Spatial and temporal analysis of Rift Valley Fever outbreaks in livestock in Uganda:A retrospective study from 2013 to 2022

Spatial and temporal analysis of Rift Valley Fever outbreaks in livestock in Uganda:A retrospective study from 2013 to 2022 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Spatial and temporal analysis of Rift Valley Fever outbreaks in livestock in Uganda:A retrospective study from 2013 to 2022 Eugene Arinaitwe, David Kalenzi Atuhaire, Emmanuel Hasahya, Gladys Nakanjako Kiggundu, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5128612/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 May, 2025 Read the published version in BMC Veterinary Research → Version 1 posted 8 You are reading this latest preprint version Abstract Background: Rift Valley fever (RVF) is a zoonotic disease caused by the Rift Valley fever virus (RVFV), primarily affecting livestock and transmitted by Aedes mosquitoes. First detected in Kenya in 1930, RVF has since spread across Africa, including Uganda, and to the Arabian Peninsula. Uganda reported its first human cases of RVF in 1968, with sporadic outbreaks continuing since the significant outbreak in 2016, particularly in regions with high livestock populations. Although RVFV was detected in mosquitoes in Uganda as early as 1944, the spatial and temporal distribution of RVF outbreaks has not been thoroughly documented. This study aimed to analyze trends in RVF outbreaks across Uganda from 2013 to 2022 to provide insights for effective control measures. A retrospective study was conducted utilizing archived RVF data from NADDEC, along with rainfall and temperature data from the Uganda Meteorological Centre. Maps were generated using QGIS software to illustrate the spatial distribution of RVF outbreaks. The distribution and trends were analyzed using the R programming language. Results: During the study period, RVF outbreaks were reported in 74.1 % of districts surveyed, representing 27.2 % of all districts nationwide. The overall RVF seropositivity among tested animals was found to be 13.02 % [95% CI: 12.4-13.7%], with bovine exhibiting the highest RVF seropositivity among the commonly raised species, such as cattle, goats and sheep. The year 2017 recorded the highest RVF seropositivity at 19.6 %. Notably, the central region had the highest RVF seropositivity at 17.7 % [95% CI: 15.8-19.7%] while the eastern region recorded the lowest at 4.6 %. Conclusion: This analysis provides crucial insights into the spatial and temporal patterns of RVF outbreaks in Uganda, emphasizing the need for targeted interventions, strengthened surveillance, and interdisciplinary collaboration. Despite significant number of studies on RVF outbreaks and prevalence over recent years, little is known about the virus's maintenance mechanisms in the absence of visible outbreaks. Potential reservoirs, vector dynamics, and environmental factors that facilitate its survival and re-emergence remain poorly characterized. Addressing these gaps is critical to improving early warning systems, guiding targeted surveillance, and implementing effective control measures to mitigate future outbreaks. Rift Valley fever zoonotic disease Livestock Public health Retrospective study Figures Figure 1 Figure 2 Background Rift Valley fever (RVF) is a zoonotic disease caused by the Rift Valley fever virus (RVFV) which affects mainly livestock and other ruminants. The RVFV is mainly transmitted by mosquitoes of the genus Aedes ( 1 ). RVF virus is a Bunyavirus of the genus Phlebovirus and of the family Phenuiviridae . It is a single-stranded RNA virus and its genome has three segments L (large), M (medium), and S (small). The virus mainly targets the brain, liver, and spleen as major sites of viral replication ( 2 ). In ruminants, RVF causes storms of abortions, and high mortality in the young animals, (lambs, kids, and calves). Humans normally get infected with RVF when they come into contact with the body fluids of infected animals ( 2 ). RVF outbreaks are normally triggered by combination of factors such as heavy rains and flooding which favor the breeding of mosquitos, and favorable environmental conditions that facilitate mosquitos to spread the virus ( 3 ). RVF was first detected in the Kenyan Rift Valley in 1930 ( 4 ), and since then the disease has not only been reported to other African countries including Uganda, but also in Saudi Arabia and Yemen ( 5 , 6 ). In Uganda, the first human cases of RVF were recorded in 1968 ( 7 ). Since then, Uganda recorded another outbreak of RVF in 2016 in Kabale District, South Western Uganda, where the disease was confirmed in both livestock and humans ( 8 ). Since 2016 the disease has spread to all regions of the country, causing some sporadic outbreaks ( 6 ). Between the years 2017 to 2020, Uganda reported sporadic RVF outbreaks in both humans and animals across the country with 52 human cases and a case fatality rate in humans of 42%. Most of these cases were from areas with high livestock populations (Central and Western) and the main risk factor was contact with animals ( 8 ). Since the first recorded detection of the RVF virus in mosquitos in Uganda in 1944 (Smithburn strain) ( 9 ) and 1955 (Lunyo strain) ( 10 ), the trends of the occurrence of the disease have never been documented in the literature. Despite many seroprevalence studies conducted in different regions of Uganda, no studies have investigated the spatial and temporal distribution of RVF in the whole country. This study aimed to determine the distribution and trends of Rift Valley fever (RVF) outbreaks in Uganda from 2013 to 2022. Understanding the spatial and temporal distribution of RVF is essential for planning and executing effective control measures, as the dynamics of the epidemic differ across various regions. Identifying areas with a history of RVF outbreaks is fundamental for creating targeted mitigation strategies. Materials and Methods Study design and study population To assess the distribution and trends of RVF outbreaks in Uganda, a retrospective study was conducted using archived RVF surveillance data at National Animal Disease Diagnostics and Epidemiology Centre (NADDEC) over 10 years (2013 to 2022). The study utilized results of livestock serum samples submitted to the NADDEC laboratory, which had been analyzed using ELISA to detect immunoglobulin G against RVFV and archived within NADDEC’s Laboratory Information Management System, known as SILAB. Notably, national RVF surveillance in animals by NADDEC was not established before 2013. Description of the study area Uganda is located in Eastern Africa, and is surrounded by Kenya in the east, South Sudan in the north, the Democratic Republic of Congo in the west, and Rwanda and Tanzania in the south. Uganda is landlocked with no access to the ocean, its borders feature many lakes including Lake Victoria. The northeastern region is characterized by a semi-arid climate. Overall, Uganda has a warm tropical climate with the average temperature ranging from 25 to 29°C (77.0 to 84.2°F). Most areas of Uganda receive an annual rainfall of 1,000 to 1,500 millimetres or 40 to 60 inches. Data source The retrospective data on RVF cases/outbreaks in Uganda from 2013 to 2022 were retrieved from the laboratory reports at NADDEC. We exclusively used data from samples tested for RVF, irrespective of the purpose. These samples were tested for immunoglobulin G (IgG) antibodies against the RVF virus using Enzyme-Linked Immunosorbent Assay (ELISA) method. NADDEC utilizes SILAB, from which the data for this study was extracted. The retrieved data contained information such as the date of sample submission, district (the second administrative unit after the country), sub-county (smaller administrative level after the district), disease, sample type, sampling date, date of sample analysis, animal species, and test results. The historical weather data, including monthly rainfall, and mean and maximum temperatures from 2013 to 2022, was obtained from the Uganda Meteorological Centre. Data analysis After data were retrieved from SILAB, at NADDEC in Excel format, it was summarized, coded, and analyzed. For a thorough examination, the seropositivity of RVF was assessed through a univariate analysis approach, wherein the occurrence of RVF was quantified as the proportion of samples testing positive against the total samples analyzed/tested. Subgroup analyses were conducted for each year spanning from 2013 to 2022, across different districts, regions, and among various species. To determine the association between RVF seropositivity and seasonality, a generalized linear model with logistic regression was employed. The dependent variable was RVF seropositivity (whether or not a livestock sample tested positive for RVF). The primary independent variable of interest was season (wet/dry), with additional covariates including year, species, and region. Variables with a p-value ≤ 0.25 from bivariate analyses using simple logistic regression, and those that were not highly correlated (r < 0.4), were retained for the final model to control for potential confounders. A backward elimination process was applied to select the most important predictors. Each variable in final model was evaluated using the log-likelihood ratio test, with a p-value < 0.05 indicating that the variable was important in the model. The results are presented as adjusted odds ratios (ORs) with 95% confidence intervals (CIs) and p-values. All these analytical procedures were carried out using R software version 4.3.3 ( https://r.en.uptodown.com/windows/download ), a powerful tool known for its versatility in statistical analysis. The glm() function from the stats package in R was used to perform the logistic regression. The car package was used for conducting the log-likelihood ratio test, and the broom package was employed for tidying up the model results and extracting odds ratios, 95% confidence intervals, and p-values. Furthermore, to visually represent the geographic distribution of RVF cases across Uganda, the map was generated using QGIS software ( https://download.qgis.org ), accessed in September 2024, highlighting the specific districts where RVF cases were reported. Results Rift Valley fever seropositivity The study involved data from 10789 animals tested for RVFV, for a period of 10 years (2013–2022). Among the tested animals, 1405 were found to be positive for the RVFV, resulting in an overall RVF seropositivity of 13.0% [95% CI: 12.4–13.7%]. The RVF seropositivity varied among different species, with equine, camelids, and bovine having the highest and ovine and caprine having the lowest values as seen in Table 1 . Table 1 Sero-positivity of Rift Valley fever by species tested at National Animal Diseases Diagnostics and Epidemiology Centre (NADDEC) from 2013 to 2022 Species Number Positive RVF Sero-positivity 95% Lower limit 95% Upper limit Bovine 7205 1260 0.174 0.166 0.184 Caprine 3111 116 0.037 0.031 0.044 Equine 15 4 0.266 0.100 0.543 Ovine 402 18 0.044 0.028 0.069 Camelids 5 1 0.200 0.021 0.743 Others 51 6 0.117 0.053 0.239 “Others” means samples submitted but the animal species not identified Seropositivity of Rift Valley fever per year The provided data presents the seropositivity of RVF over the years 2013 to 2022. Across these years, varying numbers of animals were tested, ranging from as few as 353 in 2016 to as many as 3747 in 2022. The RVF seropositivity also fluctuated over time, with some years exhibiting higher values compared to others. For instance, in 2017, a relatively high RVF seropositivity of 19.6% was recorded, which was anticipated as it followed the 2016 RVF outbreak. In contrast, lower rates were observed in 2013 and 2020, with RVF seropositivity of 9.2% and 2.2% respectively as seen in Table 2 . Table 2 Seropositivity of RVF from 2013 to 2022 Year Number tested RVF positive RVF Sero-positivity 95% Lower limit 95% Upper limit 2013 465 43 0.092 0.069 0.122 2014 0 n/a n/a n/a n/a 2015 110 0 0.000 0.000 0.034 2016 353 55 0.155 0.122 0.197 2017 1301 256 0.196 0.176 0.219 2018 624 84 0.134 0.109 0.163 2019 641 77 0.120 0.097 0.147 2020 1058 24 0.022 0.015 0.034 2021 2490 396 0.159 0.145 0.173 2022 3747 470 0.125 0.115 0.136 n/a means not applicable, either no sampling for RVF was done or it was considered unnecessary, as seen in the year 2014. * Wilson score interval method was used to calculate 95% confidence interval since the observed proportion was 0. Historical cases and seropositivity of RVF by district and region (2013–2022) RVF cases reported per district varied across districts with Kabale, Kiruhura, Lyantonde, and Arua districts presenting the highest number of cases as shown in the map below. Similarly, among the sampled districts (n = 54), 14 of them showed no case, throughout the study period (2013–2022) as seen in Fig. 1 . RVF sero-positivity varies significantly across districts, with notably high levels observed in Kyankwanzi, Kabale, and Bukomansimbi. In contrast, Kazo, Hoima, and Adjumani districts exhibit considerably lower RVF seropositivity. Fourteen ( 14 ) districts had all samples testing negative for RVF for a period of 10 years. The seropositivity of RVF in Uganda shows significant regional variation. The Central region has the highest RVF seropositivity at 17.7% [95% CI: 15.8–19.7%], indicating that nearly one in five animals tested positive for RVF antibodies. The Southwestern region follows with a 14.5% RVF seropositivity. The Western region has a 10.9% RVF seropositivity, while the Northern and Eastern regions have lower RVF seropositivity of 7.3% and 4.6%, respectively. The confidence intervals for these regions reflect variability in the estimates, particularly in the Eastern region due to its smaller sample size as seen in Table 3 . Table 3 RVF sero-positivity by regions of Uganda from 2013 to 2022 Region RVF Sero-positivity (95%CI) Total Bovine Caprine Equine Ovine Camelids Various species Central 0.177 (0.158, 0.197) 0.250 (0.220, 0.280) 0.063 (0.037, 0.101) 0.333 (0.043, 0.777) 0.015 (0.001, 0.080) 0.200 (0.005, 0.716) 0.500 (0.068, 0.932) Southwestern 0.145 (0.136, 0.154) 0.130 (0.073, 0.208) 0.006 (0.000, 0.034) n/a 0.000 (0.000, 0.278) n/a n/a Northern 0.073 (0.062, 0.085) 0.114 (0.088, 0.145) 0.029 (0.019, 0.042) n/a 0.000 (0.000, 0.099) n/a 0.050 (0.006, 0.169) Eastern 0.046 (0.028, 0.076) 0.167 (0.132, 0.207) 0.026 (0.012, 0.048) n/a n/a n/a 0.286 (0.037, 0.709) Western 0.109 (0.091, 0.131) 0.139 (0.129, 0.150) 0.039 (0.027, 0.053) 0.222 (0.028, 0.600) 0.063 (0.026, 0.126) n/a n/a n/a; no samples collected RVF seasonality The data on RVF seasonality reveals distinct trends in RVF seropositivity between the wet and dry seasons. During the wet season, a total of 5418 samples were collected, resulting in 950 positive cases, translating to an RVF seropositivity rate of 17.5% [95% CI: 16.5–18.5%]. Throughout the dry season, 5371 samples were tested, resulting in a lower RVF seropositivity rate of 8.5% [95% CI: 7.8–9.2%] (Table 4 ). Table 4 RVF seropositivity by season Season No. samples Positive Seropositivity 95%CI lower 95%CI upper Wet 5418 950 0.175 0.165 0.185 Dry 5371 455 0.085 0.078 0.092 Association between season and RVF seropositivity Table 5 presents the association between season and RVF seropositivity, adjusted for other variables. The analysis reveals that livestock in the wet season are 2.44 times more likely to test positive for RVF compared to the dry season (95% CI: 2.10, 2.82). The year also shows a slight increase in the likelihood of RVF seropositivity, with a 5% increase per year (95% CI: 1.01, 1.10). In terms of species, goats (caprine) and sheep (ovine) are significantly less likely to test positive for RVF compared to bovines, with odds ratios of 0.21 (95% CI: 0.16–0.26, p < 0.001) and 0.15 (95% CI: 0.08–0.31, p < 0.001), respectively. The analysis revealed no significant difference in RVF seropositivity Camels (95% CI: 0.05, 3.86) and horses (95% CI: 0.31, 3.19) show compared to bovines. However, the small number of horses and camels tested raises concerns about the robustness of these findings, potentially undermining the statistical power regarding their susceptibility to the virus. This limited data could impact the reliability of conclusions about their roles in RVF epidemiology. To better understand the involvement of horses and camels in RVF transmission dynamics, further investigation with larger sample sizes is essential. Regionally, the Central region has a 3.99 times higher likelihood of RVF seropositivity compared to the Southwestern region (95% CI: 2.29, 6.95), while the Western region has a 1.72 times higher likelihood (95% CI: 1.01, 2.96). The Northern and Eastern regions do not show significant differences in RVF seropositivity. Table 5 Analysis of RVF seropositivity by species, Season, Region, and Year (2013–2022) Variable Crude OR (95%CI) P-value Adjusted OR (95%CI) P-value Season Wet 2.29 (2.04, 2.58) < 0.001 2.44 (2.10, 2.82) < 0.001 Dry 1.00 1.00 Year 1.16 (1.11, 1.20) < 0.001 1.05 (1.01, 1.10) 0.038 Species Bovine 1.00 1.00 Camel 1.38 (0.15, 12.36) 0.773 0.43 (0.05 3.86) 0.451 Caprine 0.19 (0.15, 0.24) < 0.001 0.21 (0.16, 0.26) < 0.001 Equine 2.01 (0.64, 6.32) 0.233 0.99 (0.31, 3.19) 0.991 Ovine 0.20 (0.09, 0.41) < 0.001 0.15 (0.08, 0.31) < 0.001 Various species 0.74 (0.31, 1.73) 0.482 0.79 (0.32, 1.93) 0.608 Region Southwestern 1.00 1.00 Central 4.38 (2.55, 7.52) < 0.001 3.99 (2.29, 6.95) < 0.001 Northern 1.09 (0.62, 1.91) 0.762 1.57 (0.86, 2.85) 0.137 Western 2.45 (1.44, 4.14) 0.001 1.72 (1.01, 2.96) 0.048 Eastern 2.04 (1.15, 3.60) 0.015 1.62 (0.90, 2.91) 0.105 *The OR for wet season has been adjusted for species (bovine, equine, ovine, camelids, caprine, and others), year (2013 to 2022), and region (Central, Western, Northern, Southwestern, and Eastern)”. Discussion In this study, we describe the spatio-temporal distribution of Rift Valley fever (RVF) outbreaks in Uganda, using retrospective data. Our findings indicate that RVF was circulating in several districts, including Masindi, Hoima, Kibaale, Arua, and Adjumani, as early as 2013, well before the first reported RVF outbreak in five decades in 2016 ( 9 ). Additionally, a case report documented the infection of an expatriate who fell sick with RVF shortly after returning to the United Kingdom (UK) from Uganda in early 2013 ( 11 ). Research among livestock populations in Uganda has detected antibodies against RVF viruses during non-epidemic periods. These seroprevalence studies suggest that animals can be exposed to the virus without resulting in a full-blown outbreak ( 12 – 14 ). The favorable climatic conditions, including heavy rainfall and flooding events, have been linked to increased risks of RVF outbreaks; however, they also indicate that these conditions facilitate sustained low-level transmission between epidemics ( 15 ). These cases are a trigger that there is potential silent RVF transmission in Uganda. Our study found that all the districts with reported positive cases of RVF before 2016 were geographically distant from Kabale, the location of the first reported outbreak in decades. Whereas the virus may have been introduced into the district through the movement of viremic livestock, as supported by findings in other studies ( 16 , 17 ), it is possible that the RVFV was circulating undetected among communities in Kabale. Our analysis indicates that bovine exhibited the highest RVF seropositivity, while ovine showed significantly lower levels of RVF seropositivity ( 6 , 18 ). Notably, our study also provides new evidence that equines and camelids are susceptible to RVF, with equines showing higher levels of RVF seropositivity than previously reported. This finding challenges earlier assumptions regarding their resistance to the RVF virus infection ( 19 , 20 ). A previous study by Tigoi et al (2020) also documented a substantial infection rate among donkeys with a prevalence of 30.3% ( 21 ). This finding underscores the potential role of donkeys as vectors for the RVF virus, facilitating its transmission across various animal populations and geographic regions. The high prevalence indicates that donkeys may serve as important reservoirs of the virus, contributing to its persistence in the environment and enabling spillover events to livestock and humans during interepidemic periods. Such insights highlight the necessity for targeted surveillance and control measures that consider all potential hosts in efforts to effectively mitigate RVF outbreaks. The high RVF seropositivity observed in these two species, despite small sample sizes, may also suggest localized RVF infections. Our findings align with a study conducted in Egypt and Turkey, which reported the presence of antibodies against the RVF virus in camels, though with varying RVF seropositivity rates ( 22 ).Uganda experienced its first human outbreak of RVF in nearly five decades during 2016 ( 23 ), yet prior to this outbreak, little was known about the disease’s status in the country. Inadequate surveillance of zoonotic diseases is largely attributed to inadequacies in the animal health sector’s surveillance system. As a result, humans often serve as sentinels, with the disease typically confirmed in people before investigations are conducted in animal populations. This is evidenced by the absence of RVF samples tested in 2014, and a limited number submitted in 2015 ( 24 ). Factors such as limited understanding of RVF epidemiology and lack of knowledge among pastoralists regarding how the disease spreads have been implicated as reasons for its continued re-emergence ( 25 ). Our analysis reveals a significant upward trend over time in both the number of sample submissions and requests for RVF testing, with 2022 recording the highest number (n = 3747). This increase suggests improved awareness among animal health workers. The reporting rate of diseases, including RVF, is influenced by several factors, such as farmers’ awareness, veterinarians’ knowledge, farmers’ perceptions and compliance, veterinary coverage in remote areas, and the clinical manifestation of the disease ( 17 ). Additionally, the high RVF seropositivity observed in 2017 may be linked to the significant rainfall (1403.16 mm) that year, known to trigger RVF outbreaks ( 3 ). Following a disease outbreak, public awareness typically increases within the population, but this awareness tends to diminish overtime if no new outbreaks occur ( 17 ). In Uganda, the 2016 RVF outbreak heightened awareness and resulted in increased sample submissions. The high RVF seropositivity observed in 2017 could be as a result of the antibodies from the preceding outbreak in 2016. However, as awareness waned in subsequent years (2018 to 2020), RVF seropositivity also declined. Overall, the trend from 2013 to 2022 showed an increase in RVF seropositive cases, suggesting that the disease persists and spreads, always unnoticed. This is in agreement with a study in Tanzania where an increased trend of RVF cases in ruminants was recorded from 1930 to 2007 ( 26 ). During the COVID lockdown in 2019 and 2020, the reported cases of RVF experienced a notable decline. This decline may be attributed to restrictions on the movement of humans, animals and animal products, which limited sample submissions to the laboratory. Additionally, with livestock farmers staying at home, there is a possibility that farm management practices improved. Enhanced vector control measures, such as spraying livestock regularly and clearing vegetation to improve pastures, seem to have helped reduce vector populations and lowered the spread of the RVF virus. Although these practices are not formally documented, many farmers use them to lower disease risks. This highlights the importance of integrating local knowledge into public health strategies for controlling zoonotic diseases, allowing initiatives to be better tailored to local contexts and improving disease prevention efforts. Our findings indicate that the districts in the Eastern and Northern regions of the country were less affected by the disease and had low RVF seropositivity. In contrast, the disease was primarily concentrated in the cattle corridor districts where there is a high livestock population ( 27 ). However, this pattern does not apply to the Karamoja districts, which also have a high population of animals but limited mosquito breeding habitats due to their arid climate. The scarcity of vectors in Karamoja disrupts the disease transmission dynamics, potentially resulting in fewer RVF outbreaks. Despite this, Karamoja faces significant challenges, such as instability and underreporting of cases, necessitating improved RVF surveillance to assess the disease’s true prevalence. Additionally, livestock movement has been linked to the introduction of RVF virus and other diseases into new areas that were previously free from the disease ( 18 , 28 ). Our results indicate that Kabale district is one of the districts that reported high RVF seropositivity (42.4%). Kabale district is where the first RVF outbreak occurred in 2016 after a multi-decade hiatus ( 9 ). Since then, Kabale district has been recording the highest seroprevalence of RVF at 15.2% in 2021 ( 14 ). The high RVF seropositivity in Kabale district can be attributed to the shift in the ecological conditions of the region. Kabale district had for a long time experienced very cold conditions given its high altitude which often deterred the multiplication of mosquitoes. However, the district has now experienced an alteration of ecological conditions which could have contributed to transmission of RVF into the new environment hence recording the highest number of RVF cases ( 29 ). High human population density and having a larger proportion of land under cultivation are some of the drivers of RVF cases due to their effect on the distribution of hosts and vectors. The high population of people is associated with more farming and more water resources for livestock and people ( 30 ). Kabale is densely populated and this has led to encroachment on swamps and forests, to grow crops and rear animals ( 27 ). Land use change, primarily driven by agricultural expansion and afforestation initiatives involving eucalyptus trees, has emerged as a significant characteristic in the district. This transformation reflects broader trends in land management practices as communities seek to enhance agricultural productivity. These changes have impacted the ecosystem and influenced the dynamics of RVF transmission ( 29 ). Studies have shown that areas with previous RVF outbreaks are five times more likely to experience outbreaks in the future ( 17 , 31 ). Aedes spp. eggs can remain viable for several months, hatching during times of increased rainfall ( 32 ). This, along with the transovarial transmission of the RVF virus in Aedes mosquitoes, likely contributes to the consistently high incidence of RVF cases in Kabale, putting the region at high risk for significant outbreaks in the future. The high RVF seropositivity in Kyankwanzi (64.3%) and Lyantonde (44.9%) districts could be attributed to factors such as temperature, climate, forests, high livestock population, and livestock movement. Such an ecological system has been associated with the occurrence and transmission of RVF ( 3 , 18 ). RVF seropositivity rate in Uganda showed a significant regional variation with the central region having the highest (17.7%), and the eastern with the lowest (4.6%). The central region is located in Uganda's cattle corridor with a high livestock population. Other reasons why the central region reported high RVF seropositivity could be due movement of viremic livestock by traders, a high vector population, a high number of exotic animal breeds that are more susceptible to RVF than the indigenous animals, farming practices, and better disease reporting systems ( 16 ). Our results are consistent with results from a previous study where high RVF seropositivity was reported in southwestern and central regions and low in other regions ( 3 ). The eastern and northern regions have low RVF seropositivity and this could be a result of the animal breeds found in these regions which are known to be resistant to the disease ( 33 ), unlike the other regions which have both exotic and Indigenous livestock ( 27 ). The southwestern and central regions have a high population of livestock ( 27 ). Most of the big abattoirs are in the central region where Kampala city is also located. Traders all over the country bring animals to the city abattoirs and it has been observed in the past that animals meant for slaughter at abattoirs in Kampala end up in farms in districts surrounding Kampala which could increase the transmission rate of RVF in the central region ( 34 ). This has been evidenced with many livestock which are recorded to come to Kampala for slaughter but the numbers of slaughters do not match the animals shipped in for the same. The wet season, occurring from March to May and September to November is characterized by increased rainfall and elevated temperatures that facilitate the proliferation of vectors responsible for RVF transmission. The correlation between these seasonal factors and heightened vector activity underscores the critical role of weather patterns in shaping RVF dynamics. Understanding these relationships is essential for predicting outbreaks and implementing timely vector control measures during periods of increased RVF risk. The results also indicated a significant difference in RVF seropositivity between the wet and dry seasons, underscoring the strong association between RVF prevalence and environmental factors. This suggests that climatic changes, such as fluctuations in rainfall and temperature, play a big role in RVF epidemiology. Understanding these dynamics is vital for predicting outbreaks and developing effective control strategies, particularly in areas with notable environmental variations throughout the year. This information will guide public health workers in controlling future RVF outbreaks. An interesting observation is that RVF seropositivity was also relatively high in July, typically a dry month. This could be explained by shifting seasonal patterns, with some regions experiencing rain in July, in recent years. Additionally, the presence of vectors such as Aedes vexans and Culex poicilipes which do not rely on high rainfall to transmit RVF, may also account for the observed high RVF seropositivity during this otherwise dry month ( 35 ). It is important to note that, the results of this study may be biased due to variations in outbreak reporting rate and the differences in sample submission to NADDEC, across districts. The spatial data utilized in this study may not fully capture all affected areas, especially remote villages, leading to potential under-reporting. The impact of under-reporting and differences in sample submission across regions/districts could introduce bias into our results. The high RVF seropositivity observed in 2017 may also be attributed to the animals that were infected in 2016 since we used IgG ELISA to detect antibodies. This test method does not indicate recent infections but reflects past exposure to the virus. Therefore, the results from this study are likely to represent only a subset of the total number of infected herds and farms in Uganda. Despite these limitations, this study aimed to provide important epidemiological information regarding the spatial and temporal distribution of RVF in Uganda. The information generated from this study will guide policymakers in making evidence-based decisions regarding the prevention and control of RVF. In conclusion, this retrospective analysis provides valuable insights into the spatial and temporal dynamics of RVF outbreaks in Uganda, highlighting the importance of targeted interventions, enhanced surveillance, and interdisciplinary collaboration to effectively manage and mitigate the risks associated with this disease. Despite the significant number of studies on RVF outbreaks and prevalence, little is known about the virus's maintenance mechanisms in the absence of visible outbreaks. Potential reservoirs, vector dynamics, and environmental factors that facilitate its survival and re-emergence remain poorly characterized. Addressing these gaps is critical to improving early warning systems, guiding targeted surveillance, and implementing effective control measures to mitigate future outbreaks. The findings from this study will inform strategies to control future RVF outbreaks in Uganda. Additionally, further extensive epidemiological studies are needed to understand RVFV transmission dynamics, in the country. Declarations Data availability Data is available upon reasonable request from the corresponding author Author Contributions EA: Study conceptualization and design, data acquisition and analysis, writing and reviewing the manuscript. DKA: Writing, editing and reviewing the manuscript. EH: Data analysis, visualization, drafting, and editing the manuscript. GNK, RM, GN, MA, FNM and JE: Writing and reviewing the manuscript. All authors have read and approved this manuscript for publication. Funding This research was partially funded by the Government of Uganda through the Makerere University Research and Innovation Fund (MAK-RIF) (Grant number. MakRIF Round 4, 2022/23 ). Ethical approval Ethical clearance for the study was obtained from the Research Ethics Committee of the College of Veterinary Medicine Animal Resources and Biosecurity (COVAB), Makerere University under REF: SVAR-IACUC/174/2024 . Informed con sent statement Permission to use the data was sought from the office of the Commissioner Animal Health (CAH), Ministry of Agriculture Animal Industry, and Fisheries (MAAIF). Conflict of Interest The authors declare that they have no conflict of interest. Acknowledgement The authors would like to thank the staff of National Animal Disease Diagnostics and Epidemiology Centre (NADDEC), Ministry of Agriculture, Animal Industries, and Fisheries (MAAIF) for their technical support during the acquisition of the laboratory reports. The data provision was instrumental in the successful completion of this research. We wish to thank Kelvin Bwambale for his invaluable help in analyzing the data and interpreting the results. References Lwande OW, Paul GO, Chiyo PI, Ng’ang’a E, Otieno V, Obanda V, et al. Spatio-temporal variation in prevalence of Rift Valley fever: a post-epidemic serum survey in cattle and wildlife in Kenya. Infect Ecol Epidemiol. 2015;5(1):1–8. Kasye M, Teshome D, Abiye A, Eshetu A. Austin Virology and Retro Virology A Review on Rift Valley Fever on Animal , Human Health and its Impact on Live Stock Marketing. Austin Virol Retro Virol. 2016;3(1):1–8. Tumusiime D, Isingoma E, Tashoroora OB, Ndumu DB, Bahati M, Nantima N, et al. Mapping the risk of Rift Valley fever in Uganda using national seroprevalence data from cattle , sheep and goats. 2023;(i):1–20. Available from: http://dx.doi.org/10.1371/journal.pntd.0010482 Nakouné E, Kamgang B, Berthet N, Manirakiza A, Kazanji M. Rift Valley Fever Virus Circulating among Ruminants, Mosquitoes and Humans in the Central African Republic. PLoS Negl Trop Dis. 2016;10(10):1–12. Himeidan YE, Kweka EJ, Mahgoub MM, Amin E, Rayah E, Ouma JO. Recent outbreaks of Rift Valley fever in East Africa and the Middle East. 2014;2(October):1–11. Birungi D, Aceng FL, Bulage L, Nkonwa IH, Mirembe BB, Biribawa C, et al. Sporadic Rift Valley Fever Outbreaks in Humans and Animals in Uganda, October 2017-January 2018. J Environ Public Health. 2021;2021. Henderson BE, Mc Crae AWR, Kirya BG, Ssenkubuge Y, Sempala SDK. Arbovirus epizootics involving man, mosquitoes and vertebrates at lunyo, uganda 1968. Ann Trop Med Parasitol. 1972;66(3):343–55. Nyakarahuka L, Whitmer S, Klena J, Balinandi S, Talundzic E, Tumusiime A, et al. Detection of Sporadic Outbreaks of Rift Valley Fever in Uganda through the National Viral Hemorrhagic Fever Surveillance System , 2017 – 2020. 2023;108(5):995–1002. Shoemaker TR, Nyakarahuka L, Balinandi S, Ojwang J, Tumusiime A, Mulei S, et al. First laboratory-confirmed outbreak of human and animal rift valley fever virus in Uganda in 48 years. Am J Trop Med Hyg. 2019;100(3):659–71. Lumley S, Horton DL, Marston DA, Johnson N, Ellis RJ, Fooks AR, et al. Complete genome sequence of Rift Valley fever virus strain Lunyo. Genome Announc. 2016;4(2). Gibson S, Linthicum KJ, Turell MJ, Anyamba A. Rift Valley fever virus : Movement of infected humans threatens global public health and agriculture. 2022;(029). Nyakarahuka L, Kyondo J, Telford C, Whitesell A, Tumusiime A, Mulei S, et al. A Countrywide Seroepidemiological Survey of Rift Valley Fever in Livestock, Uganda, 2017. Am J Trop Med Hyg. 2023;109(3):548–53. Tumusiime D, Isingoma E, Tashoroora OB, Ndumu DB, Bahati M, Nantima N, et al. Mapping the risk of Rift Valley fever in Uganda using national seroprevalence data from cattle, sheep and goats. PLoS Negl Trop Dis [Internet]. 2023;17(5):1–20. Available from: http://dx.doi.org/10.1371/journal.pntd.0010482 Ndumu DB, Bakamutumaho B, Miller E, Nakayima J, Downing R, Balinandi S, et al. Serological evidence of Rift Valley fever virus infection among domestic ruminant herds in Uganda. 2021;1–9. Tumusiime D, Nijhof AM, Groschup MH, Lutwama J, Roesel K, Bett B. Participatory survey of risk factors and pathways for Rift Valley fever in pastoral and agropastoral communities of Uganda. Prev Vet Med [Internet]. 2023;221(November):106071. Available from: https://doi.org/10.1016/j.prevetmed.2023.106071 Kim Y, Métras R, Dommergues L, Youssouffi C, Combo S, Le Godais G, et al. The role of livestock movements in the spread of rift valley fever virus in animals and humans in Mayotte, 2018–19. PLoS Negl Trop Dis. 2021;15(3):1–19. Pienaar NJ, Thompson PN. Temporal and spatial history of Rift Valley fever in South Africa: 1950 to 2011. Onderstepoort J Vet Res. 2013;80(1):1–13. Umuhoza T, Berkvens D, Gafarasi I, Rukelibuga J, Mushonga B, Biryomumaisho S. Seroprevalence of rift valley fever in cattle along the Akagera-Nyabarongo rivers, Rwanda. J S Afr Vet Assoc. 2017;88(1):1–5. Fischer EAJ, Boender G jan, Nodelijk G, Koeijer AA De, Roermund HJW Van. The transmission potential of Rift Valley fever virus among livestock in the Netherlands : a modelling study. 2013;1–13. Mahmoud HYAH, Ali AO. Epidemiology and serological detection of rift valley fever disease in farm animals in southern egypt. Onderstepoort J Vet Res. 2021;88(1):1–5. Tigoi C, Sang R, Chepkorir E, Orindi B, Arum SO, Mulwa F, et al. High risk for human exposure to rift valley fever virus in communities living along livestock movement routes: A cross-sectional survey in Kenya. PLoS Negl Trop Dis [Internet]. 2020;14(2):1–15. Available from: http://dx.doi.org/10.1371/journal.pntd.0007979 Gür S, Kale M, Erol N, Yapici O, Mamak N, Yavru S. The first serological evidence for Rift Valley fever infection in the camel , goitered gazelle and Anatolian water buffaloes in Turkey. 2017; Shoemaker T, Boulianne C, Vincent MJ, Pezzanite L, Al-Qahtani MM, Al-Mazrou Y, et al. Genetic Analysis of Viruses Associated with Emergence of Rift Valley Fever in Saudi Arabia and Yemen, 2000-01. Vol. 8, Emerging Infectious Diseases •. 2002. World Health Organization (WHO). WHO JEE Report for the Republic of Uganda. 2017; Available from: http://search.ebscohost.com/login.aspx?direct=true&db=buh&AN=10712711&site=ehost-live Ramadhani A, Id I, Ahmed A, Katakweba S, Kicheleri P, Chengula AA, et al. PLOS NEGLECTED TROPICAL DISEASES Knowledge , attitudes and practices on rift valley fever among pastoral and agropastoral communities of Ngorongoro in the rift valley ecosystem , Tanzania , conducted in 2021 / 2022. 2023;1–21. Available from: http://dx.doi.org/10.1371/journal.pntd.0011560 Sindato C, Karimuribo ED, Pfeiffer DU, Mboera LEG, Kivaria F, Dautu G, et al. Spatial and temporal pattern of rift valley fever outbreaks in Tanzania; 1930 to 2007. PLoS One. 2014;9(2). UBOS. National Livestock Census 2021 Main Report. 2024;(March):1–718. Hasahya E, Thakur K, Dione MM, Kerfua SD, Mugezi I, Lee HS. Analysis of patterns of livestock movements in the Cattle Corridor of Uganda for risk-based surveillance of infectious diseases. Front Vet Sci. 2023;10. Mugisha J, Alobo &. Determinants of land management practices in the agricultural highlands of Uganda: A case of Kabale highlands in Western Uganda. Third RUFORUM Bienn Meet 24. 2012;(September):923–38. Redding DW, Tiedt S, Lo Iacono G, Bett B, Jones KE. Spatial, seasonal and climatic predictive models of rift valley fever disease across Africa. Philos Trans R Soc B Biol Sci. 2017;372(1725):1–9. Murithi RM, Munyua P, Ithondeka PM, MacHaria JM, Hightower A, Luman ET, et al. Rift Valley fever in Kenya: History of epizootics and identification of vulnerable districts. Epidemiol Infect. 2011;139(3):372–80. Prasad A, Sreedharan S, Bakthavachalu B, Laxman S. Eggs of the mosquito Aedes aegypti survive desiccation by rewiring their polyamine and lipid metabolism. PLoS Biol [Internet]. 2023;21(10 OCTOBER):1–24. Available from: http://dx.doi.org/10.1371/journal.pbio.3002342 Fao. Preparation of Rift Valley Fever Contingency Plans. 2002;1–67. Available from: http://www.fao.org/docrep/005/Y4140E/Y4140E00.HTM Kagoro-Rugunda J, Lejju GB, Andama JB, Matofari MW, Nalwanga JW. A pilot study on roles and operations of actors in the beef value chain in central and Western Uganda. Int J Dev Sustain [Internet]. 2018;7(7):2063–79. Available from: www.isdsnet.com/ijds Talla C, Diallo D, Dia I, Ba Y, Ndione J andré, Morse AP, et al. Modelling hotspots of the two dominant Rift Valley fever vectors ( Aedes vexans and Culex poicilipes ) in Barkédji , Sénégal. Parasit Vectors [Internet]. 2016;1–10. Available from: http://dx.doi.org/10.1186/s13071-016-1399-3 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 26 May, 2025 Read the published version in BMC Veterinary Research → Version 1 posted Editorial decision: Revision requested 17 Apr, 2025 Reviews received at journal 15 Apr, 2025 Reviewers agreed at journal 04 Apr, 2025 Reviews received at journal 03 Apr, 2025 Reviewers agreed at journal 28 Mar, 2025 Reviewers invited by journal 25 Mar, 2025 Submission checks completed at journal 25 Mar, 2025 First submitted to journal 25 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5128612","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":433850614,"identity":"c2570fc6-3934-4e61-8825-0a1f8401fa0d","order_by":0,"name":"Eugene Arinaitwe","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYFACxgYIzd7AwEyiFp4DRGuBAYkEIrXwzz7c+Jjnl00ev+Qbw88FFTYM/O3dCfjNPpfYbMzbl1YsOTvHWHrGmTQGiTNnN+C35gxjmzRvz+HEDbdzDKR52w4zGEjk4tcif4ax/Tdvz//E/TfPGP8mSosB0BZmnh8HEjdI8JgRZ4vhGcZmybkNyYlAb5RZ85xJ4yHoF7kz7A8/vPljl9jffnjzbZ4KGzn+9l4C3gcBxjYQyWEAInkIKweDPyCC/QGRqkfBKBgFo2CkAQBeXEdW5V0MjwAAAABJRU5ErkJggg==","orcid":"","institution":"National Animal Disease Diagnostics and Epidemiology Centre (NADDEC)","correspondingAuthor":true,"prefix":"","firstName":"Eugene","middleName":"","lastName":"Arinaitwe","suffix":""},{"id":433850616,"identity":"25bd8e25-8664-4884-9494-b253bf3945f9","order_by":1,"name":"David Kalenzi Atuhaire","email":"","orcid":"","institution":"Makerere University, COVAB","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"Kalenzi","lastName":"Atuhaire","suffix":""},{"id":433850618,"identity":"9471d770-9ee5-4365-90dc-03018b3d6226","order_by":2,"name":"Emmanuel Hasahya","email":"","orcid":"","institution":"International Livestock Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Emmanuel","middleName":"","lastName":"Hasahya","suffix":""},{"id":433850621,"identity":"26d8d3bd-9e25-4793-bf5b-d325f007263e","order_by":3,"name":"Gladys Nakanjako Kiggundu","email":"","orcid":"","institution":"National Animal Disease Diagnostics and Epidemiology Centre (NADDEC)","correspondingAuthor":false,"prefix":"","firstName":"Gladys","middleName":"Nakanjako","lastName":"Kiggundu","suffix":""},{"id":433850623,"identity":"e63605c2-458b-4f52-a60c-84b163fd2376","order_by":4,"name":"Robert Mwebe","email":"","orcid":"","institution":"National Animal Disease Diagnostics and Epidemiology Centre (NADDEC)","correspondingAuthor":false,"prefix":"","firstName":"Robert","middleName":"","lastName":"Mwebe","suffix":""},{"id":433850626,"identity":"5b6dd757-7e7d-4420-a27c-48f04ef2ed5c","order_by":5,"name":"Gerald Nizeyimana","email":"","orcid":"","institution":"Makerere University, COVAB","correspondingAuthor":false,"prefix":"","firstName":"Gerald","middleName":"","lastName":"Nizeyimana","suffix":""},{"id":433850628,"identity":"64a47233-9df4-4b4d-9a1e-f62db65524c1","order_by":6,"name":"Mathias Afayoa","email":"","orcid":"","institution":"Makerere University, COVAB","correspondingAuthor":false,"prefix":"","firstName":"Mathias","middleName":"","lastName":"Afayoa","suffix":""},{"id":433850630,"identity":"36c73e4c-c256-4413-8eea-3d75330db105","order_by":7,"name":"Frank Norbert Mwiine","email":"","orcid":"","institution":"Makerere University, COVAB","correspondingAuthor":false,"prefix":"","firstName":"Frank","middleName":"Norbert","lastName":"Mwiine","suffix":""},{"id":433850632,"identity":"13f2508a-a735-4a62-9139-7711a7a4878b","order_by":8,"name":"Joseph Erume","email":"","orcid":"","institution":"Makerere University, COVAB","correspondingAuthor":false,"prefix":"","firstName":"Joseph","middleName":"","lastName":"Erume","suffix":""}],"badges":[],"createdAt":"2024-09-21 12:29:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5128612/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5128612/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12917-025-04825-6","type":"published","date":"2025-05-26T15:57:01+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":79272332,"identity":"6780cccc-87f3-48b0-968c-79568d899868","added_by":"auto","created_at":"2025-03-26 11:30:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":763235,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eMap of Uganda showing the sero-positivity of RVF by district from 2013 to 2022\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5128612/v1/214f566d51bb0880ac6c6403.png"},{"id":79272331,"identity":"0d454750-8e77-4683-9419-aef67b5075fa","added_by":"auto","created_at":"2025-03-26 11:30:07","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":42912,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eAverage Monthly Rainfall and RVF seropositivity patterns for 2013 to 2022\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eRVF seropositivity exhibited two peaks, coinciding with periods of high rainfall. The first peak occurred in April, when the average monthly rainfall was 178 mm and RVF seropositivity reached 31%. The second peak was observed immediately after the October rainfall, with an average of 160 mm, followed by a 29% RVF seropositivity in November.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5128612/v1/73c98d272827d15650662f13.png"},{"id":83782865,"identity":"40ba6360-0c9c-41c5-a045-b97ecac34368","added_by":"auto","created_at":"2025-06-02 16:07:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2087346,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5128612/v1/c5b92ba6-5315-402d-b30c-31d02b1419bd.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eSpatial and temporal analysis of Rift Valley Fever outbreaks in livestock in Uganda:A retrospective study from 2013 to 2022\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eRift Valley fever (RVF) is a zoonotic disease caused by the Rift Valley fever virus (RVFV) which affects mainly livestock and other ruminants. The RVFV is mainly transmitted by mosquitoes of the genus Aedes (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). RVF virus is a \u003cem\u003eBunyavirus\u003c/em\u003e of the genus \u003cem\u003ePhlebovirus\u003c/em\u003e and of the family \u003cem\u003ePhenuiviridae\u003c/em\u003e. It is a single-stranded RNA virus and its genome has three segments L (large), M (medium), and S (small). The virus mainly targets the brain, liver, and spleen as major sites of viral replication (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). In ruminants, RVF causes storms of abortions, and high mortality in the young animals, (lambs, kids, and calves). Humans normally get infected with RVF when they come into contact with the body fluids of infected animals (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). RVF outbreaks are normally triggered by combination of factors such as heavy rains and flooding which favor the breeding of mosquitos, and favorable environmental conditions that facilitate mosquitos to spread the virus (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRVF was first detected in the Kenyan Rift Valley in 1930 (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e), and since then the disease has not only been reported to other African countries including Uganda, but also in Saudi Arabia and Yemen (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). In Uganda, the first human cases of RVF were recorded in 1968 (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Since then, Uganda recorded another outbreak of RVF in 2016 in Kabale District, South Western Uganda, where the disease was confirmed in both livestock and humans (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Since 2016 the disease has spread to all regions of the country, causing some sporadic outbreaks (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Between the years 2017 to 2020, Uganda reported sporadic RVF outbreaks in both humans and animals across the country with 52 human cases and a case fatality rate in humans of 42%. Most of these cases were from areas with high livestock populations (Central and Western) and the main risk factor was contact with animals (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Since the first recorded detection of the RVF virus in mosquitos in Uganda in 1944 (Smithburn strain) (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) and 1955 (Lunyo strain) (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e), the trends of the occurrence of the disease have never been documented in the literature. Despite many seroprevalence studies conducted in different regions of Uganda, no studies have investigated the spatial and temporal distribution of RVF in the whole country.\u003c/p\u003e \u003cp\u003eThis study aimed to determine the distribution and trends of Rift Valley fever (RVF) outbreaks in Uganda from 2013 to 2022. Understanding the spatial and temporal distribution of RVF is essential for planning and executing effective control measures, as the dynamics of the epidemic differ across various regions. Identifying areas with a history of RVF outbreaks is fundamental for creating targeted mitigation strategies.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and study population\u003c/h2\u003e \u003cp\u003eTo assess the distribution and trends of RVF outbreaks in Uganda, a retrospective study was conducted using archived RVF surveillance data at National Animal Disease Diagnostics and Epidemiology Centre (NADDEC) over 10 years (2013 to 2022). The study utilized results of livestock serum samples submitted to the NADDEC laboratory, which had been analyzed using ELISA to detect immunoglobulin G against RVFV and archived within NADDEC\u0026rsquo;s Laboratory Information Management System, known as SILAB. Notably, national RVF surveillance in animals by NADDEC was not established before 2013.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDescription of the study area\u003c/h3\u003e\n\u003cp\u003eUganda is located in Eastern Africa, and is surrounded by Kenya in the east, South Sudan in the north, the Democratic Republic of Congo in the west, and Rwanda and Tanzania in the south. Uganda is landlocked with no access to the ocean, its borders feature many lakes including Lake Victoria. The northeastern region is characterized by a semi-arid climate. Overall, Uganda has a warm tropical climate with the average temperature ranging from 25 to 29\u0026deg;C (77.0 to 84.2\u0026deg;F). Most areas of Uganda receive an annual rainfall of 1,000 to 1,500 millimetres or 40 to 60 inches. \u003cp\u003eData source\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe retrospective data on RVF cases/outbreaks in Uganda from 2013 to 2022 were retrieved from the laboratory reports at NADDEC. We exclusively used data from samples tested for RVF, irrespective of the purpose. These samples were tested for immunoglobulin G (IgG) antibodies against the RVF virus using Enzyme-Linked Immunosorbent Assay (ELISA) method. NADDEC utilizes SILAB, from which the data for this study was extracted. The retrieved data contained information such as the date of sample submission, district (the second administrative unit after the country), sub-county (smaller administrative level after the district), disease, sample type, sampling date, date of sample analysis, animal species, and test results. The historical weather data, including monthly rainfall, and mean and maximum temperatures from 2013 to 2022, was obtained from the Uganda Meteorological Centre.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eAfter data were retrieved from SILAB, at NADDEC in Excel format, it was summarized, coded, and analyzed.\u003c/p\u003e \u003cp\u003eFor a thorough examination, the seropositivity of RVF was assessed through a univariate analysis approach, wherein the occurrence of RVF was quantified as the proportion of samples testing positive against the total samples analyzed/tested. Subgroup analyses were conducted for each year spanning from 2013 to 2022, across different districts, regions, and among various species.\u003c/p\u003e \u003cp\u003eTo determine the association between RVF seropositivity and seasonality, a generalized linear model with logistic regression was employed. The dependent variable was RVF seropositivity (whether or not a livestock sample tested positive for RVF). The primary independent variable of interest was season (wet/dry), with additional covariates including year, species, and region.\u003c/p\u003e \u003cp\u003eVariables with a p-value\u0026thinsp;\u0026le;\u0026thinsp;0.25 from bivariate analyses using simple logistic regression, and those that were not highly correlated (r\u0026thinsp;\u0026lt;\u0026thinsp;0.4), were retained for the final model to control for potential confounders. A backward elimination process was applied to select the most important predictors. Each variable in final model was evaluated using the log-likelihood ratio test, with a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicating that the variable was important in the model. The results are presented as adjusted odds ratios (ORs) with 95% confidence intervals (CIs) and p-values. All these analytical procedures were carried out using R software version 4.3.3 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://r.en.uptodown.com/windows/download\u003c/span\u003e\u003cspan address=\"https://r.en.uptodown.com/windows/download\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), a powerful tool known for its versatility in statistical analysis. The \u003cb\u003eglm()\u003c/b\u003e function from the \u003cb\u003estats\u003c/b\u003e package in R was used to perform the logistic regression. The \u003cb\u003ecar\u003c/b\u003e package was used for conducting the log-likelihood ratio test, and the \u003cb\u003ebroom\u003c/b\u003e package was employed for tidying up the model results and extracting odds ratios, 95% confidence intervals, and p-values. Furthermore, to visually represent the geographic distribution of RVF cases across Uganda, the map was generated using QGIS software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://download.qgis.org\u003c/span\u003e\u003cspan address=\"https://download.qgis.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), accessed in September 2024, highlighting the specific districts where RVF cases were reported.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eRift Valley fever seropositivity\u003c/h2\u003e \u003cp\u003eThe study involved data from 10789 animals tested for RVFV, for a period of 10 years (2013\u0026ndash;2022). Among the tested animals, 1405 were found to be positive for the RVFV, resulting in an overall RVF seropositivity of 13.0% [95% CI: 12.4\u0026ndash;13.7%]. The RVF seropositivity varied among different species, with equine, camelids, and bovine having the highest and ovine and caprine having the lowest values as seen in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSero-positivity of Rift Valley fever by species tested at National Animal Diseases Diagnostics and Epidemiology Centre (NADDEC) from 2013 to 2022\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRVF Sero-positivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95% Lower limit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% Upper limit\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBovine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.184\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCaprine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEquine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.543\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOvine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCamelids\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.743\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.239\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;Others\u0026rdquo; means samples submitted but the animal species not identified\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSeropositivity of Rift Valley fever per year\u003c/h2\u003e \u003cp\u003eThe provided data presents the seropositivity of RVF over the years 2013 to 2022. Across these years, varying numbers of animals were tested, ranging from as few as 353 in 2016 to as many as 3747 in 2022. The RVF seropositivity also fluctuated over time, with some years exhibiting higher values compared to others. For instance, in 2017, a relatively high RVF seropositivity of 19.6% was recorded, which was anticipated as it followed the 2016 RVF outbreak. In contrast, lower rates were observed in 2013 and 2020, with RVF seropositivity of 9.2% and 2.2% respectively as seen in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSeropositivity of RVF from 2013 to 2022\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber tested\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRVF positive\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRVF Sero-positivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95% Lower limit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% Upper limit\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e465\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.122\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003en/a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en/a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003en/a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003en/a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.197\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.219\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.163\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e641\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.147\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2490\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.173\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.136\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003en/a means not applicable, either no sampling for RVF was done or it was considered unnecessary, as seen in the year 2014.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003e* Wilson score interval method was used to calculate 95% confidence interval since the observed proportion was 0.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eHistorical cases and seropositivity of RVF by district and region (2013–2022)\u003c/h3\u003e\n\u003cp\u003eRVF cases reported per district varied across districts with Kabale, Kiruhura, Lyantonde, and Arua districts presenting the highest number of cases as shown in the map below. Similarly, among the sampled districts (n\u0026thinsp;=\u0026thinsp;54), 14 of them showed no case, throughout the study period (2013\u0026ndash;2022) as seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRVF sero-positivity varies significantly across districts, with notably high levels observed in Kyankwanzi, Kabale, and Bukomansimbi. In contrast, Kazo, Hoima, and Adjumani districts exhibit considerably lower RVF seropositivity. Fourteen (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) districts had all samples testing negative for RVF for a period of 10 years.\u003c/p\u003e \u003cp\u003eThe seropositivity of RVF in Uganda shows significant regional variation. The Central region has the highest RVF seropositivity at 17.7% [95% CI: 15.8\u0026ndash;19.7%], indicating that nearly one in five animals tested positive for RVF antibodies. The Southwestern region follows with a 14.5% RVF seropositivity. The Western region has a 10.9% RVF seropositivity, while the Northern and Eastern regions have lower RVF seropositivity of 7.3% and 4.6%, respectively. The confidence intervals for these regions reflect variability in the estimates, particularly in the Eastern region due to its smaller sample size as seen in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRVF sero-positivity by regions of Uganda from 2013 to 2022\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c8\" namest=\"c2\"\u003e \u003cp\u003eRVF Sero-positivity (95%CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBovine\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCaprine\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEquine\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOvine\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCamelids\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eVarious species\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.177 (0.158, 0.197)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.250 (0.220, 0.280)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.063 (0.037, 0.101)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.333 (0.043, 0.777)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.015 (0.001, 0.080)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.200 (0.005, 0.716)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.500 (0.068, 0.932)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouthwestern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.145 (0.136, 0.154)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.130 (0.073, 0.208)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.006 (0.000, 0.034)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003en/a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000 (0.000, 0.278)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003en/a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003en/a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorthern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.073 (0.062, 0.085)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.114 (0.088, 0.145)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.029 (0.019, 0.042)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003en/a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000 (0.000, 0.099)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003en/a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.050 (0.006, 0.169)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEastern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.046 (0.028, 0.076)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.167 (0.132, 0.207)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.026 (0.012, 0.048)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003en/a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003en/a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003en/a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.286 (0.037, 0.709)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWestern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.109 (0.091, 0.131)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.139 (0.129, 0.150)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.039 (0.027, 0.053)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.222 (0.028, 0.600)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.063 (0.026, 0.126)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003en/a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003en/a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003en/a; no samples collected\u003c/h3\u003e\n\u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eRVF seasonality\u003c/h2\u003e \u003cp\u003eThe data on RVF seasonality reveals distinct trends in RVF seropositivity between the wet and dry seasons. During the wet season, a total of 5418 samples were collected, resulting in 950 positive cases, translating to an RVF seropositivity rate of 17.5% [95% CI: 16.5\u0026ndash;18.5%]. Throughout the dry season, 5371 samples were tested, resulting in a lower RVF seropositivity rate of 8.5% [95% CI: 7.8\u0026ndash;9.2%] (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRVF seropositivity by season\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeason\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo. samples\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSeropositivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95%CI lower\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95%CI upper\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.185\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eAssociation between season and RVF seropositivity\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents the association between season and RVF seropositivity, adjusted for other variables. The analysis reveals that livestock in the wet season are 2.44 times more likely to test positive for RVF compared to the dry season (95% CI: 2.10, 2.82). The year also shows a slight increase in the likelihood of RVF seropositivity, with a 5% increase per year (95% CI: 1.01, 1.10). In terms of species, goats (caprine) and sheep (ovine) are significantly less likely to test positive for RVF compared to bovines, with odds ratios of 0.21 (95% CI: 0.16\u0026ndash;0.26, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and 0.15 (95% CI: 0.08\u0026ndash;0.31, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), respectively. The analysis revealed no significant difference in RVF seropositivity Camels (95% CI: 0.05, 3.86) and horses (95% CI: 0.31, 3.19) show compared to bovines. However, the small number of horses and camels tested raises concerns about the robustness of these findings, potentially undermining the statistical power regarding their susceptibility to the virus. This limited data could impact the reliability of conclusions about their roles in RVF epidemiology. To better understand the involvement of horses and camels in RVF transmission dynamics, further investigation with larger sample sizes is essential.\u003c/p\u003e \u003cp\u003eRegionally, the Central region has a 3.99 times higher likelihood of RVF seropositivity compared to the Southwestern region (95% CI: 2.29, 6.95), while the Western region has a 1.72 times higher likelihood (95% CI: 1.01, 2.96). The Northern and Eastern regions do not show significant differences in RVF seropositivity.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnalysis of RVF seropositivity by species, Season, Region, and Year (2013\u0026ndash;2022)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCrude OR (95%CI) P-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdjusted OR (95%CI) P-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeason\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.29 (2.04, 2.58)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2.44 (2.10, 2.82)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.16 (1.11, 1.20)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.05 (1.01, 1.10) 0.038\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBovine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCamel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.38 (0.15, 12.36) 0.773\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.43 (0.05 3.86) 0.451\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCaprine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.19 (0.15, 0.24)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.21 (0.16, 0.26)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEquine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.01 (0.64, 6.32) 0.233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.99 (0.31, 3.19) 0.991\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOvine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.20 (0.09, 0.41)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.15 (0.08, 0.31)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVarious species\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.74 (0.31, 1.73) 0.482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.79 (0.32, 1.93) 0.608\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouthwestern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.38 (2.55, 7.52)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e3.99 (2.29, 6.95)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorthern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.09 (0.62, 1.91) 0.762\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.57 (0.86, 2.85) 0.137\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWestern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.45 (1.44, 4.14) 0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.72 (1.01, 2.96) 0.048\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEastern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.04 (1.15, 3.60) 0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.62 (0.90, 2.91) 0.105\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003e*The OR for wet season has been adjusted for species (bovine, equine, ovine, camelids, caprine, and others), year (2013 to 2022), and region (Central, Western, Northern, Southwestern, and Eastern)\u0026rdquo;.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we describe the spatio-temporal distribution of Rift Valley fever (RVF) outbreaks in Uganda, using retrospective data. Our findings indicate that RVF was circulating in several districts, including Masindi, Hoima, Kibaale, Arua, and Adjumani, as early as 2013, well before the first reported RVF outbreak in five decades in 2016 (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Additionally, a case report documented the infection of an expatriate who fell sick with RVF shortly after returning to the United Kingdom (UK) from Uganda in early 2013 (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Research among livestock populations in Uganda has detected antibodies against RVF viruses during non-epidemic periods. These seroprevalence studies suggest that animals can be exposed to the virus without resulting in a full-blown outbreak (\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). The favorable climatic conditions, including heavy rainfall and flooding events, have been linked to increased risks of RVF outbreaks; however, they also indicate that these conditions facilitate sustained low-level transmission between epidemics (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). These cases are a trigger that there is potential silent RVF transmission in Uganda.\u003c/p\u003e \u003cp\u003eOur study found that all the districts with reported positive cases of RVF before 2016 were geographically distant from Kabale, the location of the first reported outbreak in decades. Whereas the virus may have been introduced into the district through the movement of viremic livestock, as supported by findings in other studies (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), it is possible that the RVFV was circulating undetected among communities in Kabale. Our analysis indicates that bovine exhibited the highest RVF seropositivity, while ovine showed significantly lower levels of RVF seropositivity (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Notably, our study also provides new evidence that equines and camelids are susceptible to RVF, with equines showing higher levels of RVF seropositivity than previously reported. This finding challenges earlier assumptions regarding their resistance to the RVF virus infection (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). A previous study by Tigoi et al (2020) also documented a substantial infection rate among donkeys with a prevalence of 30.3% (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). This finding underscores the potential role of donkeys as vectors for the RVF virus, facilitating its transmission across various animal populations and geographic regions. The high prevalence indicates that donkeys may serve as important reservoirs of the virus, contributing to its persistence in the environment and enabling spillover events to livestock and humans during interepidemic periods. Such insights highlight the necessity for targeted surveillance and control measures that consider all potential hosts in efforts to effectively mitigate RVF outbreaks.\u003c/p\u003e \u003cp\u003eThe high RVF seropositivity observed in these two species, despite small sample sizes, may also suggest localized RVF infections. Our findings align with a study conducted in Egypt and Turkey, which reported the presence of antibodies against the RVF virus in camels, though with varying RVF seropositivity rates (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).Uganda experienced its first human outbreak of RVF in nearly five decades during 2016 (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e), yet prior to this outbreak, little was known about the disease\u0026rsquo;s status in the country. Inadequate surveillance of zoonotic diseases is largely attributed to inadequacies in the animal health sector\u0026rsquo;s surveillance system. As a result, humans often serve as sentinels, with the disease typically confirmed in people before investigations are conducted in animal populations. This is evidenced by the absence of RVF samples tested in 2014, and a limited number submitted in 2015 (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Factors such as limited understanding of RVF epidemiology and lack of knowledge among pastoralists regarding how the disease spreads have been implicated as reasons for its continued re-emergence (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur analysis reveals a significant upward trend over time in both the number of sample submissions and requests for RVF testing, with 2022 recording the highest number (n\u0026thinsp;=\u0026thinsp;3747). This increase suggests improved awareness among animal health workers. The reporting rate of diseases, including RVF, is influenced by several factors, such as farmers\u0026rsquo; awareness, veterinarians\u0026rsquo; knowledge, farmers\u0026rsquo; perceptions and compliance, veterinary coverage in remote areas, and the clinical manifestation of the disease (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Additionally, the high RVF seropositivity observed in 2017 may be linked to the significant rainfall (1403.16 mm) that year, known to trigger RVF outbreaks (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFollowing a disease outbreak, public awareness typically increases within the population, but this awareness tends to diminish overtime if no new outbreaks occur (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). In Uganda, the 2016 RVF outbreak heightened awareness and resulted in increased sample submissions. The high RVF seropositivity observed in 2017 could be as a result of the antibodies from the preceding outbreak in 2016. However, as awareness waned in subsequent years (2018 to 2020), RVF seropositivity also declined. Overall, the trend from 2013 to 2022 showed an increase in RVF seropositive cases, suggesting that the disease persists and spreads, always unnoticed. This is in agreement with a study in Tanzania where an increased trend of RVF cases in ruminants was recorded from 1930 to 2007 (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDuring the COVID lockdown in 2019 and 2020, the reported cases of RVF experienced a notable decline. This decline may be attributed to restrictions on the movement of humans, animals and animal products, which limited sample submissions to the laboratory. Additionally, with livestock farmers staying at home, there is a possibility that farm management practices improved. Enhanced vector control measures, such as spraying livestock regularly and clearing vegetation to improve pastures, seem to have helped reduce vector populations and lowered the spread of the RVF virus. Although these practices are not formally documented, many farmers use them to lower disease risks. This highlights the importance of integrating local knowledge into public health strategies for controlling zoonotic diseases, allowing initiatives to be better tailored to local contexts and improving disease prevention efforts. Our findings indicate that the districts in the Eastern and Northern regions of the country were less affected by the disease and had low RVF seropositivity. In contrast, the disease was primarily concentrated in the cattle corridor districts where there is a high livestock population (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). However, this pattern does not apply to the Karamoja districts, which also have a high population of animals but limited mosquito breeding habitats due to their arid climate. The scarcity of vectors in Karamoja disrupts the disease transmission dynamics, potentially resulting in fewer RVF outbreaks. Despite this, Karamoja faces significant challenges, such as instability and underreporting of cases, necessitating improved RVF surveillance to assess the disease\u0026rsquo;s true prevalence. Additionally, livestock movement has been linked to the introduction of RVF virus and other diseases into new areas that were previously free from the disease (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Our results indicate that Kabale district is one of the districts that reported high RVF seropositivity (42.4%). Kabale district is where the first RVF outbreak occurred in 2016 after a multi-decade hiatus (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Since then, Kabale district has been recording the highest seroprevalence of RVF at 15.2% in 2021 (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). The high RVF seropositivity in Kabale district can be attributed to the shift in the ecological conditions of the region. Kabale district had for a long time experienced very cold conditions given its high altitude which often deterred the multiplication of mosquitoes. However, the district has now experienced an alteration of ecological conditions which could have contributed to transmission of RVF into the new environment hence recording the highest number of RVF cases (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). High human population density and having a larger proportion of land under cultivation are some of the drivers of RVF cases due to their effect on the distribution of hosts and vectors. The high population of people is associated with more farming and more water resources for livestock and people (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Kabale is densely populated and this has led to encroachment on swamps and forests, to grow crops and rear animals (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Land use change, primarily driven by agricultural expansion and afforestation initiatives involving eucalyptus trees, has emerged as a significant characteristic in the district. This transformation reflects broader trends in land management practices as communities seek to enhance agricultural productivity. These changes have impacted the ecosystem and influenced the dynamics of RVF transmission (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eStudies have shown that areas with previous RVF outbreaks are five times more likely to experience outbreaks in the future (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). \u003cem\u003eAedes\u003c/em\u003e spp. eggs can remain viable for several months, hatching during times of increased rainfall (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). This, along with the transovarial transmission of the RVF virus in \u003cem\u003eAedes\u003c/em\u003e mosquitoes, likely contributes to the consistently high incidence of RVF cases in Kabale, putting the region at high risk for significant outbreaks in the future.\u003c/p\u003e \u003cp\u003eThe high RVF seropositivity in Kyankwanzi (64.3%) and Lyantonde (44.9%) districts could be attributed to factors such as temperature, climate, forests, high livestock population, and livestock movement. Such an ecological system has been associated with the occurrence and transmission of RVF (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRVF seropositivity rate in Uganda showed a significant regional variation with the central region having the highest (17.7%), and the eastern with the lowest (4.6%). The central region is located in Uganda's cattle corridor with a high livestock population. Other reasons why the central region reported high RVF seropositivity could be due movement of viremic livestock by traders, a high vector population, a high number of exotic animal breeds that are more susceptible to RVF than the indigenous animals, farming practices, and better disease reporting systems (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Our results are consistent with results from a previous study where high RVF seropositivity was reported in southwestern and central regions and low in other regions (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). The eastern and northern regions have low RVF seropositivity and this could be a result of the animal breeds found in these regions which are known to be resistant to the disease (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e), unlike the other regions which have both exotic and Indigenous livestock (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). The southwestern and central regions have a high population of livestock (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Most of the big abattoirs are in the central region where Kampala city is also located. Traders all over the country bring animals to the city abattoirs and it has been observed in the past that animals meant for slaughter at abattoirs in Kampala end up in farms in districts surrounding Kampala which could increase the transmission rate of RVF in the central region (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). This has been evidenced with many livestock which are recorded to come to Kampala for slaughter but the numbers of slaughters do not match the animals shipped in for the same.\u003c/p\u003e \u003cp\u003eThe wet season, occurring from March to May and September to November is characterized by increased rainfall and elevated temperatures that facilitate the proliferation of vectors responsible for RVF transmission. The correlation between these seasonal factors and heightened vector activity underscores the critical role of weather patterns in shaping RVF dynamics. Understanding these relationships is essential for predicting outbreaks and implementing timely vector control measures during periods of increased RVF risk.\u003c/p\u003e \u003cp\u003eThe results also indicated a significant difference in RVF seropositivity between the wet and dry seasons, underscoring the strong association between RVF prevalence and environmental factors. This suggests that climatic changes, such as fluctuations in rainfall and temperature, play a big role in RVF epidemiology. Understanding these dynamics is vital for predicting outbreaks and developing effective control strategies, particularly in areas with notable environmental variations throughout the year. This information will guide public health workers in controlling future RVF outbreaks.\u003c/p\u003e \u003cp\u003eAn interesting observation is that RVF seropositivity was also relatively high in July, typically a dry month. This could be explained by shifting seasonal patterns, with some regions experiencing rain in July, in recent years. Additionally, the presence of vectors such as \u003cem\u003eAedes vexans\u003c/em\u003e and \u003cem\u003eCulex poicilipes\u003c/em\u003e which do not rely on high rainfall to transmit RVF, may also account for the observed high RVF seropositivity during this otherwise dry month (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIt is important to note that, the results of this study may be biased due to variations in outbreak reporting rate and the differences in sample submission to NADDEC, across districts. The spatial data utilized in this study may not fully capture all affected areas, especially remote villages, leading to potential under-reporting. The impact of under-reporting and differences in sample submission across regions/districts could introduce bias into our results. The high RVF seropositivity observed in 2017 may also be attributed to the animals that were infected in 2016 since we used IgG ELISA to detect antibodies. This test method does not indicate recent infections but reflects past exposure to the virus. Therefore, the results from this study are likely to represent only a subset of the total number of infected herds and farms in Uganda. Despite these limitations, this study aimed to provide important epidemiological information regarding the spatial and temporal distribution of RVF in Uganda. The information generated from this study will guide policymakers in making evidence-based decisions regarding the prevention and control of RVF.\u003c/p\u003e \u003cp\u003eIn conclusion, this retrospective analysis provides valuable insights into the spatial and temporal dynamics of RVF outbreaks in Uganda, highlighting the importance of targeted interventions, enhanced surveillance, and interdisciplinary collaboration to effectively manage and mitigate the risks associated with this disease. Despite the significant number of studies on RVF outbreaks and prevalence, little is known about the virus's maintenance mechanisms in the absence of visible outbreaks. Potential reservoirs, vector dynamics, and environmental factors that facilitate its survival and re-emergence remain poorly characterized. Addressing these gaps is critical to improving early warning systems, guiding targeted surveillance, and implementing effective control measures to mitigate future outbreaks. The findings from this study will inform strategies to control future RVF outbreaks in Uganda. Additionally, further extensive epidemiological studies are needed to understand RVFV transmission dynamics, in the country.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData is available upon reasonable request from the corresponding author\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEA: Study conceptualization and design, data acquisition and analysis, writing and reviewing the manuscript. DKA: Writing, editing and reviewing the manuscript. EH: Data analysis, visualization, drafting, and editing the manuscript. GNK, RM, GN, MA, FNM and JE: Writing and reviewing the manuscript. All authors have read and approved this manuscript for publication.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was partially funded by the Government of Uganda through the Makerere University Research and Innovation Fund (MAK-RIF) (Grant number.\u0026nbsp;\u003cstrong\u003eMakRIF Round 4, 2022/23\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical clearance for the study was obtained from the Research Ethics Committee of the College of Veterinary Medicine Animal Resources and Biosecurity (COVAB), Makerere University under \u003cstrong\u003eREF:\u003c/strong\u003e \u003cstrong\u003eSVAR-IACUC/174/2024\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed con\u003c/strong\u003e\u003cstrong\u003esent statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePermission to use the data was sought from the office of the Commissioner Animal Health (CAH), Ministry of Agriculture Animal Industry, and Fisheries (MAAIF).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the staff of National Animal Disease Diagnostics and Epidemiology Centre (NADDEC), Ministry of Agriculture, Animal Industries, and Fisheries (MAAIF) for their technical support during the acquisition of the laboratory reports. The data provision was instrumental in the successful completion of this research. We wish to thank Kelvin Bwambale for his invaluable help in analyzing the data and interpreting the results.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLwande OW, Paul GO, Chiyo PI, Ng\u0026rsquo;ang\u0026rsquo;a E, Otieno V, Obanda V, et al. Spatio-temporal variation in prevalence of Rift Valley fever: a post-epidemic serum survey in cattle and wildlife in Kenya. Infect Ecol Epidemiol. 2015;5(1):1\u0026ndash;8. \u003c/li\u003e\n\u003cli\u003eKasye M, Teshome D, Abiye A, Eshetu A. Austin Virology and Retro Virology A Review on Rift Valley Fever on Animal , Human Health and its Impact on Live Stock Marketing. Austin Virol Retro Virol. 2016;3(1):1\u0026ndash;8. \u003c/li\u003e\n\u003cli\u003eTumusiime D, Isingoma E, Tashoroora OB, Ndumu DB, Bahati M, Nantima N, et al. Mapping the risk of Rift Valley fever in Uganda using national seroprevalence data from cattle , sheep and goats. 2023;(i):1\u0026ndash;20. Available from: http://dx.doi.org/10.1371/journal.pntd.0010482\u003c/li\u003e\n\u003cli\u003eNakoun\u0026eacute; E, Kamgang B, Berthet N, Manirakiza A, Kazanji M. Rift Valley Fever Virus Circulating among Ruminants, Mosquitoes and Humans in the Central African Republic. PLoS Negl Trop Dis. 2016;10(10):1\u0026ndash;12. \u003c/li\u003e\n\u003cli\u003eHimeidan YE, Kweka EJ, Mahgoub MM, Amin E, Rayah E, Ouma JO. Recent outbreaks of Rift Valley fever in East Africa and the Middle East. 2014;2(October):1\u0026ndash;11. \u003c/li\u003e\n\u003cli\u003eBirungi D, Aceng FL, Bulage L, Nkonwa IH, Mirembe BB, Biribawa C, et al. Sporadic Rift Valley Fever Outbreaks in Humans and Animals in Uganda, October 2017-January 2018. J Environ Public Health. 2021;2021. \u003c/li\u003e\n\u003cli\u003eHenderson BE, Mc Crae AWR, Kirya BG, Ssenkubuge Y, Sempala SDK. Arbovirus epizootics involving man, mosquitoes and vertebrates at lunyo, uganda 1968. Ann Trop Med Parasitol. 1972;66(3):343\u0026ndash;55. \u003c/li\u003e\n\u003cli\u003eNyakarahuka L, Whitmer S, Klena J, Balinandi S, Talundzic E, Tumusiime A, et al. Detection of Sporadic Outbreaks of Rift Valley Fever in Uganda through the National Viral Hemorrhagic Fever Surveillance System , 2017 \u0026ndash; 2020. 2023;108(5):995\u0026ndash;1002. \u003c/li\u003e\n\u003cli\u003eShoemaker TR, Nyakarahuka L, Balinandi S, Ojwang J, Tumusiime A, Mulei S, et al. First laboratory-confirmed outbreak of human and animal rift valley fever virus in Uganda in 48 years. Am J Trop Med Hyg. 2019;100(3):659\u0026ndash;71. \u003c/li\u003e\n\u003cli\u003eLumley S, Horton DL, Marston DA, Johnson N, Ellis RJ, Fooks AR, et al. Complete genome sequence of Rift Valley fever virus strain Lunyo. Genome Announc. 2016;4(2). \u003c/li\u003e\n\u003cli\u003eGibson S, Linthicum KJ, Turell MJ, Anyamba A. Rift Valley fever virus : Movement of infected humans threatens global public health and agriculture. 2022;(029). \u003c/li\u003e\n\u003cli\u003eNyakarahuka L, Kyondo J, Telford C, Whitesell A, Tumusiime A, Mulei S, et al. A Countrywide Seroepidemiological Survey of Rift Valley Fever in Livestock, Uganda, 2017. Am J Trop Med Hyg. 2023;109(3):548\u0026ndash;53. \u003c/li\u003e\n\u003cli\u003eTumusiime D, Isingoma E, Tashoroora OB, Ndumu DB, Bahati M, Nantima N, et al. Mapping the risk of Rift Valley fever in Uganda using national seroprevalence data from cattle, sheep and goats. PLoS Negl Trop Dis [Internet]. 2023;17(5):1\u0026ndash;20. Available from: http://dx.doi.org/10.1371/journal.pntd.0010482\u003c/li\u003e\n\u003cli\u003eNdumu DB, Bakamutumaho B, Miller E, Nakayima J, Downing R, Balinandi S, et al. Serological evidence of Rift Valley fever virus infection among domestic ruminant herds in Uganda. 2021;1\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eTumusiime D, Nijhof AM, Groschup MH, Lutwama J, Roesel K, Bett B. Participatory survey of risk factors and pathways for Rift Valley fever in pastoral and agropastoral communities of Uganda. Prev Vet Med [Internet]. 2023;221(November):106071. Available from: https://doi.org/10.1016/j.prevetmed.2023.106071\u003c/li\u003e\n\u003cli\u003eKim Y, M\u0026eacute;tras R, Dommergues L, Youssouffi C, Combo S, Le Godais G, et al. The role of livestock movements in the spread of rift valley fever virus in animals and humans in Mayotte, 2018\u0026ndash;19. PLoS Negl Trop Dis. 2021;15(3):1\u0026ndash;19. \u003c/li\u003e\n\u003cli\u003ePienaar NJ, Thompson PN. Temporal and spatial history of Rift Valley fever in South Africa: 1950 to 2011. Onderstepoort J Vet Res. 2013;80(1):1\u0026ndash;13. \u003c/li\u003e\n\u003cli\u003eUmuhoza T, Berkvens D, Gafarasi I, Rukelibuga J, Mushonga B, Biryomumaisho S. Seroprevalence of rift valley fever in cattle along the Akagera-Nyabarongo rivers, Rwanda. J S Afr Vet Assoc. 2017;88(1):1\u0026ndash;5. \u003c/li\u003e\n\u003cli\u003eFischer EAJ, Boender G jan, Nodelijk G, Koeijer AA De, Roermund HJW Van. The transmission potential of Rift Valley fever virus among livestock in the Netherlands : a modelling study. 2013;1\u0026ndash;13. \u003c/li\u003e\n\u003cli\u003eMahmoud HYAH, Ali AO. Epidemiology and serological detection of rift valley fever disease in farm animals in southern egypt. Onderstepoort J Vet Res. 2021;88(1):1\u0026ndash;5. \u003c/li\u003e\n\u003cli\u003eTigoi C, Sang R, Chepkorir E, Orindi B, Arum SO, Mulwa F, et al. High risk for human exposure to rift valley fever virus in communities living along livestock movement routes: A cross-sectional survey in Kenya. PLoS Negl Trop Dis [Internet]. 2020;14(2):1\u0026ndash;15. Available from: http://dx.doi.org/10.1371/journal.pntd.0007979\u003c/li\u003e\n\u003cli\u003eG\u0026uuml;r S, Kale M, Erol N, Yapici O, Mamak N, Yavru S. The first serological evidence for Rift Valley fever infection in the camel , goitered gazelle and Anatolian water buffaloes in Turkey. 2017; \u003c/li\u003e\n\u003cli\u003eShoemaker T, Boulianne C, Vincent MJ, Pezzanite L, Al-Qahtani MM, Al-Mazrou Y, et al. Genetic Analysis of Viruses Associated with Emergence of Rift Valley Fever in Saudi Arabia and Yemen, 2000-01. Vol. 8, Emerging Infectious Diseases \u0026bull;. 2002. \u003c/li\u003e\n\u003cli\u003eWorld Health Organization (WHO). WHO JEE Report for the Republic of Uganda. 2017; Available from: http://search.ebscohost.com/login.aspx?direct=true\u0026amp;db=buh\u0026amp;AN=10712711\u0026amp;site=ehost-live\u003c/li\u003e\n\u003cli\u003eRamadhani A, Id I, Ahmed A, Katakweba S, Kicheleri P, Chengula AA, et al. PLOS NEGLECTED TROPICAL DISEASES Knowledge , attitudes and practices on rift valley fever among pastoral and agropastoral communities of Ngorongoro in the rift valley ecosystem , Tanzania , conducted in 2021 / 2022. 2023;1\u0026ndash;21. Available from: http://dx.doi.org/10.1371/journal.pntd.0011560\u003c/li\u003e\n\u003cli\u003eSindato C, Karimuribo ED, Pfeiffer DU, Mboera LEG, Kivaria F, Dautu G, et al. Spatial and temporal pattern of rift valley fever outbreaks in Tanzania; 1930 to 2007. PLoS One. 2014;9(2). \u003c/li\u003e\n\u003cli\u003eUBOS. National Livestock Census 2021 Main Report. 2024;(March):1\u0026ndash;718. \u003c/li\u003e\n\u003cli\u003eHasahya E, Thakur K, Dione MM, Kerfua SD, Mugezi I, Lee HS. Analysis of patterns of livestock movements in the Cattle Corridor of Uganda for risk-based surveillance of infectious diseases. Front Vet Sci. 2023;10. \u003c/li\u003e\n\u003cli\u003eMugisha J, Alobo \u0026amp;. Determinants of land management practices in the agricultural highlands of Uganda: A case of Kabale highlands in Western Uganda. Third RUFORUM Bienn Meet 24. 2012;(September):923\u0026ndash;38. \u003c/li\u003e\n\u003cli\u003eRedding DW, Tiedt S, Lo Iacono G, Bett B, Jones KE. Spatial, seasonal and climatic predictive models of rift valley fever disease across Africa. Philos Trans R Soc B Biol Sci. 2017;372(1725):1\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eMurithi RM, Munyua P, Ithondeka PM, MacHaria JM, Hightower A, Luman ET, et al. Rift Valley fever in Kenya: History of epizootics and identification of vulnerable districts. Epidemiol Infect. 2011;139(3):372\u0026ndash;80. \u003c/li\u003e\n\u003cli\u003ePrasad A, Sreedharan S, Bakthavachalu B, Laxman S. Eggs of the mosquito Aedes aegypti survive desiccation by rewiring their polyamine and lipid metabolism. PLoS Biol [Internet]. 2023;21(10 OCTOBER):1\u0026ndash;24. Available from: http://dx.doi.org/10.1371/journal.pbio.3002342\u003c/li\u003e\n\u003cli\u003eFao. Preparation of Rift Valley Fever Contingency Plans. 2002;1\u0026ndash;67. Available from: http://www.fao.org/docrep/005/Y4140E/Y4140E00.HTM\u003c/li\u003e\n\u003cli\u003eKagoro-Rugunda J, Lejju GB, Andama JB, Matofari MW, Nalwanga JW. A pilot study on roles and operations of actors in the beef value chain in central and Western Uganda. Int J Dev Sustain [Internet]. 2018;7(7):2063\u0026ndash;79. Available from: www.isdsnet.com/ijds\u003c/li\u003e\n\u003cli\u003eTalla C, Diallo D, Dia I, Ba Y, Ndione J andr\u0026eacute;, Morse AP, et al. Modelling hotspots of the two dominant Rift Valley fever vectors ( Aedes vexans and Culex poicilipes ) in Bark\u0026eacute;dji , S\u0026eacute;n\u0026eacute;gal. Parasit Vectors [Internet]. 2016;1\u0026ndash;10. Available from: http://dx.doi.org/10.1186/s13071-016-1399-3\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-veterinary-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [BMC Veterinary Research](http://bmcvetres.biomedcentral.com/)","snPcode":"12917","submissionUrl":"https://submission.nature.com/new-submission/12917/3?","title":"BMC Veterinary Research","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Rift Valley fever, zoonotic disease, Livestock, Public health, Retrospective study","lastPublishedDoi":"10.21203/rs.3.rs-5128612/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5128612/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Rift Valley fever (RVF) is a zoonotic disease caused by the Rift Valley fever virus (RVFV), primarily affecting livestock and transmitted by Aedes mosquitoes. First detected in Kenya in 1930, RVF has since spread across Africa, including Uganda, and to the Arabian Peninsula. Uganda reported its first human cases of RVF in 1968, with sporadic outbreaks continuing since the significant outbreak in 2016, particularly in regions with high livestock populations. Although RVFV was detected in mosquitoes in Uganda as early as 1944, the spatial and temporal distribution of RVF outbreaks has not been thoroughly documented. This study aimed to analyze trends in RVF outbreaks across Uganda from 2013 to 2022 to provide insights for effective control measures. A retrospective study was conducted utilizing archived RVF data from NADDEC, along with rainfall and temperature data from the Uganda Meteorological Centre. Maps were generated using QGIS software to illustrate the spatial distribution of RVF outbreaks. The distribution and trends were analyzed using the R programming language.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e During the study period, RVF outbreaks were reported in 74.1 % of districts surveyed, representing 27.2 % of all districts nationwide. The overall RVF seropositivity among tested animals was found to be 13.02 % [95% CI: 12.4-13.7%], with bovine exhibiting the highest RVF seropositivity among the commonly raised species, such as cattle, goats and sheep. The year 2017 recorded the highest RVF seropositivity at 19.6 %. Notably, the central region had the highest RVF seropositivity at 17.7 % [95% CI: 15.8-19.7%] while the eastern region recorded the lowest at 4.6 %.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e This analysis provides crucial insights into the spatial and temporal patterns of RVF outbreaks in Uganda, emphasizing the need for targeted interventions, strengthened surveillance, and interdisciplinary collaboration. Despite significant number of studies on RVF outbreaks and prevalence over recent years, little is known about the virus's maintenance mechanisms in the absence of visible outbreaks. Potential reservoirs, vector dynamics, and environmental factors that facilitate its survival and re-emergence remain poorly characterized. Addressing these gaps is critical to improving early warning systems, guiding targeted surveillance, and implementing effective control measures to mitigate future outbreaks.\u003c/p\u003e","manuscriptTitle":"Spatial and temporal analysis of Rift Valley Fever outbreaks in livestock in Uganda:A retrospective study from 2013 to 2022","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-26 11:30:02","doi":"10.21203/rs.3.rs-5128612/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-18T01:24:17+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-15T13:58:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"227383040453615633453853779139636708898","date":"2025-04-04T06:12:05+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-03T13:12:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"90841461486447408170672740386145115047","date":"2025-03-28T06:03:02+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-25T15:13:12+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-25T08:21:16+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Veterinary Research","date":"2025-03-25T05:22:55+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-veterinary-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [BMC Veterinary Research](http://bmcvetres.biomedcentral.com/)","snPcode":"12917","submissionUrl":"https://submission.nature.com/new-submission/12917/3?","title":"BMC Veterinary Research","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0364a7eb-0262-4047-a90f-38a32068d411","owner":[],"postedDate":"March 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-06-02T16:01:01+00:00","versionOfRecord":{"articleIdentity":"rs-5128612","link":"https://doi.org/10.1186/s12917-025-04825-6","journal":{"identity":"bmc-veterinary-research","isVorOnly":false,"title":"BMC Veterinary Research"},"publishedOn":"2025-05-26 15:57:01","publishedOnDateReadable":"May 26th, 2025"},"versionCreatedAt":"2025-03-26 11:30:02","video":"","vorDoi":"10.1186/s12917-025-04825-6","vorDoiUrl":"https://doi.org/10.1186/s12917-025-04825-6","workflowStages":[]},"version":"v1","identity":"rs-5128612","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5128612","identity":"rs-5128612","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}