greater horn of Africa (Ethiopia, Kenya, Somalia, Uganda and Tanzania) using data from epidemics, small RVF clusters (≤20 human cases) and seroprevalence findings identified a broader set of risk factors of RVF occurrence, including new soil types (nitisols, vertisols), both low and high elevation areas and highly variable temperatures during the driest months.13 In addition, Uganda, which was never involved in severe RVF epidemic occurring in East Africa, started reporting an increasing number of RVF outbreaks from 2016, and significant disease seroprevalence primarily in the highlands of western and central regions of the country.19 20 To gain an understanding of the changing RVF epidemiology, we investigated the geoecological and climatic factors associated with the spatiotemporal occurrence of small RVF clusters in Kenya, Uganda and Tanzania between 2008 and 2022.


Study area

The study was conducted in the East Africa region (Kenya, Uganda and Tanzania) which is characterised by a diverse ecosystem ranging from agriculturally productive highlands to semiarid and arid lands inhabited by nomadic pastoralists.21 Regional climatic conditions are heterogeneous, with Kenya, Uganda and northeastern Tanzania experiencing bimodal rainfall seasons with long rains occurring from March to May and short rains from October to December.22 In contrast, central and southern Tanzania experience unimodal rainfall from November to May.22 Most of the region experiences substantial rainfall levels in at least one season except the northern and eastern parts of Kenya.22 Temperatures in most areas range from a moderate 15°C–25°C except in coastal belts characterised by humidity and higher temperatures.22 In the arid and semiarid regions of northern Kenya and Uganda, dry and hotter conditions prevail.22

Collection of RVF disease data

For this study, an RVF disease event was defined as a report of suspected RVF cases in either humans or livestock, confirmed by the detection of viral RNA, or antiviral IgM antibodies within a specific location and within a duration of 21 days. The study used presence-only data for disease events and classified each event as either a small cluster if reporting ≤20 confirmed cases (human and animal), or an outbreak characterised by >20 confirmed human and animal cases. This classification was based on our review of RVF disease events reported during inter-epidemic periods in Africa and designed to exclude large outbreaks typically associated with extreme weather conditions and flooding from the analysis.

We collected RVF disease data spanning from 2008 to 2022 in Kenya and Tanzania, and from 2016 to 2022 in Uganda. The data were sourced from Ministries of Health and Agriculture databases, national research institutions, and both published and unpublished reports.3 19 20 23–38 The search strategy for disease events employed precise search strings incorporating keywords like “Rift Valley Fever” “RVF”, “East Africa”, “Kenya”, “Tanzania”, “Uganda”, “IgM” and “PCR”. We used Boolean operators “AND” and “OR” to logically combine these terms, focusing on identifying relevant literature on RVF outbreaks/cases in East Africa. For each RVF disease event, exact geo-coordinates, or geo-coordinates of the area district centroid, start and end date, animal and human involvement, human fatalities, previous history of RVF occurrence, human and livestock population and densities, land and climatic factors were recorded.

Collection of risk factor data

Data on risk factors such as livestock production systems, land use changes, excessive rainfall and the presence of significant water accumulation were extracted from disease surveillance reports, online databases and peer-reviewed publications.3 30 32 35 Information on proximity to wildlife conservations, soil types and elevation level was extracted from MODIS satellite images39 and Food and Agriculture Organisation (FAO) databases.40 Soil types were categorised based on their physical and chemical properties using the FAO scheme.40 Furthermore, data on animal and human-related factors, including predominant livestock species, livestock and human population densities were gathered.41–45 Population and density data for both humans and livestock covering the period from 2008 to 2022 were obtained from the National census data of Kenya,41 Uganda,42 Tanzania43 44 and the World Population databases.45

Short-term and long-term climate data

Short-term climatic variables included rainfall, temperature, humidity and normalised difference vegetation index (NDVI) data from 3 years and 3 months prior to disease event e obtained from Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS),46 Climate Hazards Group InfraRed Temperature with Station data47 and FAO databases.40 The study further examined long-term trends in daily mean temperature and precipitation metrics over the past four decades (1981–2022), using high-resolution data of 0.05°, specifically daily precipitation data and monthly temperature data from the (CHIRPS)46 and Climate Research Unit Gridded Time-Series (CRU TS version 4.07).48 49 These data sets were employed to quantify seasonal and annual total rainfall, determine the annual number of dry days each (days with precipitation ≤1 mm/day) and calculate annual and seasonal mean temperatures.

Data analysis

For risk factors analysis, we assessed the impact of 45 variables associated with demographic, climatic, and anthropogenic predictors, soil types, altitude, distance to wildlife conservation, predominant livestock species, livestock production system, monthly (up to 3 months prior), and annual (up to 3 years prior) NDVI, temperature, rainfall and humidity (online supplemental table 1).50 51 Using total number of RVF cases (both human and livestock) as the dependent variable, we conducted negative binomial regression analysis to model the number of RVF cases (livestock and human combined) with an offset term (total host density minus number of hosts per km2) and reported as incidence rate ratios (IRRs). For continuous variables with missing values, imputation was performed using a single imputation method.52 To address collinearity, we conducted pairwise correlations among predictor variables, identifying high correlations among several of them particularly those describing rainfall, temperature and humidity, as well as intercorrelations between temperature, rainfall, humidity and NDVI. To mitigate multicollinearity, the model included only first-month rainfall in the outbreak year and third-month temperature in the outbreak year. Certain variables that had missing values and those not statistically significant in univariable analysis were dropped. A total of 6 predictor variables with a significance level of p≤0.05 (country, temperature, two NDVI variables), and those historically known to be associated with RVFV activity (humidity and soil type), were included in the final multivariable regression model. This model was constructed using a forward variable selection approach with a p-value threshold of ≤0.05. The final model was refined to include variables without significant collinearity, as indicated by a variance inflation factor ≤5 and the lowest Akaike Information Criterion (AIC).

Supplemental material

For long-term climate data analysis, daily gridded precipitation data from CHIRPS and monthly gridded temperature data from the CRU TS (version 4.07) were used for trend analysis. Spatial patterns of long-term trends were visualised using Python packages: namely matplotlib, geocat, xarray and scipy. Linear trends and their associated p values were determined through the linear least-squares regression function (linregress) within the scipy package. The significance of trends was assessed using the Wald test, and maps were employed to display significance levels for p values <0.1. It is important to highlight that all climate data analyses and visualisations were performed using the specified Python packages, and the significance threshold for p values was set at 0.05. We also plotted temporal climate trends against the number of small RVF clusters.


Characteristics and spatial distribution of small RVF clusters

We collected data on 100 RVF disease events in East Africa from 2008 through 2022. Of the 100 events, 44 were reported in Uganda, 41 in Kenya and 15 in Tanzania. Among these, 91 were classified as small clusters, characterised by a median of 3 human cases (IQR, 1–3) and 3 livestock cases (IQR, 1–7), with minimal human mortality (IQR, 0–1) as detailed in table 1. 35% of the clusters occurred in regions that had never reported RVF cases before, spanning a broader range of altitudes as low as 77 m to as high as 2867 m above sea level, with a median altitude of 1224 m. In Uganda, 93% of the clusters were situated in altitudes ≥1000 m, with 48% (17/35) in Kenya, and 25% (3/12) in Tanzania (table 1, figure 1). Geographically, 43% (15/35) of the small RVF clusters in Kenya were concentrated in the southwestern highlands, while 75% (33/44) in Uganda occurred in the southcentral highlands (figure 1). The southcentral Kenya and southwestern Uganda regions are agriculturally productive highlands, characterised by intensive and semi-intensive livestock production systems. Overall, small clusters occurred across the entire spectrum of livestock production systems, including intensive, semi-intensive and extensive, whereas RVF epidemics were primarily reported in dry areas with extensive livestock production systems. The majority (65%) of the soils where the small RVF clusters occurred were clayey, specifically nitisols, planosols and ferralsols soil types, followed by loamy (18%), and sandy soil types (15%).

Figure 1

Location where small Rift Valley fever clusters reported in East Africa between 2008 and 2022.

Table 1

Characteristics of small RVF clusters in East Africa, 2008–2022

A positive NDVI change 1 month prior to RVF occurrence was observed in 51% of the small clusters. The small RVF clusters were situated in areas with notable land use changes, particularly those leading to significant water accumulation such as rice farming, mining and irrigation. In areas with water pools, either as a result of natural or man-made conditions, 72% of the small RVF clusters occurred. On average 28% of the small RVF clusters were in areas with man-made pools resulting from construction of ponds or dams, swamps, irrigation schemes and excavated mines.

Seasonality and short-term geoecological determinants

When plotted by month of occurrence, the small RVF clusters demonstrated a higher frequency (75/91, 82%) during and immediately after the wet seasons as shown in figure 2. In univariable analysis, a history of previous RVF occurrence, the presence of significant water pools, various types of land use changes, both long-term and short-term temperature variations, and increases in NDVI changes were associated with an elevated IRR with p values of ≤0.05 (online supplemental table 2). Results from the multivariate analysis are presented in table 2. The multivariate model in online supplemental table 3 exhibited a higher AIC indicating a poorer fit compared with the model presented in table 2. Temperature and NDVI demonstrated a significant positive association with the occurrence of small RVF clusters while soil types and elevation were not found to be associated with the changes in IRR. A 1-unit positive change in NDVI a month prior to the disease occurrence, or 10 C temperature rise in the third month prior to occurrence were associated with disease IRR of 1.93 (95% CI 1.01, 3.71) and 1.20 (95% CI 1.1, 1.2), respectively as shown in table 2.

Supplemental material

Supplemental material

Table 2

Multivariate analysis of potential drivers of RVF clusters in East Africa, 2008–2022

Figure 2

Seasonal distribution of Rift Valley fever (RVF clusters reported in East Africa between 2008 and 2022.

Long-term climatic trends in East Africa, 1981–2022

There is substantial spatial heterogeneity in climatological temperature (figure 3A–D) and rainfall patterns (figure 3E–H) across East Africa, partly associated with the topographic complexity of the region (climatology is calculated as the 30-year average over the current normal period 1991–2020 as defined by the World Meteorological Organisation). Annual mean temperatures show substantial warming across the entire region, with larger trends (calculated over 1981–2022) across most of Kenya than in Uganda and Tanzania (figure 4A). We analysed the spatial patterns of trends during the long (March to May) and short (October toDecember) rains seasons; 82.4% of the RVF cluster occurred during and after these seasons. Temperature trends during the long rains varied between 0.12 (oC/decade) in southwestern Uganda and 0.3 (oC/decade) in northern Kenya (figure 3B). This warming trend is more widespread and amplified across all three countries during the short rainy seasons (figure 3D). Trends in rainfall are mixed, showing increasing wetness in southwestern Uganda, western Kenya and most of Tanzania but increasing dryness in northwestern Uganda and the expansive low-elevation eastern Kenya region stretching from the coastal south to the arid and semi-arid regions north during the long rainy seasons (figure 3E,F). In contrast, the short rains seasons experienced stronger and more widespread wetting trends across Kenya, Uganda and northwestern Tanzania but strong decreasing trends across southern Tanzania (figure 3G,H).

Figure 3

Seasonal temperature and rainfall patterns in Kenya, Uganda and Tanzania.

Figure 4

Effect of annual and seasonal temperature and rainfall on occurrence of Rift Valley fever incidences.

Figure 3A,C depicts the long-term climatology (average over 1991–2020) and figure 3B,D illustrates decadal trends (calculated from 1981 to 2022) of mean temperature during the two main rainy seasons—long rains (March to May) and short rains (October to December) seasons over East Africa. Figure 3E–H shows the same as above but for total precipitation. Black dots in figure 3B,D,F and figure 3H represent areas with trends that are significant at the 10% level based on the Wald test.

Annual precipitation trends reflected the patterns of trends in seasonal rainfall, showing increased wetness across southern and eastern Uganda, western Kenya and Tanzania (figure 4B). Another important climate variability parameter is trends in annual number of dry days (<1 mm of rain/day). There has been a decreasing trend in the number of dry days (ie, trend towards more rainy days) across most of Uganda, while the number of dry days is increasing across much of Kenya and parts of Tanzania (figure 4C). Parts of Kenya with a high number of clusters experienced dry day increases of ~3–6 days/decade while parts of Uganda with a high number of clusters experienced dry day decreases of 3–4.5 days/decade, despite increasing trends in annual rainfall (figure 4B,C).

Long-term climate variability in areas with a high density of RVF clusters

Because of limited number of RVF clusters from Tanzania, we use the data from Kenya and Uganda to assess the impact of climatic variability on incidence of RVF clusters. The assessment compared annual and seasonal mean temperature and rainfall trends in two regions; area A in southwestern Uganda where 76% of the RVF clusters of the country occurred (figure 4A—turquoise box), and area B in southwestern Kenya where 30% of in-country clusters occurred (figure 4A—blue box). Time-series of temperature and total rainfall during the long-rains (March to May), intervening dry seasons (June to August) and short-rains (October to December) in the RVF cluster area A and area B were compared (figure 4D–K).

The two areas with a high concentration of RVF cluster areas (figure 4A, area A and area B) in Uganda and Kenya experienced significant warming trends (p<0.05) across all seasons and annually over the past four decades. Area B in Kenya experienced slightly stronger warming than area A (figure 4D–G). For example, during the short-rain seasons, temperature in area A increased at 0.16°C/decade while in area B temperature increased at 0.21°C/decade (figure 4G). Relative to other areas in Uganda, parts of area A experienced weaker annual warming while annual trends in area B were similar in magnitude to trends across other parts of Kenya (figure 4A). The consistent 2°C–3°C higher temperature in Uganda across all seasons (figure 4D–G) is not unique to high concentration RVF cluster sites but rather reflects the normal climatological difference across the two regions.

Annual total rainfall over the high concentrations RVF cluster areas showed increasing trends in area B in Kenya being approximately 1.7 times higher than in area A (figure 4H), although trends are not statistically significant. Similarly, although not significant, rainfall over the area B in Kenya showed a faster increasing trend relative to area A during the dry seasons and short rains seasons but not in the long-rains seasons (figure 4I–K). Trends in the short-rain seasons in area B were >2.5 times higher than in area A.

Top panels decadal trends (calculated from 1981 to 2022) in (figure 4A) annual temperature, (figure 4B) annual rainfall and (figure 4C) annual number of dry days (days with <1 mm/day) overlayed with the RVF clusters (yellow dots) in Kenya, Uganda and Tanzania. Black dots in figure 4A–C represent trends that are significant at the 10% level based on the Wald test. The turquoise and blue boxes in figure 4A indicate RVF clusters in Uganda and Kenya, respectively. Time-series of area-average, mean temperature (figure 4D–G) and rainfall (figure 4H–K) in the RVF cluster area A (Uganda) and area B (Kenya) calculated annual and for the long-rains, dry and short-rains seasons. Numbers in each panel represent the decadal trend magnitude (in °C/decade in figure 4D–G and mm/decade in figure 4H–K and p values of the linear trend in brackets for the corresponding time-series.

A time-series plots of number of RVF clusters in the two hotspots; area A in Uganda (figure 5—top panel) and area B in Kenya (figure 5—bottom panel) during the long-rain seasons showed increasing trend as temperature and rainfall increased in these highland regions.

Figure 5

Increasing frequency of Rift Valley fever (RVF) clusters in two highland area of Kenya and Uganda associated with increasing precipitation and temperature.


We performed spatiotemporal modelling involving various geoecological and short-term climatic factors associated with the increasing occurrence of small RVF clusters reported across widening geographic regions in East Africa in recent years. In addition, we analysed long-term temperature and precipitation metrics trends in the region over four decades (1981–2022) and evaluated the association of these trends with hotspots of RVF clusters in Kenya and Uganda. The described RVF clusters are characterised by <5 human cases and <10 livestock cases. Notably, 35% of these clusters occurred in geographic areas that had not previously reported RVF disease. The small RVF clusters occurred during or immediately after rainy seasons, highlighting the critical role that mosquito vectors continue to play in the dissemination of the RVF virus. Comparable trends of small RVF clusters have been documented in various African countries in recent years.53 54 In contrast to the large epidemics associated with extreme weather and primarily occurring in areas of low elevation, specific soil types and extensive livestock production, the small RVF clusters occurred in diverse ecosystems. These included both highlands and lowlands with varied soil types and encompassing the entire spectrum of livestock production systems (intensive, semi-intensive and extensive). Notably, a substantial proportion of clusters in Uganda (85%) and Kenya (55%) occurred in the highlands where mixed farming with intensive and semi-intensive livestock production was prevalent.

When we evaluated >40 independent variables for association with RVF occurrence, comparable short-term and long-term climatic factors emerged as primary drivers shaping the evolving landscape of RVF disease. Warmer temperatures and rainfall 1–3 months prior correlated with a higher RVF IRR, with 80% of the clusters occurring during this phase. Factors crucial for extreme weather-associated RVF epidemics like excessive flooding, low elevation, clayey soil types and extensive livestock production systems were not significant for small RVF cluster occurrence. Our regional analysis of long-term climatic trends revealed an annual mean temperature increase of up to 0.12–0.3°C/decade in parts of East Africa, indicating a rise in annual average temperatures of up to 1.2°C over the past 40 years. These climatic variations have directly contributed to escalating frequency and severity of droughts, particularly in the arid and semi-arid lowlands such as northeastern Kenya.55 56 Our findings on climatic trends analysis reveal increasing rainfall trends in scattered highlands of the regions. Importantly, our data indicates that areas in the highlands of southwestern Uganda and Kenya experiencing rising temperature and rainfall trends alongside diverging trends in dry days coincide with significantly increasing frequency of RVF clusters. The disease landscape in Uganda more vividly reflects the impact of climatic variations. Despite not being part of regional RVF epidemics and having no documented significant human RVF clusters for decades, the country has reported over 60 small RVF clusters across the country since 2016.

Our findings suggest that the combination of increasing temperature, increasing rainfall trends; and a decrease in the annual number of dry days is a driving factor behind the escalating public health risk of RVF disease in Uganda. With ongoing global warming, East Africa is projected to undergo changes in temperature and precipitation patterns with continued increases in seasonal and annual mean temperatures, particularly in highland areas. Seasonal and annual mean temperatures are expected to keep rising with the most significant increases occurring in the highland areas.57 Even with a 2°C of global warming aligning with the Paris Climate Agreement goals, annual temperatures in the region may increase by 1.5°C–2°C accompanied by over 40% increase in the number of days with temperature above 35°C.58 In terms of precipitation, the observed wetting during the short rain seasons is projected to persist, accompanied by an increase in heavy rain event whereas the changes in rainfall patterns during the long rain seasons remain uncertain.59 60 The likely impact of these future climatic trends is a heightened frequency of small RVF clusters across the geographic areas described in our study, with a reduced frequency of occurrence for the extreme weather-associated large epidemics in the region’s lowlands.

The global impact of the spreading RVF clusters includes a wider dispersal of the virus leading to the creation of new regions with virus endemicity. This escalation increases the risk of more widespread epidemics or pandemics in the future. Changing climate conditions and drivers of natural climate variability such as ENSO that causes extensive heavy rains and flooding across Africa and other regions can trigger widespread virus amplification and human infections.60 61 This is particularly concerning in rural Africa and Middle East where there are significant populations of livestock to support virus amplification and enhanced human infections through consumption of animal products.60 61 Studies have shown widespread distribution of Aedes mosquito vectors globally, including Europe, Australia and North America.62 It is crucial for global public health institutions to consistently approve human RVF vaccine therapeutics and implement swift distribution plans through regional vaccine banks.63 64 Fortunately, the highly conserved RVF virus genome and minimal antigenic variation observed during epidemics guarantee the high efficacy of vaccines and immunotherapeutics.65 66

The study has limitations. First, reliance on available data introduces potential bias because of the incompleteness of some of the data points and under-reporting in regions with limited health infrastructure. In addition, the presence of potential multicollinearity of variables and the use of presence-only data should be considered when interpreting the data. Conversely, an increase in disease reporting rate over time and in new geographic areas is difficult to disentangle from increases in actual disease prevalence.

While we acknowledge these potential limitations, our analyses suggest distinct ecological and climatic risk factors underlying the changing epidemiology of RVF disease in East Africa, which should be investigated for direct causal relationship. Specifically, our study highlights the increasing frequency of small RVF clusters in previously unaffected areas, associated with a combination of higher temperature and rainfall.

Data availability statement

Data are available upon reasonable request. Request for data may be addressed to

Ethics statements

Patient consent for publication

Ethics approval

Data access, usage and overall study approach were reviewed and approved by Kenyatta National Hospital - University of Nairobi ethical review committee (KNH-UoN ERC P810/10/2022) for Kenya. Uganda data access and use were approved by the Uganda Ministry of Health, while all Tanzania data were obtained from public databases and publications.


We acknowledge the collaborative support from the Ministries of Health and Agriculture in Kenya and Uganda. Additionally, we express our gratitude to the Kenya Medical Research Institute (KEMRI) and the Directorate of Veterinary Services for their essential contributions to this research.


Supplementary materials


  • Handling editor Helen J Surana

  • X @SilviaSituma

  • MKN and DS contributed equally.

  • Contributors SS, LN, MKN and DS: conceptualisation, data curation, formal analysis, writing original draft and writing-review and editing. IN, EOmondi, EC, EL, EOsoro and SOO: methodology, formal analysis, supervision, and writing-review and editing. MMureithi, MMM, MMuturi, AM, LK, SK, IN, RFB, BB, AM: supervision, writing-review and editing. JG: data curation, methodology, formal analysis, supervision and writing-review and editing. JD, JG, DS, MKN: conceptualisation, data curation, methodology, formal analysis, supervision and writing-review and editing. All authors gave final approval for publication and agreed to be held accountable for the work performed in it. MKN is the guarantor.

  • Funding Funding for the project was provided by the US National Institute of Allergy and Infectious Disease/National Institutes of Health (NIAID/NIH), grants number U01AI151799 through the Centre for Research in Emerging Infectious Diseases-East and Central Africa (CREID-ECA).

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  • Competing interests None declared.

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