Seroprevalence of Peste des Petits Ruminants (PPR) and associated risk factors in selected sub-counties of Lyantonde district, South Western Uganda

Seroprevalence of Peste des Petits Ruminants (PPR) and associated risk factors in selected sub-counties of Lyantonde district, South Western Uganda | 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 Seroprevalence of Peste des Petits Ruminants (PPR) and associated risk factors in selected sub-counties of Lyantonde district, South Western Uganda Edgar Musiime, Kelvin Bwambale, Priscilla Babirye, Paul Lumu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7463240/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Apr, 2026 Read the published version in BMC Veterinary Research → Version 1 posted 12 You are reading this latest preprint version Abstract Background: Lyantonde District in Uganda, a recognized hotspot for Peste des Petits Ruminants (PPR) outbreaks. However, the magnitude and risk factors of PPR in this area remain poorly characterized. This study aimed to determine the prevalence of PPR antibodies in goats and sheep and to identify associated risk factors in selected sub-counties of Lyantonde District. Methods: A cross-sectional serosurvey was conducted in July 2024 in three purposively selected sub-counties (Lyantonde, Lyakajula, and Kinuuka), chosen for their large small ruminant populations, high livestock trade activity, and active animal movement. Farms without a history of PPR vaccination were randomly selected, and simple random sampling was used to select study animals. Whole blood samples were collected from the jugular vein and analyzed using the ID Screen® PPR competitive ELISA kit to detect virus-specific IgG antibodies. Multivariate analysis was performed using Modified Poisson regression to identify risk factors associated with PPR seroprevalence. Adjusted prevalence ratios (aPRs), 95% confidence intervals (CIs), and p-values were reported. Results: A total of 351 goats and sheep were sampled, with an overall apparent seroprevalence of 42.17% (n=148). The Rogan-Gladen estimator gave a true prevalence of 44.27%, with goats showing a significantly higher prevalence (46.27%) compared to sheep (33.62%) (p=0.046). Age was strongly associated with seropositivity: animals aged >1–3 years (aPR=1.86; 95% CI: 1.16–2.97) and >3–5 years (aPR=2.56; 95% CI: 1.54–4.24) had higher risk compared to those <1 year. Goats were more likely than sheep to test positive (aPR=1.59; 95% CI: 1.01–2.53). Farms reporting wildlife interactions had a 43% higher prevalence (aPR=1.43; 95% CI: 1.07–1.91), and acquisition of new animals from other farmers also increased risk (aPR=1.32; 95% CI: 1.02–1.71). Conclusion: PPR transmission in Lyantonde District is driven by host factors, farm-level practices, and livestock-wildlife interactions. Risk-based control strategies, including targeted vaccination in areas with intense animal trade and movement, are essential. Strengthened collaboration between MAAIF and UWA is needed to minimize livestock–wildlife contact, alongside improved farm biosecurity practices. Peste des Petits Ruminants seroprevalence goats sheep risk factors Uganda Figures Figure 1 Back ground Peste des Petits Ruminants (PPR), which is commonly known as Small Ruminant Plague, is an extremely infectious viral disease affecting small ruminants, including sheep and goats. The disease is caused by the Peste des Petits Ruminants virus (PPRV), a member of the Morbillivirus genus belonging to the Paramyxoviridae family ( 1 ). The PPR virus is primarily transmitted through respiratory aerosols and direct contact with infected animals ( 1 ). The disease manifests with a range of clinical signs that include fever, nasal and ocular discharge, coughing, and strenuous breathing. Affected animals may also exhibit oral mucosal lesions and gastrointestinal signs ( 2 ). The disease has the most serious economic impact on communities that depend on small ruminants for sustenance and income ( 3 ). In particular, PPR is a major threat to the livelihoods of small-scale farmers and pastoralists, especially in developing countries. In naïve populations, PPR registers high mortality and morbidity rates of up to 90% and 100% respectively. In addition to reduced fertility, decreased milk and meat production directly affect the overall productivity of small ruminants ( 2 ). Globally an estimated, 2.1 billion US dollars is lost annually due to the effects of PPR ( 4 ). Uganda boasts of approximately 20 million goats and sheep that significantly contribute to the wellbeing of farming communities ( 5 ). Approximately over 40% of households in Uganda keep and depend on goats and sheep, more so in the cattle corridor of the country ( 6 ). About five hundred thousand animals valued at 15 million US Dollars have been lost to PPR since the first outbreak in 2007 ( 5 ). The disease undermines the growth of the goat and sheep-subsector which is targeted for development in the wider drive to achieve the sustainable development goals (SDGs) like ending poverty, zero hunger, good health and wellbeing ( 7 ). In light of the graveness of the threat posed by PPR, global efforts like vaccination campaigns, surveillance programs, and strict quarantine measures have been instituted by international organizations such as the Food and Agriculture Organization (FAO) and the World Organization for Animal Health (WOAH) with the objective to control and eradicate the disease by 2030 through the PPR Global Control and Eradication Strategy ( 8 ). Uganda committed to this strategy and consequently developed the national PPR control and eradication strategy in line with resolutions of international conference in Abidjan of April 2015 to improve among others, surveillance, diagnostic capacity, and mass vaccination ( 5 ). Peste des Petits Ruminants (PPR) remains a significant threat to small ruminant populations globally, causing substantial economic losses and negatively impacting the livelihoods of vulnerable communities ( 4 ). In spite of extensive endeavors to control the disease, several challenges persist, necessitating focused research and interventions ( 9 ). There is fragmented understanding of risk factors and occurrence patterns of PPR in Lyantonde district, which is predicted to have more than 50% probability of having future PPR outbreaks as well as an upsurge in trend of occurrence ( 10 ). Proximity to Lake Mburo wildlife national park and presence of a booming livestock market could be major drivers in PPR disease occurrence. Amidst all these predispositions, the prevalence of PPR and associated risk factors have never been established. The development of effective control strategies is hampered by this limitation and renders the district vulnerable to imminent PPR incursion. Future prospects for controlling of PPR in the district will depend on proper understanding of the disease’s occurrence patterns and associated risk factors, which is largely lacking in Uganda. This study aims to establish the seroprevalence and risk factors of PPR in small ruminants in selected sub-counties of Lyantonde district Methods Study design This was a cross-sectional serosurvey to determine the seroprevalence and risk factors of PPR in small ruminants in three selected sub counties of Lyantonde district which was carried out in July 2024 among farms with no history of vaccination. Study area The study was carried out in Lyantonde district which is located in the South-western region of Uganda. The coordinates of the district are 0.2241° S, 31.2168° E. Lyantonde borders Rakai district in the south, Masaka in the east, Kiruhura in the west and Sembabule in the north. Lyantonde is located in the dry cattle corridor of Uganda with vast pastoral rangelands characterized by semi-arid features, including intermittent low rainfall and protracted drought conditions characterized by water limitation and extreme heat result in shortage of pasture that in turn translates into frequent movement and interaction between different herds of small ruminants as well as wildlife species within the area. This interaction abets close contact and subsequently disease spread ( 11 ). Three sub counties were purposively selected, namely; Lyantonde sub county, Lyakajula subcounty and Kinuuka subcounty as shown in Fig. 1 . Lyantonde sub county is characterized by goats and sheep farms with grazing area closest to Lake Mburo National Park. Lyakajula sub county hosts Kyemamba livestock market, the biggest livestock market in the district with booming goat and sheep trade while Kinuuka sub county has a main route for animal traffic running through. Study population The study population were sheep and goats aged greater than 4 months with no history of vaccination against PPR virus. Goats and sheep production is a significant livelihood activity in the Lyantonde practiced by 37.8% of the households ( 12 ). The farmers interviewed for this study were got from this demographic group. The farms were majorly small to medium holder farms ranging between 15–203 animals in an agro-pastoral setting ( 13 ) with communal grazing, fenced farms and tethering being the predominant production systems employed. Indigenous goat breeds were the most commonly kept which included Mubende, Small East African and Kigezi goats whereas the exotic/cross goat breeds were uncommon and included, Boer, Kalahari red and Savannah. Sheep breeds kept include the East African black head, Masai red and East African long tailed sheep. Crosses among exotic and local breeds are also common and in many farming households, breeding is random. Lyantonde district was selected based on the large goat and sheep population, sizable traffic of animal movement, absence of a current active outbreak and willingness of farmers to participate in this research work. Inclusion and Exclusion criteria In this study, only sheep and goat farms with no history of vaccination against PPRV were included. Sheep and goats younger than 4 months of age and clinically sick small ruminants were excluded from sampling. Sample size Determination and sampling The sample size for goats and sheep for this study was deduced using the formula by ( 14 ). Using 31.7% expected prevalence from a study ( 15 ), 5% desired absolute precision and 95% confidence interval, sample size n was determined to be 333 sheep and goats. The three sub-counties were purposively selected based on their being on high risk of PPR outbreaks given proximity to the game park, active animal market and having an animal trade route traversing the area. Following this selection, a comprehensive list of sheep and goat farms within these sub-counties was obtained from veterinary staff in charge of the respective sub-counties. Then an integrated list of all sheep and goat keeping households was generated. Having set the maximum number of small ruminants to be sampled per farm at 12, 30 farms were randomly drawn from the list using a paper lottery method. Another 30 farms were also randomly selected as standby replacements from which any farm would be drawn if any of the selected farms did not meet the inclusion criteria. At sub county level, proportionate random sampling was employed. A total of 10 farms were drawn from each of the sub counties. At the farm level, the first 11 small ruminants meeting the inclusion criteria to be restrained were sampled. Animal handling and sample Collection Blood samples were collected from live sheep and goats via jugular venipuncture. No anaesthesia or euthanasia was performed. The animals were manually restrained in a standing position, with their backs supported against the handler’s legs. The head was gently turned approximately 30° to expose the jugular vein. The collection site was disinfected with 70% alcohol, and blood was drawn using a sterile 19-gauge needle attached to a 10 ml vacutainer. After collection, firm pressure was applied with sterile cotton wool to stop bleeding. Each procedure involved two personnel, one restraining the animal and the other collecting the blood. All animals remained alive and were returned to normal management immediately after sample collection. The procedure followed established standard operating protocols ( 16 , 17 ). Plain red top 10 ml vacutainer tubes and 19-gauge sterile needles were used to collect 6–10 ml of whole blood from the jugular vein of sheep and goats that had not been previously vaccinated against PPR. To ensure accurate identification of every animal, the samples were labelled immediately. During blood sampling, data on the related risk factors, such as species, age, sex, herd size, and research area, were noted for each animal. Samples of collected blood were stored at room temperature overnight in order to separate the serum. After that, these were briefly kept at the district laboratory at -20 ºC temperature before being transported to the National Animal Disease Diagnostics and Epidemiology Centre (NADDEC) laboratory in Entebbe for processing and laboratory analysis. Data collection A structured questionnaire ( Supplementary file 1 ) was developed specifically for this study to collect information from sheep and goat owners about vaccination status to assist the sampling procedure and other farm characteristics as putative risk factors. At each selected farm, farmers or their representatives were interviewed to assess associated factors of PPR disease, such as species, sex, age, herd size, the likelihood of interaction with other flocks and wildlife, origin of the animals for restocking and breeding, distance from livestock markets, shared watering and grazing areas. The questionnaire was pretested by administering it to three selected farmers outside the sampling pool, one farmer from each of the selected sub-counties for this study. To ensure effective communication, the questionnaire was translated into two local languages, Luganda and Runyankore, and administered in these languages during interviews. Laboratory analysis The ID Screen® PPR (IDvet, 310 rue Louis Pasteur Grabels, France) commercial competitive ELISA kit was used to detect PPR virus specific IgG antibodies in serum as per the manufacturer’s manual. This kit works on the principle of Antigen-Antibody reaction between purified recombinant PPR nucleoprotein (NP) and Anti-NP antibodies if present in serum. The antibody-antigen complex covers the NP epitopes. Briefly, an anti-NP-peroxidase (HRP) is added to the microwells followed by incubation. This fixes to the remaining free NP epitopes forming an antigen-conjugate-HRP complex. Washing is done to remove excess conjugate followed by addition of substrate solution (TMB). The resulting coloration depends on the quality of specific antibodies present in the serum to be tested. In absence of antibodies, a blue solution appears which turns to yellow on addition of stop solution (sulfuric acid). In presence of Antibodies, no coloration appears. The microplate is read at 450nm. For each sample, the competition percentage (S/N%) is calculated from: $$\:S/N\%=\frac{{OD}_{sample}}{{OD}_{NC}}x\:100$$ Samples presenting S/N less than or equal to 50% were considered positive. Values greater than 50% and less than or equal to 60% were considered doubtful. An S/N value greater than 60% was considered negative. Estimation of true prevalence With the sensitivity (99.4%) and specificity (94.5%) values of the ID Screen® PPR (IDvet, 310 rue Louis Pasteur Grabels, France), true prevalence was deduced using the Rogan & Gladen estimator below: $$\:True\:prevalence=\frac{\left(Apparent\:prevalence+Sp-1\right)}{\left(Se+Sp-1\right)}$$ where Se is the test sensitivity and Sp is the test specificity ( 18 ). The Rogan–Gladen estimator method yields negative values if the apparent prevalence is less than the likelihood of discovering a false positive (1-test specificity). Furthermore, the true prevalence estimates will have a percentage greater than 100% if the apparent prevalence exceeds the sensitivity of the diagnostic test. Accordingly, the true prevalence estimates that are returned in both cases are not epidemiologically credible ( 15 ). The predetermined values for specificity (99.4%) and sensitivity (94.5%) provided by the manufacturers of the ELISA test were utilized to estimate the true prevalence Statistical analysis Descriptive statistics were first applied to summarize the data at the univariate level, calculating the mean and standard deviation (SD) for numerical variables such as age and herd size. Frequencies and percentages were utilized for categorical variables, including PPR seroprevalence and animal age categories. To identify risk factors associated with PPR seroprevalence, a modified Poisson regression was employed, as the outcome variable demonstrating a high prevalence, nearing 50%. Variables of species, age, sex, herd size, source of new stock, type of housing and noticing wildlife on farms were selected for multivariate analysis guided by two criteria: a variable was included if it had a p-value of less than 0.25 or if it was deemed to have a plausible association with PPR seroprevalence. To address multicollinearity, Pearson’s correlation coefficient was assessed, and variables with a coefficient greater than 0.4 with others were excluded to ensure the robustness of the model. A stepwise model-building technique was utilized to refine the model, with statistical significance set at a p-value of 0.05. The results of the analysis were systematically presented in tables. Results Demographic characteristics of individual animals Of the 351 small ruminants sampled, 92 (26.21%) were from Kinuuka, 129 (36.75%) from Lyakajula, and 130 (37.04%) were from Lyantonde. Goats constituted the majority of the animals sampled (n = 313; 89.17%) and 38 (10.83%) were sheep. The age of the animals ranged from less than a year to five years, with the mean age being 1.97 years (SD = 1.12) (Table 1 ). Table 1 Demographic characteristics of individual animals Variable Category Number sampled Sub-County Kinuuka 92 (26.21) Lyakajula 129 (36.75) Lyantonde 130 (37.04) Age of the animal Less than 1 year 73 (20.80) 1 year 83 (23.65) > 1–3 years 160 (45.58) > 3–5 years 35 (9.97) Sex Female 270 (76.92) Male 81 (23.08) Species Sheep 38 (10.83) Goat 313 (89.17) Breed Cross 261 (74.36) Local 90 (25.64) Farm and herd management characteristics The herds consisted primarily of goats, with an average herd size of 72 goats (SD = 44), while the number of sheep was much smaller, averaging only 8 (SD = 11) (Table 2 ). Table 2 Farm and herd management characteristics Herd Characteristic Category Frequency (n = 30) Other species present on farm Cattle 27 (90.00) Pigs 8 (26.67) Type of housing Goat Kraal/Boma 19 (63.33) Goat shed 4 (13.33) Raised goat house 6 (20.00) None 1 (3.33) Source of new animals Fellow farmers 13 (43.33) Livestock market 16 (53.33) Received as gifts 1 (3.33) Production system Free range/extensive 29 (96.67) Tethering 1 (3.33) Contact with other herds/flocks No 2 (6.67) Yes 28 (93.34) Share grazing and watering grounds No 4 (13.33) Yes 26 (86.67) Noticed wild life on the farm No 16 (53.33) Yes 14 (46.67) Contact with market bound animals No 11 (36.67) Yes 19 (63.33) Share breeding males No 7 (23.33) Yes 23 (76.67) Nursing kids following mothers No 10 (33.33) Yes 20 (66.67) Socio-demographic characteristics of the respondents The study included 30 farmers evenly distributed across the three sub-counties of Kinuuka, Lyakajula, and Lyantonde, with each sub-county contributing 10 farmers. The mean age of the farmers was 46.63 years (SD = 17.38) (Table 3 ). Table 3 Socio-demographic characteristics of study participants Farmer Characteristic Category Frequency (n = 30) Age category of farmer Below 40 years 12 (40.00) Above 40 years 18 (60.00) Main occupation Farmer 26 (86.67) Other 4 (13.33) Marital status Married 26 (86.67) Single 4 (13.33) Highest level of Education None 2 (6.67) Primary 16 (53.33) Secondary 7 (23.33) Tertiary 5 (16.67) Role on the farm Care taker 3 (10.00) Herdsman 3 (10.00) Manager 2 (6.67) Owner 22 (73.33) Seroprevalence of PPR in small ruminants in selected sub-counties of Lyantonde district, parishes and villages A total of 351 small ruminants were sampled, comprising 313 goats and 38 sheep. Among the goats, 136 tested positive for PPR antibodies, corresponding to an apparent prevalence of 43.5%, with an estimated true prevalence of 46.3% (95% CI: 33.7–61.4). In sheep, 12 of the 38 samples were positive, giving an apparent prevalence of 31.6% and an estimated true prevalence of 33.6% (95% CI: 23.5–47.5). When data were combined across species, the overall apparent prevalence of PPR in the sampled population was 42.2%, with an estimated true prevalence of 44.3% (95% CI: 32.8–60.2) (Table 4 ). Table 4 Seroprevalence of PPR among small ruminants Species Number sampled Number positive Apparent Prevalence Estimated True prevalence (95%CI) Goats 313 136 43.45 46.27 (33.67, 61.35) Sheep 38 12 31.58 33.62 (23.54, 47.51) Overall 351 148 42.17 44.27 (32.82, 60.21) Sub-County Kinuuka 92 38 41.30 43.34 (38.26, 48.42) Lyakajula 129 62 48.06 50.54 (45.39, 55.69) Lyantonde 130 48 36.92 38.68 (33.70, 43.66) Overall 351 148 42.17 44.27 (39.18, 49.36) Parish Biwolobo 13 2 15.38 16.37 (9.14, 25.98) Bwamulamila 9 5 55.56 59.16 (44.91, 76.10) Kalagala 61 28 45.90 48.88 (36.25, 64.78) Kikwamba 15 9 60.00 63.89 (49.28, 81.72) Kirowoozo 31 10 32.26 34.35 (23.54, 47.51) Kyemamba 34 9 26.47 28.18 (18.60, 40.46) Kyewanula 25 8 32.00 34.07 (23.54, 47.51) Lyakajula 41 26 63.41 67.52 (52.80, 86.20) Nakasozi 52 12 23.08 24.57 (16.17, 36.90) Rweera 39 18 46.15 49.14 (36.25, 64.78) Wabusana 31 21 67.74 72.13 (56.33, 90.67) Village Akabale 9 4 44.44 47.33 (34.53, 62.50) Binikila 13 12 92.31 98.30 (79.56, 99.84) Bwamulamila 9 5 55.56 59.16 (44.91, 76.10) Bwihagaju 15 9 60.00 63.89 (49.28, 81.72) Gayaza 14 3 21.43 22.81 (14.58, 34.51) Kamusenene 13 2 15.38 16.38 (9.14, 25.98) Kasambya b 16 9 56.25 59.90 (45.78, 77.23) Katoogo 13 2 15.38 16.38 (9.14, 25.98) Kenshangu 15 2 13.33 14.19 (7.65, 23.48) Kinuuka 15 2 13.33 14.19 (7.65, 23.48) Kyabazala 16 3 18.75 19.96 (12.21, 30.88) Kyaluguguza 7 2 28.57 30.42 (20.24, 42.82) Kyanzala 16 7 43.75 46.59 (34.53, 62.50) Kyemamba 6 5 83.33 88.74 (71.47, 99.52) Kyenvunikide 15 12 80.00 85.19 (67.89, 95.10) Kyetume 15 5 33.33 35.49 (24.37, 48.67) Kyewanula 9 1 11.11 11.83 (6.20, 20.96) Lugalama 10 8 80.00 85.19 (67.89, 95.10) Lyakajula 41 26 63.41 67.53 (52.80, 86.20) Makondo 4 0 0.00 - Mukokoma 15 7 46.67 49.69 (37.11, 65.91) Nkoote b 8 1 12.50 13.31 (6.92, 22.23) Nsheshe 15 2 13.33 14.19 (7.65, 23.48) Rwemikoma 12 5 41.67 44.37 (31.97, 59.06) Rwenyonyozi 30 14 46.67 49.69 (37.11, 65.91) Risk factors associated with PPR occurrence among small ruminants in the selected sub-counties of Lyantonde district Individual animal factors significantly associated with the prevalence of Peste des Petits Ruminants (PPR) included age, and species. Specifically, animals aged > 1–3 years and > 3–5 years were more likely to have PPR antibodies compared to those less than one year (aPR = 1.86; 95% CI: 1.16, 2.97) and (aPR = 2.56; 95% CI: 1.54, 4.24), respectively. Furthermore, goats showed a higher risk of PPR compared to sheep (aPR = 1.59; 95% CI: 1.01, 2.53). Farm characteristics that were significantly associated with PPR included noticing wildlife on the farm, the type of housing, source of new animals, and the practice of nursing kids not following their mothers to grazing. Farms which reported presence of wildlife on the farm showed 43% higher seroprevalence of PPR compared to those that didn’t notice any wild life (aPR = 1.43, 95%CI:1.07, 1.91). Farms that utilized a goat shed as housing type exhibited a reduced risk of PPR (aPR = 0.44; 95% CI: 0.24, 0.77) compared to those with raised goat houses. Additionally, acquiring new animals from fellow farmers increased the likelihood of PPR (aPR = 1.32; 95% CI: 1.02, 1.71). Lastly, not allowing nursing kids to follow their mothers was associated with a higher risk of PPR (aPR = 1.36; 95% CI: 1.04, 1.76) Univariate, bivariate and multivariate analysis of exploratory risk factors is summarized in Table 5 . Table 5 Individual animal and herd characteristics associated with sero prevalence of PPR. Variable PPR Occurrence uPR (95%CI) P-value aPR (95%CI) P-value Positive, n (%) Negative, n (%) Age category Less than 1 year 17 (11.49) 56 (28.08) 1.00 1.00 1 year 28 (18.92) 55 (26.60) 1.48 (0.88, 2.48) 0.131 1.41 (0.84, 2.34) 0.189 > 1–3 years 81 (54.73) 79 (38.92) 2.20 (1.41, 3.43) 0.001 1.86 (1.16, 2.97) 0.009 > 3–5 years 22 (14.86) 13 (6.40) 2.73 (1.67, 4.46) < 0.001 2.56 (1.54, 4.24) < 0.001 Sex Female 127 (85.81) 143 (70.44) 1.81 (1.22, 2.67) 0.003 1.32 (0.91, 1.92) 0.148 Male 21 (14.191) 60 (29.56) 1.00 1.00 Species Sheep 12 (8.11) 26 (12.81) 1.00 1.00 Goat 136 (91.89) 177 (87.19) 1.38 (0.84, 2.23) 0.198 1.59 (1.01, 2.53) 0.046 Breed Cross 110 (74.32) 151 (74.38) 1.00 1.00 Local 38 (25.68) 52 (25.62) 1.01 (0.75, 1.33) 0.990 1.03 (0.78, 1.35) 0.840 Noticed wildlife on the farm No 96 (64.86) 104 (51.23) 1.00 1.00 Yes 52 (35.14) 99 (48.77) 1.39 (1.07, 1.81) 0.014 1.43 (1.07, 1.91) 0.015 Source of new animals Fellow farmers 67 (45.27) 89 (43.84) 1.08 (0.84, 1.39) 0.543 1.32 (1.02, 1.71) 0.029 Livestock market 71 (47.97) 108 (53.20) 1.00 1.00 Received as gifts 10 (6.76) 6 (2.96) 1.58 (1.03, 2.40) 0.034 1.12 (0.68, 1.86) 0.642 Nursing kids following mothers No 62 (41.89) 63 (31.03) 1.30 (1.02, 1.66) 0.033 1.36 (1.04, 1.76) 0.02 Yes 86 (58.11) 140 (68.97) 1.00 1.00 Discussion This study set out to assess the seroprevalence of Peste des Petits Ruminants (PPR) antibodies and identify associated risk factors among small ruminants in Lyantonde district, Uganda. The results revealed a high overall estimated “true” prevalence of 44.27%, with variability across the selected sub-counties. The high seroprevalence obtained by this study is evidence of the high exposure of goats and sheep to PPRV. The highest seroprevalence was recorded in Lyakajula (50.54%), followed by Kinuuka (43.34%), and the lowest in Lyantonde (38.68%). These findings highlight the general understanding that PPR is endemic and remains a significant ever-looming threat in sub-Saharan Africa, but also highlight important local variations in exposure and risk. This state of persistent endemicity is recipe for imminent PPR outbreaks in Lyantonde and spread to previously unaffected areas ( 10 ). The observed overall high prevalence of 44.27% is consistent with other studies conducted in Uganda and across Africa. For example, a study conducted in Nakapiripit in Uganda ( 13 ) found PPR seroprevalence at 44.1%, 60% in the Afar region in Ethiopia ( 19 ), and 62% in South Sudan ( 20 ). However, the prevalence in Lyantonde district is notably higher than figures reported in other African settings, such as in the Oromia region in Ethiopia at 10% ( 21 ). This variation might be due to climatic differences between the localities, variation in the production system, local transmission dynamics, and proportion of the sample size. The seroprevalence of Peste des Petits Ruminants (PPR) in the different sub-counties can be closely linked to their geographical proximity to significant locations like livestock markets and wildlife habitats. In Lyakajjula sub-county, which hosts the Kyemamba livestock market, the highest seroprevalence of 50.54% was recorded. This elevated rate may be attributed to the high density of livestock trading in this area, facilitating the movement of potentially infected animals and increasing exposure to the PPR virus. A seroprevalence of 43.34% was documented in Kinuuka subcounty that has significant livestock trade routes between sub counties of Lyakajula and Lyantonde. A similar study ( 10 ) found a significant correlation between increase in road network, increased livestock numbers and the occurrence of PPR. The study also highlights how decrease in annual precipitation translates into increased movement of animals over long distances in search for pastures and water. A situation like this increases the possibility of contact between infected and naïve flock, which could aid in PPRV transmission ( 22 ). A seroprevalence of 38.68% was recorded in Lyantonde subcounty that is closest to Lake Mburo National Park. This is quite similar to the findings that reported seropositivity of 35% and 13% at two points of wildlife-livestock interface ( 23 ). Mahapatra et al., 2015 reports cross infection of PPRV between domestic small ruminants and wild species like Impala and Grant’s gazelles ( 24 ). The higher risk of PPR in farms reporting frequent wildlife sightings supports the hypothesis that wild animals may serve as a reservoir for PPR, contributing to transmission to domestic small ruminants. A study conducted at the wild life-livestock interface in the Northern Albertine Rift Valley and Nile Basin in East Africa indicates an epidemiological linkage between epizootic cycles in livestock and exposure in wildlife, denoting the importance of PPR surveillance on wild artiodactyls for both conservation and eradication programs ( 25 ). Also, similar studies in Tanzania and Kenya (in the Greater Serengeti eco-system) have pointed to the potential role of wild ungulates, which can carry the virus without showing clinical signs, thereby acting as silent vectors for the disease ( 26 ). The finding that goats were more likely to have PPR antibodies than sheep echoes similar results from studies in Ethiopia ( 21 ) and Sudan ( 27 ), which consistently report higher PPR exposure in goats than sheep. Another study ( 1 ) alludes to the possibility that PPRV prefers goat over sheep when both are reared together. A study ( 19 ) deduced that a higher seroprevalence in goats than in sheep was associated with higher fertility in goats than sheep. This study cannot rule out the possibility that the difference in seropositivity between goats and sheep could be due to the difference in the husbandry practices and sampled proportions in both species. Most studies ( 28 – 30 ) note that goats are affected more severely by PPR virus exposure compared to sheep, and they exhibit easily noticed clinical signs while sheep undergo a mild form of the disease. On the contrary, some studies have identified sheep as being more susceptible to PPR infection, for example in Kassala state Sudan the apparent prevalence to be 68.1% in sheep and 43.5% in goats ( 31 ). In the Punjab province of Pakistan, the seroprevalence of PPR was in sheep was 56.80% versus 48.24% in goats ( 32 ). Nonetheless, some studies ( 33 ) have not identified such clear differences between species, suggesting that local factors such as husbandry practices, population and sample structure, and ecological settings and geospatial systems may influence species susceptibility. Older animals (> 1–3 years and > 3–5 years) were found to be at higher risk of having PPR antibodies compared to those under one year of age. This finding aligns with findings from several studies, such as in Uganda ( 15 ) and Sudan ( 21 ). This is likely due to cumulative exposure to the virus over time. As older animals are more likely to have encountered infected animals or contaminated environments, the likelihood of seroconversion increases with age. Additionally, very young animals may benefit from maternal immunity, temporarily protecting them from infection, further explaining the lower seroprevalence in younger age groups ( 15 ). The significant association between acquiring new animals from fellow farmers and increased PPR risk highlights the role of informal animal trade in disease spread. This finding aligns with findings where the introduction of new animals into the flock without following appropriate trading or animal exchange protocol is a risk factor for PPR transmission ( 21 ). When farmers engage in unregulated exchanges without adequate biosecurity measures, such as quarantine or health screenings, they risk introducing infected animals into previously unexposed herds. This is particularly alarming for diseases like Peste des Petits Ruminants, which can spread rapidly among susceptible populations. The absence of such protocols allows asymptomatic carriers to enter healthy herds, leading to outbreaks that can have devastating economic impacts on farmers and threaten food security ( 5 ). The higher seroprevalence of PPR in farms that do not allow nursing kids to follow their dams raises intriguing questions about the protective mechanisms at play. While the exact reasons for this association remains unclear, one plausible explanation is that all-day nursing behavior may provide young animals with protective immunity through milk, which is rich in antibodies ( 34 ). Despite its strengths, this study had several limitations. First, the cross-sectional design limits the ability to establish causal relationships between the identified risk factors and PPR seroprevalence. While associations were observed, it remains unclear whether the risk factors directly contributed to increased infection rates or if they are proxies for other unmeasured factors, such as the presence of other infectious diseases or broader ecological changes. Longitudinal studies are needed to establish more robust cause-and-effect relationships. The sheep population on the selected farms limited sample size for sheep in this study. This could have a bearing on the statistical validity and inference to the larger sheep population. More comprehensive and varied sheep samples should be used in future studies to validate these findings and improve their relevance to actual farming situations. The study relied on antibody detection assay rather than viral isolation, meaning it only captured past exposure to PPR and not active infection per se. As a result, the true prevalence of current infections could be lower than the reported figures. Additionally, antibody levels can persist for extended periods, and the study does not differentiate between recent and historic exposures. To gain a clearer picture of ongoing transmission dynamics, future studies should include viral detection methods such as RT-PCR alongside serology. Conclusion This study demonstrated a high prevalence of PPR antibodies among small ruminants in Lyantonde District, with notable variation across sub-counties, and identified several animals- and farm-level risk factors for infection. Older animals, goats compared to sheep, farms reporting wildlife presence, and specific management practices such as sourcing new animals and the handling of kids or lambs were strongly associated with PPR seropositivity. These findings highlight the urgent need for risk-based control measures, including extensive vaccination in areas of high livestock trade and movement, strengthened collaboration between MAAIF and UWA to minimize livestock–wildlife interactions, and enhanced farm-level biosecurity practices such as quarantining new animals and improving kid and lamb management, in order to effectively mitigate PPR transmission and safeguard small ruminant production in the district. Abbreviations aPR Apparent prevalence OD PC Optical Density of Positive Control PPRV: Peste des Petits Ruminants Virus RNA Ribonucleic acid UBOS Uganda Bureau of Statistics VNT Virus Neutralization Test WOAH World Organization for Animal Health Declarations Ethical Consideration and consent to participate Ethical approval for this study was obtained from the School of Veterinary Medicine and Animal Resources Research and Ethics Committee, Makerere University (Approval No. SVAR/216/2024). Administrative permission to conduct the study was also granted by the Lyantonde District Veterinary Office (DVO). All animal procedures were performed by veterinary personnel licensed by the Uganda Veterinary Board (UVB) in accordance with the OIE Terrestrial Animal Health Code and the ARRIVE guidelines for animal research. For the human component, written informed consent was obtained from all participating farmers prior to animal sampling and interviews. Participation was entirely voluntary, and all data were anonymized and treated with strict confidentiality. The study adhered to the ethical principles outlined in the Declaration of Helsinki. Consent for Publication Not applicable. Competing Interests The authors declare no competing interests. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Author Contribution EM conceptualized the study, collected data, performed analysis, interpreted the results, and drafted the manuscript. FM and DRM supervised the study, contributed to conceptualization and methodology, and critically reviewed the manuscript. KB contributed to data analysis and critically reviewed the manuscript draft. PB and PL contributed to data collection, data analysis, and reviewed manuscript drafts. All authors read and approved the final manuscript. Acknowledgement My sincere thanks go to the veterinary officers, livestock farmers, and local authorities in the selected sub-counties of Lyantonde District for their cooperation and participation in this study. Their willingness to share their experiences and knowledge made this research possible. Data Availability The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable written request ( [email protected] ). 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Paul Lule KR and HK. Peste des petits ruminants (PPR) in systems and coordination Peste des petits ruminants (PPR) in Uganda Assessment of animal health systems and coordination. 2022;(March). Byaruhanga C, Oluka J, Olinga S. Socio-economic Aspects of Goat Production in a Rural Agro-pastoral System of Uganda. Univers J Agric Res. 2015;3(6):203–10. Mehrabi Z, Gill M, van Wijk M, Herrero M, Ramankutty N. Livestock policy for sustainable development. Nat Food. 2020;1(3):160–5. Zhao H, Njeumi F, Parida S, Benfield CTO. Progress towards eradication of peste des petits ruminants through vaccination. Viruses. 2021;13(1):1–15. Akwongo CJ, Quan M, Byaruhanga C, Prevalence. Risk Factors for Exposure, and Socio-Economic Impact of Peste Des Petits Ruminants in Karenga District, Karamoja Region, Uganda. 2022. Nkamwesiga J, Korennoy F, Lumu P, Nsamba P, Mwiine FN, Roesel K, et al. Spatio-temporal cluster analysis and transmission drivers for Peste des Petits Ruminants in Uganda. Transbound Emerg Dis. 2022;69(5):e1642–58. 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;10:1095293. UBOS. National Livestock Census. 2024;(March):136–42. Nkamwesiga J, Lumu P, Nalumenya DP, Korennoy F, Roesel K, Wieland B et al. Seroprevalence and risk factors of Peste des petits ruminants in different production systems in Uganda. Prev Vet Med [Internet]. 2023;221(January 2024):106051. Available from: https://doi.org/10.1016/j.prevetmed.2023.106051 Thrushfield M. Veterinary Epidemiology. 3rd ed. Singapore: Blackwell Science; 2007. p. 276. Nkamwesiga J, Lumu P, Nalumenya DP, Korennoy F, Roesel K, Wieland B et al. Seroprevalence and risk factors of Peste des petits ruminants in different production systems in Uganda. Prev Vet Med [Internet]. 2023;221(September):106051. Available from: https://doi.org/10.1016/j.prevetmed.2023.106051 Virginia Tech IACUC. SOP: Blood Collection in Sheep. 2017. Robertson S. AEC-SOP-9.04-Collection- of-blood-small-ruminants-SOP013 . 2023;22–4. ROGAN, WJ, GLADEN B. ESTIMATING PREVALENCE FROM THE RESULTS. OF A SCREENING TEST. Am J Epidemiol. 1978;107(1):71–6. Dubie T, Dagnew B, Gelo E, Negash W, Hussein F, Woldehana M. Seroprevalence and associated risk factors of peste des petits ruminants among ovine and caprine in selected districts of Afar region, Ethiopia. BMC Vet Res. 2022;18(1):429. Abdela N. Sero-prevalence, risk factors and distribution of foot and mouth disease in Ethiopia. Acta Trop. 2017;169:125–32. Ejigu E, Tolosa T, Begna F, Tegegne H. Sero-Prevalence and Associated Risk Factors of Peste Des Petits Ruminants in Dera and Gerar Jarso Districts of Oromia Region, Ethiopia. Vet Med Res Rep. 2023;14(July):111–23. Herzog C. Transmission Dynamics of the Multi-Host Pathogen Peste des Petits Ruminants Virus Among Sheep, Goats, and Cattle in Northern Tanzania. 2020. Rahman AKMA, Islam SS, Sufian MA, Talukder MH, Ward MP, Martínez-López B. Peste des Petits Ruminants Risk Factors and Space-Time Clusters in Bangladesh. Front Vet Sci. 2021;7(January):1–11. Parida S, Muniraju M, Mahapatra M, Muthuchelvan D, Buczkowski H, Banyard AC. Peste des petits ruminants. Vet Microbiol. 2015;181(1):90–106. Fernandez Aguilar X, Mahapatra M, Begovoeva M, Kalema-Zikusoka G, Driciru M, Ayebazibwe C et al. Peste des Petits Ruminants at the Wildlife-Livestock Interface in the Northern Albertine Rift and Nile Basin, East Africa. Viruses. 2020;12(3). Jones BA, Mahapatra M, Mdetele D, Keyyu J, Gakuya F, Eblate E et al. Peste des Petits Ruminants Virus Infection at the Wildlife-Livestock Interface in the Greater Serengeti Ecosystem, 2015–2019. Viruses. 2021;13(5). Ali SEM, Ahmed YAM, Osman AA, Gamal Eldin OA, Osman NA. Prevalence of peste des petits ruminants virus antibodies in sheep and goats sera from Central-Western Sudan. Onderstepoort J Vet Res. 2023;90(1):e1–8. Delil A, Asfaw A, Gebreegziabher B. Prevalence of antibodies to peste des petits ruminants virus before and during outbreaks of the disease in Awash Fentale district, Afar. Ethiop Trop Anim Heal Prod. 2012;44(7):1329–30. Kardjadj M, Kouidri B, Metref D, Luka PD, Ben-Mahdi MH. Seroprevalence, distribution and risk factor for peste des petits ruminants (PPR) in Algeria. Prev Vet Med [Internet]. 2015;122(1–2):205–10. Available from: http://dx.doi.org/10.1016/j.prevetmed.2015.09.002 Sakhare P, Kalyani I, Vihol P, Sharma K. Seroepidemiology of Peste Des Petits Ruminants (PPR) in Sheep and Goats of Southern Districts of Gujarat, India. 2019;8(11):1552–65. Saeed FA, Abdel-Aziz SA, Gumaa MM. Seroprevalence and Associated Risk Factors of Peste des Petits Ruminants among Sheep and Goats in Kassala State, Sudan. Open J Anim Sci. 2018;08(04):381–95. Khan HA, Siddique M, Abubakar M, Arshad MJ, Hussain M. Prevalence and distribution of peste des petits ruminants virus infection in small ruminants. Small Rumin Res. 2008;79(2–3):152–7. Gebre T, Deneke Y, Begna F. Seroprevalence and Associated Risk Factors of Peste Des Petits Ruminants (PPR) in Sheep and Goats in Four Districts of Bench Maji and Kafa Zones. South West Ethiopia. 2018;20(6):260–70. Bailey AS. Importance of Colostrum for Lambs and Kids. 2019. Additional Declarations No competing interests reported. Supplementary Files SupplementaryfileI.docx Cite Share Download PDF Status: Published Journal Publication published 13 Apr, 2026 Read the published version in BMC Veterinary Research → Version 1 posted Editorial decision: Revision requested 12 Nov, 2025 Reviews received at journal 12 Nov, 2025 Reviewers agreed at journal 20 Oct, 2025 Reviews received at journal 30 Sep, 2025 Reviews received at journal 24 Sep, 2025 Reviewers agreed at journal 13 Sep, 2025 Reviewers agreed at journal 11 Sep, 2025 Reviewers invited by journal 11 Sep, 2025 Editor assigned by journal 11 Sep, 2025 Editor invited by journal 11 Sep, 2025 Submission checks completed at journal 10 Sep, 2025 First submitted to journal 10 Sep, 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. 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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-7463240","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":514562071,"identity":"243579d3-2471-4afe-ba2a-e4f38481bd53","order_by":0,"name":"Edgar Musiime","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+ElEQVRIiWNgGAWjYFCCBAjFxgwiK4CYmbmBFC1nQFoYidQCBoxtYBK/Fn729IufK3MO2/Oxcyc+LpxXG83fDtTyo2IbTi2SPW+KJc9uO5zYxsy72XjmtuO5Mw4zNjD2nLmNU4vBjZwEycZthxPYmHm3SfNuO5bbANTCzNiGW4v9jZzkn0At9kAt23/zzjmWO5+QFgOJ9GMgWxiBDtvGzNtQk7uBkBaJM2/YLBu3pYP9Is1z7EDuRqCWg/j8wt+e/vhm4zZre/n+sxs/89TU5c47f/jggx8VuLUwMPAYIPMOg8kDeNQDAfsDZF4dfsWjYBSMglEwIgEAxiVamgWPePwAAAAASUVORK5CYII=","orcid":"","institution":"Lyantonde District Local Government","correspondingAuthor":true,"prefix":"","firstName":"Edgar","middleName":"","lastName":"Musiime","suffix":""},{"id":514562072,"identity":"9f2c3de8-31ff-4408-9f65-f76211ed8604","order_by":1,"name":"Kelvin Bwambale","email":"","orcid":"","institution":"Makerere University","correspondingAuthor":false,"prefix":"","firstName":"Kelvin","middleName":"","lastName":"Bwambale","suffix":""},{"id":514562073,"identity":"772e4e76-9196-44c9-80c2-f1472debf7e2","order_by":2,"name":"Priscilla Babirye","email":"","orcid":"","institution":"Ministry of Agriculture, Animal Industry and Fisheries","correspondingAuthor":false,"prefix":"","firstName":"Priscilla","middleName":"","lastName":"Babirye","suffix":""},{"id":514562074,"identity":"f76b9be7-8652-4cec-bb2d-bade5eee6fe6","order_by":3,"name":"Paul Lumu","email":"","orcid":"","institution":"Ministry of Agriculture, Animal Industry and Fisheries","correspondingAuthor":false,"prefix":"","firstName":"Paul","middleName":"","lastName":"Lumu","suffix":""},{"id":514562075,"identity":"5f2cb576-9734-4a43-a645-1418d24f5b60","order_by":4,"name":"Francis Mutebi","email":"","orcid":"","institution":"Makerere University","correspondingAuthor":false,"prefix":"","firstName":"Francis","middleName":"","lastName":"Mutebi","suffix":""},{"id":514562076,"identity":"9457bed9-f669-49c5-8641-59ac9a1b0c1d","order_by":5,"name":"Denis R. Mugizi","email":"","orcid":"","institution":"Makerere University","correspondingAuthor":false,"prefix":"","firstName":"Denis","middleName":"R.","lastName":"Mugizi","suffix":""}],"badges":[],"createdAt":"2025-08-26 13:08:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7463240/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7463240/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12917-026-05432-9","type":"published","date":"2026-04-13T15:58:07+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":91632454,"identity":"8398b249-06f6-4bef-b0a9-5e5b35b33018","added_by":"auto","created_at":"2025-09-18 13:20:57","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":137282,"visible":true,"origin":"","legend":"\u003cp\u003eA map showing the study area. The map was generated using QGIS 3.36 software using open-source datasets from the Uganda Bureau of Statistics.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7463240/v1/6dbf5a25d8c6ae2f9d634472.jpeg"},{"id":107351036,"identity":"45d48552-e321-4c2a-b860-483af21cb6ea","added_by":"auto","created_at":"2026-04-20 16:08:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":968668,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7463240/v1/2f94372a-ecaa-473c-93ae-099db60ebdf7.pdf"},{"id":91632453,"identity":"257b0152-3e34-44f9-8910-16b17ac6e851","added_by":"auto","created_at":"2025-09-18 13:20:57","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":37346,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryfileI.docx","url":"https://assets-eu.researchsquare.com/files/rs-7463240/v1/eaa364a218119f123a521cf4.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Seroprevalence of Peste des Petits Ruminants (PPR) and associated risk factors in selected sub-counties of Lyantonde district, South Western Uganda","fulltext":[{"header":"Back ground","content":"\u003cp\u003ePeste des Petits Ruminants (PPR), which is commonly known as Small Ruminant Plague, is an extremely infectious viral disease affecting small ruminants, including sheep and goats. The disease is caused by the Peste des Petits Ruminants virus (PPRV), a member of the Morbillivirus genus belonging to the Paramyxoviridae family (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). The PPR virus is primarily transmitted through respiratory aerosols and direct contact with infected animals (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). The disease manifests with a range of clinical signs that include fever, nasal and ocular discharge, coughing, and strenuous breathing. Affected animals may also exhibit oral mucosal lesions and gastrointestinal signs (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe disease has the most serious economic impact on communities that depend on small ruminants for sustenance and income (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). In particular, PPR is a major threat to the livelihoods of small-scale farmers and pastoralists, especially in developing countries. In na\u0026iuml;ve populations, PPR registers high mortality and morbidity rates of up to 90% and 100% respectively. In addition to reduced fertility, decreased milk and meat production directly affect the overall productivity of small ruminants (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eGlobally an estimated, 2.1\u0026nbsp;billion US dollars is lost annually due to the effects of PPR (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Uganda boasts of approximately 20\u0026nbsp;million goats and sheep that significantly contribute to the wellbeing of farming communities (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Approximately over 40% of households in Uganda keep and depend on goats and sheep, more so in the cattle corridor of the country (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). About five hundred thousand animals valued at 15\u0026nbsp;million US Dollars have been lost to PPR since the first outbreak in 2007 (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). The disease undermines the growth of the goat and sheep-subsector which is targeted for development in the wider drive to achieve the sustainable development goals (SDGs) like ending poverty, zero hunger, good health and wellbeing (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn light of the graveness of the threat posed by PPR, global efforts like vaccination campaigns, surveillance programs, and strict quarantine measures have been instituted by international organizations such as the Food and Agriculture Organization (FAO) and the World Organization for Animal Health (WOAH) with the objective to control and eradicate the disease by 2030 through the PPR Global Control and Eradication Strategy (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Uganda committed to this strategy and consequently developed the national PPR control and eradication strategy in line with resolutions of international conference in Abidjan of April 2015 to improve among others, surveillance, diagnostic capacity, and mass vaccination (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003ePeste des Petits Ruminants (PPR) remains a significant threat to small ruminant populations globally, causing substantial economic losses and negatively impacting the livelihoods of vulnerable communities (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). In spite of extensive endeavors to control the disease, several challenges persist, necessitating focused research and interventions (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). There is fragmented understanding of risk factors and occurrence patterns of PPR in Lyantonde district, which is predicted to have more than 50% probability of having future PPR outbreaks as well as an upsurge in trend of occurrence (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Proximity to Lake Mburo wildlife national park and presence of a booming livestock market could be major drivers in PPR disease occurrence. Amidst all these predispositions, the prevalence of PPR and associated risk factors have never been established. The development of effective control strategies is hampered by this limitation and renders the district vulnerable to imminent PPR incursion.\u003c/p\u003e\u003cp\u003eFuture prospects for controlling of PPR in the district will depend on proper understanding of the disease\u0026rsquo;s occurrence patterns and associated risk factors, which is largely lacking in Uganda. This study aims to establish the seroprevalence and risk factors of PPR in small ruminants in selected sub-counties of Lyantonde district\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy design\u003c/h2\u003e\u003cp\u003eThis was a cross-sectional serosurvey to determine the seroprevalence and risk factors of PPR in small ruminants in three selected sub counties of Lyantonde district which was carried out in July 2024 among farms with no history of vaccination.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eStudy area\u003c/h3\u003e\n\u003cp\u003eThe study was carried out in Lyantonde district which is located in the South-western region of Uganda. The coordinates of the district are 0.2241\u0026deg; S, 31.2168\u0026deg; E. Lyantonde borders Rakai district in the south, Masaka in the east, Kiruhura in the west and Sembabule in the north. Lyantonde is located in the dry cattle corridor of Uganda with vast pastoral rangelands characterized by semi-arid features, including intermittent low rainfall and protracted drought conditions characterized by water limitation and extreme heat result in shortage of pasture that in turn translates into frequent movement and interaction between different herds of small ruminants as well as wildlife species within the area. This interaction abets close contact and subsequently disease spread (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Three sub counties were purposively selected, namely; Lyantonde sub county, Lyakajula subcounty and Kinuuka subcounty as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Lyantonde sub county is characterized by goats and sheep farms with grazing area closest to Lake Mburo National Park. Lyakajula sub county hosts Kyemamba livestock market, the biggest livestock market in the district with booming goat and sheep trade while Kinuuka sub county has a main route for animal traffic running through.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eStudy population\u003c/h3\u003e\n\u003cp\u003eThe study population were sheep and goats aged greater than 4 months with no history of vaccination against PPR virus. Goats and sheep production is a significant livelihood activity in the Lyantonde practiced by 37.8% of the households (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). The farmers interviewed for this study were got from this demographic group. The farms were majorly small to medium holder farms ranging between 15\u0026ndash;203 animals in an agro-pastoral setting (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) with communal grazing, fenced farms and tethering being the predominant production systems employed. Indigenous goat breeds were the most commonly kept which included Mubende, Small East African and Kigezi goats whereas the exotic/cross goat breeds were uncommon and included, Boer, Kalahari red and Savannah. Sheep breeds kept include the East African black head, Masai red and East African long tailed sheep. Crosses among exotic and local breeds are also common and in many farming households, breeding is random. Lyantonde district was selected based on the large goat and sheep population, sizable traffic of animal movement, absence of a current active outbreak and willingness of farmers to participate in this research work.\u003c/p\u003e\n\u003ch3\u003eInclusion and Exclusion criteria\u003c/h3\u003e\n\u003cp\u003eIn this study, only sheep and goat farms with no history of vaccination against PPRV were included. Sheep and goats younger than 4 months of age and clinically sick small ruminants were excluded from sampling.\u003c/p\u003e\n\u003ch3\u003eSample size Determination and sampling\u003c/h3\u003e\n\u003cp\u003eThe sample size for goats and sheep for this study was deduced using the formula by (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Using 31.7% expected prevalence from a study (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e), 5% desired absolute precision and 95% confidence interval, sample size \u003cem\u003en\u003c/em\u003e was determined to be 333 sheep and goats.\u003c/p\u003e\u003cp\u003eThe three sub-counties were purposively selected based on their being on high risk of PPR outbreaks given proximity to the game park, active animal market and having an animal trade route traversing the area. Following this selection, a comprehensive list of sheep and goat farms within these sub-counties was obtained from veterinary staff in charge of the respective sub-counties. Then an integrated list of all sheep and goat keeping households was generated. Having set the maximum number of small ruminants to be sampled per farm at 12, 30 farms were randomly drawn from the list using a paper lottery method. Another 30 farms were also randomly selected as standby replacements from which any farm would be drawn if any of the selected farms did not meet the inclusion criteria. At sub county level, proportionate random sampling was employed. A total of 10 farms were drawn from each of the sub counties. At the farm level, the first 11 small ruminants meeting the inclusion criteria to be restrained were sampled.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eAnimal handling and sample Collection\u003c/h2\u003e\u003cp\u003eBlood samples were collected from live sheep and goats via jugular venipuncture. No anaesthesia or euthanasia was performed. The animals were manually restrained in a standing position, with their backs supported against the handler\u0026rsquo;s legs. The head was gently turned approximately 30\u0026deg; to expose the jugular vein. The collection site was disinfected with 70% alcohol, and blood was drawn using a sterile 19-gauge needle attached to a 10 ml vacutainer. After collection, firm pressure was applied with sterile cotton wool to stop bleeding. Each procedure involved two personnel, one restraining the animal and the other collecting the blood. All animals remained alive and were returned to normal management immediately after sample collection. The procedure followed established standard operating protocols (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e\u003cp\u003ePlain red top 10 ml vacutainer tubes and 19-gauge sterile needles were used to collect 6\u0026ndash;10 ml of whole blood from the jugular vein of sheep and goats that had not been previously vaccinated against PPR. To ensure accurate identification of every animal, the samples were labelled immediately. During blood sampling, data on the related risk factors, such as species, age, sex, herd size, and research area, were noted for each animal.\u003c/p\u003e\u003cp\u003eSamples of collected blood were stored at room temperature overnight in order to separate the serum. After that, these were briefly kept at the district laboratory at -20 \u0026ordm;C temperature before being transported to the National Animal Disease Diagnostics and Epidemiology Centre (NADDEC) laboratory in Entebbe for processing and laboratory analysis.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003eA structured questionnaire (\u003cem\u003eSupplementary file 1\u003c/em\u003e) was developed specifically for this study to collect information from sheep and goat owners about vaccination status to assist the sampling procedure and other farm characteristics as putative risk factors. At each selected farm, farmers or their representatives were interviewed to assess associated factors of PPR disease, such as species, sex, age, herd size, the likelihood of interaction with other flocks and wildlife, origin of the animals for restocking and breeding, distance from livestock markets, shared watering and grazing areas. The questionnaire was pretested by administering it to three selected farmers outside the sampling pool, one farmer from each of the selected sub-counties for this study. To ensure effective communication, the questionnaire was translated into two local languages, Luganda and Runyankore, and administered in these languages during interviews.\u003c/p\u003e\n\u003ch3\u003eLaboratory analysis\u003c/h3\u003e\n\u003cp\u003eThe ID Screen\u0026reg; PPR (IDvet, 310 rue Louis Pasteur Grabels, France) commercial competitive ELISA kit was used to detect PPR virus specific IgG antibodies in serum as per the manufacturer\u0026rsquo;s manual. This kit works on the principle of Antigen-Antibody reaction between purified recombinant PPR nucleoprotein (NP) and Anti-NP antibodies if present in serum. The antibody-antigen complex covers the NP epitopes. Briefly, an anti-NP-peroxidase (HRP) is added to the microwells followed by incubation. This fixes to the remaining free NP epitopes forming an antigen-conjugate-HRP complex. Washing is done to remove excess conjugate followed by addition of substrate solution (TMB). The resulting coloration depends on the quality of specific antibodies present in the serum to be tested. In absence of antibodies, a blue solution appears which turns to yellow on addition of stop solution (sulfuric acid). In presence of Antibodies, no coloration appears. The microplate is read at 450nm. For each sample, the competition percentage (S/N%) is calculated from:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:S/N\\%=\\frac{{OD}_{sample}}{{OD}_{NC}}x\\:100$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eSamples presenting S/N less than or equal to 50% were considered positive. Values greater than 50% and less than or equal to 60% were considered doubtful. An S/N value greater than 60% was considered negative.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eEstimation of true prevalence\u003c/h2\u003e\u003cp\u003eWith the sensitivity (99.4%) and specificity (94.5%) values of the ID Screen\u0026reg; PPR (IDvet, 310 rue Louis Pasteur Grabels, France), true prevalence was deduced using the Rogan \u0026amp; Gladen estimator below:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:True\\:prevalence=\\frac{\\left(Apparent\\:prevalence+Sp-1\\right)}{\\left(Se+Sp-1\\right)}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere Se is the test sensitivity and Sp is the test specificity (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe Rogan\u0026ndash;Gladen estimator method yields negative values if the apparent prevalence is less than the likelihood of discovering a false positive (1-test specificity). Furthermore, the true prevalence estimates will have a percentage greater than 100% if the apparent prevalence exceeds the sensitivity of the diagnostic test. Accordingly, the true prevalence estimates that are returned in both cases are not epidemiologically credible (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). The predetermined values for specificity (99.4%) and sensitivity (94.5%) provided by the manufacturers of the ELISA test were utilized to estimate the true prevalence\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eDescriptive statistics were first applied to summarize the data at the univariate level, calculating the mean and standard deviation (SD) for numerical variables such as age and herd size. Frequencies and percentages were utilized for categorical variables, including PPR seroprevalence and animal age categories. To identify risk factors associated with PPR seroprevalence, a modified Poisson regression was employed, as the outcome variable demonstrating a high prevalence, nearing 50%. Variables of species, age, sex, herd size, source of new stock, type of housing and noticing wildlife on farms were selected for multivariate analysis guided by two criteria: a variable was included if it had a p-value of less than 0.25 or if it was deemed to have a plausible association with PPR seroprevalence. To address multicollinearity, Pearson\u0026rsquo;s correlation coefficient was assessed, and variables with a coefficient greater than 0.4 with others were excluded to ensure the robustness of the model. A stepwise model-building technique was utilized to refine the model, with statistical significance set at a p-value of 0.05. The results of the analysis were systematically presented in tables.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eDemographic characteristics of individual animals\u003c/h2\u003e\u003cp\u003eOf the 351 small ruminants sampled, 92 (26.21%) were from Kinuuka, 129 (36.75%) from Lyakajula, and 130 (37.04%) were from Lyantonde. Goats constituted the majority of the animals sampled (n\u0026thinsp;=\u0026thinsp;313; 89.17%) and 38 (10.83%) were sheep. The age of the animals ranged from less than a year to five years, with the mean age being 1.97 years (SD\u0026thinsp;=\u0026thinsp;1.12) (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\u003eDemographic characteristics of individual animals\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=\"left\" 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\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNumber sampled\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSub-County\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKinuuka\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e92 (26.21)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLyakajula\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e129 (36.75)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLyantonde\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e130 (37.04)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge of the animal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLess than 1 year\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e73 (20.80)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 year\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e83 (23.65)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;1\u0026ndash;3 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e160 (45.58)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;3\u0026ndash;5 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e35 (9.97)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e270 (76.92)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e81 (23.08)\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\u003cp\u003eSheep\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e38 (10.83)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGoat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e313 (89.17)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBreed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCross\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e261 (74.36)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLocal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e90 (25.64)\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=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eFarm and herd management characteristics\u003c/h2\u003e\u003cp\u003eThe herds consisted primarily of goats, with an average herd size of 72 goats (SD\u0026thinsp;=\u0026thinsp;44), while the number of sheep was much smaller, averaging only 8 (SD\u0026thinsp;=\u0026thinsp;11) (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\u003eFarm and herd management characteristics\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=\"left\" 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\u003eHerd Characteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFrequency (n\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther species present on farm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCattle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e27 (90.00)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePigs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8 (26.67)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eType of housing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGoat Kraal/Boma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19 (63.33)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGoat shed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4 (13.33)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRaised goat house\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6 (20.00)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1 (3.33)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSource of new animals\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFellow farmers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13 (43.33)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLivestock market\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16 (53.33)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReceived as gifts\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1 (3.33)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProduction system\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFree range/extensive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e29 (96.67)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTethering\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1 (3.33)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eContact with other herds/flocks\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2 (6.67)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e28 (93.34)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eShare grazing and watering grounds\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4 (13.33)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26 (86.67)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNoticed wild life on the farm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16 (53.33)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14 (46.67)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eContact with market bound animals\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11 (36.67)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19 (63.33)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eShare breeding males\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7 (23.33)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e23 (76.67)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNursing kids following mothers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10 (33.33)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20 (66.67)\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=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eSocio-demographic characteristics of the respondents\u003c/h2\u003e\u003cp\u003eThe study included 30 farmers evenly distributed across the three sub-counties of Kinuuka, Lyakajula, and Lyantonde, with each sub-county contributing 10 farmers. The mean age of the farmers was 46.63 years (SD\u0026thinsp;=\u0026thinsp;17.38) (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\u003eSocio-demographic characteristics of study participants\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=\"left\" 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\u003eFarmer Characteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFrequency (n\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge category of farmer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBelow 40 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12 (40.00)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAbove 40 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18 (60.00)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMain occupation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFarmer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26 (86.67)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4 (13.33)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26 (86.67)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSingle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4 (13.33)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHighest level of Education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2 (6.67)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrimary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16 (53.33)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSecondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7 (23.33)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTertiary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5 (16.67)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRole on the farm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCare taker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3 (10.00)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHerdsman\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3 (10.00)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eManager\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2 (6.67)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOwner\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22 (73.33)\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=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eSeroprevalence of PPR in small ruminants in selected sub-counties of Lyantonde district, parishes and villages\u003c/h2\u003e\u003cp\u003eA total of 351 small ruminants were sampled, comprising 313 goats and 38 sheep. Among the goats, 136 tested positive for PPR antibodies, corresponding to an apparent prevalence of 43.5%, with an estimated true prevalence of 46.3% (95% CI: 33.7\u0026ndash;61.4). In sheep, 12 of the 38 samples were positive, giving an apparent prevalence of 31.6% and an estimated true prevalence of 33.6% (95% CI: 23.5\u0026ndash;47.5). When data were combined across species, the overall apparent prevalence of PPR in the sampled population was 42.2%, with an estimated true prevalence of 44.3% (95% CI: 32.8\u0026ndash;60.2) (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\u003eSeroprevalence of PPR among small ruminants\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" 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\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\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\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eNumber sampled\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eNumber positive\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eApparent Prevalence\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003eEstimated True prevalence (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGoats\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e313\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e136\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e43.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e46.27 (33.67, 61.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSheep\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e31.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e33.62 (23.54, 47.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOverall\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e351\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e148\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e42.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e44.27 (32.82, 60.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSub-County\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKinuuka\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e41.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e43.34 (38.26, 48.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLyakajula\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e129\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e48.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e50.54 (45.39, 55.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLyantonde\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e36.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e38.68 (33.70, 43.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOverall\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e351\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e148\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e42.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e44.27 (39.18, 49.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParish\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBiwolobo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e15.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e16.37 (9.14, 25.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBwamulamila\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e55.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e59.16 (44.91, 76.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKalagala\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e45.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e48.88 (36.25, 64.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKikwamba\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e60.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e63.89 (49.28, 81.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKirowoozo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e32.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e34.35 (23.54, 47.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKyemamba\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e26.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e28.18 (18.60, 40.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKyewanula\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e32.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e34.07 (23.54, 47.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLyakajula\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e63.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e67.52 (52.80, 86.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNakasozi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e23.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e24.57 (16.17, 36.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRweera\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e46.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e49.14 (36.25, 64.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWabusana\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e67.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e72.13 (56.33, 90.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVillage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAkabale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e44.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e47.33 (34.53, 62.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBinikila\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e92.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e98.30 (79.56, 99.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBwamulamila\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e55.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e59.16 (44.91, 76.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBwihagaju\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e60.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e63.89 (49.28, 81.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGayaza\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e21.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e22.81 (14.58, 34.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKamusenene\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e15.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e16.38 (9.14, 25.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKasambya b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e56.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e59.90 (45.78, 77.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKatoogo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e15.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e16.38 (9.14, 25.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKenshangu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e13.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e14.19 (7.65, 23.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKinuuka\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e13.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e14.19 (7.65, 23.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKyabazala\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e18.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e19.96 (12.21, 30.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKyaluguguza\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e28.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e30.42 (20.24, 42.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKyanzala\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e43.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e46.59 (34.53, 62.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKyemamba\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e83.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e88.74 (71.47, 99.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKyenvunikide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e80.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e85.19 (67.89, 95.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKyetume\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e33.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e35.49 (24.37, 48.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKyewanula\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e11.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e11.83 (6.20, 20.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLugalama\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e80.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e85.19 (67.89, 95.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLyakajula\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e63.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e67.53 (52.80, 86.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMakondo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMukokoma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e46.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e49.69 (37.11, 65.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNkoote b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e12.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e13.31 (6.92, 22.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNsheshe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e13.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e14.19 (7.65, 23.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRwemikoma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e41.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e44.37 (31.97, 59.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRwenyonyozi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e46.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e49.69 (37.11, 65.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eRisk factors associated with PPR occurrence among small ruminants in the selected sub-counties of Lyantonde district\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIndividual animal factors significantly associated with the prevalence of Peste des Petits Ruminants (PPR) included age, and species. Specifically, animals aged\u0026thinsp;\u0026gt;\u0026thinsp;1\u0026ndash;3 years and \u0026gt;\u0026thinsp;3\u0026ndash;5 years were more likely to have PPR antibodies compared to those less than one year (aPR\u0026thinsp;=\u0026thinsp;1.86; 95% CI: 1.16, 2.97) and (aPR\u0026thinsp;=\u0026thinsp;2.56; 95% CI: 1.54, 4.24), respectively. Furthermore, goats showed a higher risk of PPR compared to sheep (aPR\u0026thinsp;=\u0026thinsp;1.59; 95% CI: 1.01, 2.53).\u003c/p\u003e\u003cp\u003eFarm characteristics that were significantly associated with PPR included noticing wildlife on the farm, the type of housing, source of new animals, and the practice of nursing kids not following their mothers to grazing. Farms which reported presence of wildlife on the farm showed 43% higher seroprevalence of PPR compared to those that didn\u0026rsquo;t notice any wild life (aPR\u0026thinsp;=\u0026thinsp;1.43, 95%CI:1.07, 1.91). Farms that utilized a goat shed as housing type exhibited a reduced risk of PPR (aPR\u0026thinsp;=\u0026thinsp;0.44; 95% CI: 0.24, 0.77) compared to those with raised goat houses. Additionally, acquiring new animals from fellow farmers increased the likelihood of PPR (aPR\u0026thinsp;=\u0026thinsp;1.32; 95% CI: 1.02, 1.71). Lastly, not allowing nursing kids to follow their mothers was associated with a higher risk of PPR (aPR\u0026thinsp;=\u0026thinsp;1.36; 95% CI: 1.04, 1.76) Univariate, bivariate and multivariate analysis of exploratory risk factors is summarized in Table \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\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\u003eIndividual animal and herd characteristics associated with sero prevalence of PPR.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" 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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003ePPR Occurrence\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003euPR (95%CI) P-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eaPR (95%CI) P-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003ePositive, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eNegative, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge category\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\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLess than 1 year\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17 (11.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e56 (28.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1 year\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28 (18.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e55 (26.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.48 (0.88, 2.48) 0.131\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.41 (0.84, 2.34) 0.189\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;1\u0026ndash;3 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e81 (54.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e79 (38.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e2.20 (1.41, 3.43) 0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e1.86 (1.16, 2.97) 0.009\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;3\u0026ndash;5 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22 (14.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13 (6.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e2.73 (1.67, 4.46)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e2.56 (1.54, 4.24)\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\u003eSex\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\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e127 (85.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e143 (70.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e1.81 (1.22, 2.67) 0.003\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.32 (0.91, 1.92) 0.148\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21 (14.191)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e60 (29.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.00\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\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSheep\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12 (8.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26 (12.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGoat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e136 (91.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e177 (87.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.38 (0.84, 2.23) 0.198\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e1.59 (1.01, 2.53) 0.046\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBreed\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\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCross\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e110 (74.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e151 (74.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLocal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e38 (25.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e52 (25.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.01 (0.75, 1.33) 0.990\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.03 (0.78, 1.35) 0.840\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNoticed wildlife on the farm\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\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e96 (64.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e104 (51.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e52 (35.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e99 (48.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e1.39 (1.07, 1.81) 0.014\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e1.43 (1.07, 1.91) 0.015\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSource of new animals\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\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFellow farmers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e67 (45.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e89 (43.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.08 (0.84, 1.39) 0.543\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e1.32 (1.02, 1.71) 0.029\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLivestock market\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e71 (47.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e108 (53.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eReceived as gifts\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10 (6.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6 (2.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.58 (1.03, 2.40) 0.034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.12 (0.68, 1.86) 0.642\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNursing kids following mothers\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\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e62 (41.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e63 (31.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e1.30 (1.02, 1.66) 0.033\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e1.36 (1.04, 1.76) 0.02\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e86 (58.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e140 (68.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.00\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"},{"header":"Discussion","content":"\u003cp\u003eThis study set out to assess the seroprevalence of Peste des Petits Ruminants (PPR) antibodies and identify associated risk factors among small ruminants in Lyantonde district, Uganda. The results revealed a high overall estimated \u0026ldquo;true\u0026rdquo; prevalence of 44.27%, with variability across the selected sub-counties. The high seroprevalence obtained by this study is evidence of the high exposure of goats and sheep to PPRV. The highest seroprevalence was recorded in Lyakajula (50.54%), followed by Kinuuka (43.34%), and the lowest in Lyantonde (38.68%). These findings highlight the general understanding that PPR is endemic and remains a significant ever-looming threat in sub-Saharan Africa, but also highlight important local variations in exposure and risk. This state of persistent endemicity is recipe for imminent PPR outbreaks in Lyantonde and spread to previously unaffected areas (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe observed overall high prevalence of 44.27% is consistent with other studies conducted in Uganda and across Africa. For example, a study conducted in Nakapiripit in Uganda (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) found PPR seroprevalence at 44.1%, 60% in the Afar region in Ethiopia (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e), and 62% in South Sudan (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). However, the prevalence in Lyantonde district is notably higher than figures reported in other African settings, such as in the Oromia region in Ethiopia at 10% (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). This variation might be due to climatic differences between the localities, variation in the production system, local transmission dynamics, and proportion of the sample size.\u003c/p\u003e\u003cp\u003eThe seroprevalence of Peste des Petits Ruminants (PPR) in the different sub-counties can be closely linked to their geographical proximity to significant locations like livestock markets and wildlife habitats. In Lyakajjula sub-county, which hosts the Kyemamba livestock market, the highest seroprevalence of 50.54% was recorded. This elevated rate may be attributed to the high density of livestock trading in this area, facilitating the movement of potentially infected animals and increasing exposure to the PPR virus. A seroprevalence of 43.34% was documented in Kinuuka subcounty that has significant livestock trade routes between sub counties of Lyakajula and Lyantonde. A similar study (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) found a significant correlation between increase in road network, increased livestock numbers and the occurrence of PPR. The study also highlights how decrease in annual precipitation translates into increased movement of animals over long distances in search for pastures and water. A situation like this increases the possibility of contact between infected and na\u0026iuml;ve flock, which could aid in PPRV transmission (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). A seroprevalence of 38.68% was recorded in Lyantonde subcounty that is closest to Lake Mburo National Park. This is quite similar to the findings that reported seropositivity of 35% and 13% at two points of wildlife-livestock interface (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Mahapatra et al., 2015 reports cross infection of PPRV between domestic small ruminants and wild species like Impala and Grant\u0026rsquo;s gazelles (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe higher risk of PPR in farms reporting frequent wildlife sightings supports the hypothesis that wild animals may serve as a reservoir for PPR, contributing to transmission to domestic small ruminants. A study conducted at the wild life-livestock interface in the Northern Albertine Rift Valley and Nile Basin in East Africa indicates an epidemiological linkage between epizootic cycles in livestock and exposure in wildlife, denoting the importance of PPR surveillance on wild artiodactyls for both conservation and eradication programs (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Also, similar studies in Tanzania and Kenya (in the Greater Serengeti eco-system) have pointed to the potential role of wild ungulates, which can carry the virus without showing clinical signs, thereby acting as silent vectors for the disease (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe finding that goats were more likely to have PPR antibodies than sheep echoes similar results from studies in Ethiopia (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) and Sudan (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e), which consistently report higher PPR exposure in goats than sheep. Another study (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) alludes to the possibility that PPRV prefers goat over sheep when both are reared together. A study (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) deduced that a higher seroprevalence in goats than in sheep was associated with higher fertility in goats than sheep. This study cannot rule out the possibility that the difference in seropositivity between goats and sheep could be due to the difference in the husbandry practices and sampled proportions in both species. Most studies (\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e) note that goats are affected more severely by PPR virus exposure compared to sheep, and they exhibit easily noticed clinical signs while sheep undergo a mild form of the disease.\u003c/p\u003e\u003cp\u003eOn the contrary, some studies have identified sheep as being more susceptible to PPR infection, for example in Kassala state Sudan the apparent prevalence to be 68.1% in sheep and 43.5% in goats (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). In the Punjab province of Pakistan, the seroprevalence of PPR was in sheep was 56.80% versus 48.24% in goats (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Nonetheless, some studies (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e) have not identified such clear differences between species, suggesting that local factors such as husbandry practices, population and sample structure, and ecological settings and geospatial systems may influence species susceptibility.\u003c/p\u003e\u003cp\u003eOlder animals (\u0026gt;\u0026thinsp;1\u0026ndash;3 years and \u0026gt;\u0026thinsp;3\u0026ndash;5 years) were found to be at higher risk of having PPR antibodies compared to those under one year of age. This finding aligns with findings from several studies, such as in Uganda (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) and Sudan (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). This is likely due to cumulative exposure to the virus over time. As older animals are more likely to have encountered infected animals or contaminated environments, the likelihood of seroconversion increases with age. Additionally, very young animals may benefit from maternal immunity, temporarily protecting them from infection, further explaining the lower seroprevalence in younger age groups (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe significant association between acquiring new animals from fellow farmers and increased PPR risk highlights the role of informal animal trade in disease spread. This finding aligns with findings where the introduction of new animals into the flock without following appropriate trading or animal exchange protocol is a risk factor for PPR transmission (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). When farmers engage in unregulated exchanges without adequate biosecurity measures, such as quarantine or health screenings, they risk introducing infected animals into previously unexposed herds. This is particularly alarming for diseases like Peste des Petits Ruminants, which can spread rapidly among susceptible populations. The absence of such protocols allows asymptomatic carriers to enter healthy herds, leading to outbreaks that can have devastating economic impacts on farmers and threaten food security (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). The higher seroprevalence of PPR in farms that do not allow nursing kids to follow their dams raises intriguing questions about the protective mechanisms at play. While the exact reasons for this association remains unclear, one plausible explanation is that all-day nursing behavior may provide young animals with protective immunity through milk, which is rich in antibodies (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDespite its strengths, this study had several limitations. First, the cross-sectional design limits the ability to establish causal relationships between the identified risk factors and PPR seroprevalence. While associations were observed, it remains unclear whether the risk factors directly contributed to increased infection rates or if they are proxies for other unmeasured factors, such as the presence of other infectious diseases or broader ecological changes. Longitudinal studies are needed to establish more robust cause-and-effect relationships. The sheep population on the selected farms limited sample size for sheep in this study. This could have a bearing on the statistical validity and inference to the larger sheep population. More comprehensive and varied sheep samples should be used in future studies to validate these findings and improve their relevance to actual farming situations. The study relied on antibody detection assay rather than viral isolation, meaning it only captured past exposure to PPR and not active infection per se. As a result, the true prevalence of current infections could be lower than the reported figures. Additionally, antibody levels can persist for extended periods, and the study does not differentiate between recent and historic exposures. To gain a clearer picture of ongoing transmission dynamics, future studies should include viral detection methods such as RT-PCR alongside serology.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study demonstrated a high prevalence of PPR antibodies among small ruminants in Lyantonde District, with notable variation across sub-counties, and identified several animals- and farm-level risk factors for infection. Older animals, goats compared to sheep, farms reporting wildlife presence, and specific management practices such as sourcing new animals and the handling of kids or lambs were strongly associated with PPR seropositivity. These findings highlight the urgent need for risk-based control measures, including extensive vaccination in areas of high livestock trade and movement, strengthened collaboration between MAAIF and UWA to minimize livestock\u0026ndash;wildlife interactions, and enhanced farm-level biosecurity practices such as quarantining new animals and improving kid and lamb management, in order to effectively mitigate PPR transmission and safeguard small ruminant production in the district.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eaPR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eApparent prevalence\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eOD\u003csub\u003ePC\u003c/sub\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eOptical Density of Positive Control\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePPRV:\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePeste des Petits Ruminants Virus\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eRNA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eRibonucleic acid\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eUBOS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eUganda Bureau of Statistics\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eVNT\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eVirus Neutralization Test\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eWOAH\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eWorld Organization for Animal Health\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003e\u003cstrong\u003eEthical Consideration and consent to participate\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eEthical approval\u0026nbsp;for this study was obtained from the School of Veterinary Medicine and Animal Resources Research and Ethics Committee, Makerere University (Approval No. SVAR/216/2024). Administrative permission to conduct the study was also granted by the Lyantonde District Veterinary Office (DVO). All animal procedures were performed by veterinary personnel licensed by the Uganda Veterinary Board (UVB) in accordance with the OIE Terrestrial Animal Health Code and the ARRIVE guidelines for animal research. For the human component, written informed consent was obtained from all participating farmers prior to animal sampling and interviews. Participation was entirely voluntary, and all data were anonymized and treated with strict confidentiality. The study adhered to the ethical principles outlined in the Declaration of Helsinki.\u003c/p\u003e\n\u003ch2\u003eConsent for Publication\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eCompeting Interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eEM conceptualized the study, collected data, performed analysis, interpreted the results, and drafted the manuscript. FM and DRM supervised the study, contributed to conceptualization and methodology, and critically reviewed the manuscript. KB contributed to data analysis and critically reviewed the manuscript draft. PB and PL contributed to data collection, data analysis, and reviewed manuscript drafts. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eMy sincere thanks go to the veterinary officers, livestock farmers, and local authorities in the selected sub-counties of Lyantonde District for their cooperation and participation in this study. Their willingness to share their experiences and knowledge made this research possible.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable written request ([email protected]).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eVinayagamurthy B, Gurrappa Naidu G, Roy P. Peste des petits ruminant virus. Emerg Transbound Anim Viruses. 2020;315\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eUl-Rahman A, Abubakar M, Raza MA, Wensman JJ. Expansion in host dynamics of Peste des petits ruminants: potential attribute of outbreaks in disease-endemic settings. Acta Trop. 2022;106609.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNamatovu J, Mulindwa PL, Nkamwesiga J, Campbell Z, Ouma E. Gendered roles and disease management in small ruminant enterprises in agropastoral and pastoral systems: Implications for PPR control. ILRI Res Br. 2022;109(February):1\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLegnardi M, Raizman E, Beltran-Alcrudo D, Cinardi G, Robinson T, Falzon LC et al. Peste des Petits Ruminants in Central and Eastern Asia/West Eurasia: Epidemiological Situation and Status of Control and Eradication Activities after the First Phase of the PPR Global Eradication Programme (2017\u0026ndash;2021). 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Seroprevalence and risk factors of Peste des petits ruminants in different production systems in Uganda. Prev Vet Med [Internet]. 2023;221(September):106051. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.prevetmed.2023.106051\u003c/span\u003e\u003cspan address=\"10.1016/j.prevetmed.2023.106051\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVirginia Tech IACUC. SOP: Blood Collection in Sheep. 2017.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRobertson S. AEC-SOP-9.04-Collection-\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003eof-blood-small-ruminants-SOP013\u003c/span\u003e\u003cspan address=\"http://of-blood-small-ruminants-SOP013\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. 2023;22\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eROGAN, WJ, GLADEN B. ESTIMATING PREVALENCE FROM THE RESULTS. OF A SCREENING TEST. Am J Epidemiol. 1978;107(1):71\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDubie T, Dagnew B, Gelo E, Negash W, Hussein F, Woldehana M. Seroprevalence and associated risk factors of peste des petits ruminants among ovine and caprine in selected districts of Afar region, Ethiopia. BMC Vet Res. 2022;18(1):429.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAbdela N. Sero-prevalence, risk factors and distribution of foot and mouth disease in Ethiopia. Acta Trop. 2017;169:125\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEjigu E, Tolosa T, Begna F, Tegegne H. Sero-Prevalence and Associated Risk Factors of Peste Des Petits Ruminants in Dera and Gerar Jarso Districts of Oromia Region, Ethiopia. Vet Med Res Rep. 2023;14(July):111\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHerzog C. Transmission Dynamics of the Multi-Host Pathogen Peste des Petits Ruminants Virus Among Sheep, Goats, and Cattle in Northern Tanzania. 2020.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRahman AKMA, Islam SS, Sufian MA, Talukder MH, Ward MP, Mart\u0026iacute;nez-L\u0026oacute;pez B. Peste des Petits Ruminants Risk Factors and Space-Time Clusters in Bangladesh. Front Vet Sci. 2021;7(January):1\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eParida S, Muniraju M, Mahapatra M, Muthuchelvan D, Buczkowski H, Banyard AC. Peste des petits ruminants. Vet Microbiol. 2015;181(1):90\u0026ndash;106.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFernandez Aguilar X, Mahapatra M, Begovoeva M, Kalema-Zikusoka G, Driciru M, Ayebazibwe C et al. Peste des Petits Ruminants at the Wildlife-Livestock Interface in the Northern Albertine Rift and Nile Basin, East Africa. Viruses. 2020;12(3).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJones BA, Mahapatra M, Mdetele D, Keyyu J, Gakuya F, Eblate E et al. Peste des Petits Ruminants Virus Infection at the Wildlife-Livestock Interface in the Greater Serengeti Ecosystem, 2015\u0026ndash;2019. Viruses. 2021;13(5).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAli SEM, Ahmed YAM, Osman AA, Gamal Eldin OA, Osman NA. Prevalence of peste des petits ruminants virus antibodies in sheep and goats sera from Central-Western Sudan. Onderstepoort J Vet Res. 2023;90(1):e1\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDelil A, Asfaw A, Gebreegziabher B. Prevalence of antibodies to peste des petits ruminants virus before and during outbreaks of the disease in Awash Fentale district, Afar. Ethiop Trop Anim Heal Prod. 2012;44(7):1329\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKardjadj M, Kouidri B, Metref D, Luka PD, Ben-Mahdi MH. Seroprevalence, distribution and risk factor for peste des petits ruminants (PPR) in Algeria. Prev Vet Med [Internet]. 2015;122(1\u0026ndash;2):205\u0026ndash;10. 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Prevalence and distribution of peste des petits ruminants virus infection in small ruminants. Small Rumin Res. 2008;79(2\u0026ndash;3):152\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGebre T, Deneke Y, Begna F. Seroprevalence and Associated Risk Factors of Peste Des Petits Ruminants (PPR) in Sheep and Goats in Four Districts of Bench Maji and Kafa Zones. South West Ethiopia. 2018;20(6):260\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBailey AS. Importance of Colostrum for Lambs and Kids. 2019.\u003c/span\u003e\u003c/li\u003e\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":"Peste des Petits Ruminants, seroprevalence, goats, sheep, risk factors, Uganda","lastPublishedDoi":"10.21203/rs.3.rs-7463240/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7463240/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003eLyantonde District in Uganda, a recognized hotspot for Peste des Petits Ruminants (PPR) outbreaks. However, the magnitude and risk factors of PPR in this area remain poorly characterized. This study aimed to determine the prevalence of PPR antibodies in goats and sheep and to identify associated risk factors in selected sub-counties of Lyantonde District.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e A cross-sectional serosurvey was conducted in July 2024 in three purposively selected sub-counties (Lyantonde, Lyakajula, and Kinuuka), chosen for their large small ruminant populations, high livestock trade activity, and active animal movement. Farms without a history of PPR vaccination were randomly selected, and simple random sampling was used to select study animals. Whole blood samples were collected from the jugular vein and analyzed using the ID Screen® PPR competitive ELISA kit to detect virus-specific IgG antibodies. Multivariate analysis was performed using Modified Poisson regression to identify risk factors associated with PPR seroprevalence. Adjusted prevalence ratios (aPRs), 95% confidence intervals (CIs), and p-values were reported.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e A total of 351 goats and sheep were sampled, with an overall apparent seroprevalence of 42.17% (n=148). The Rogan-Gladen estimator gave a true prevalence of 44.27%, with goats showing a significantly higher prevalence (46.27%) compared to sheep (33.62%) (p=0.046). Age was strongly associated with seropositivity: animals aged \u0026gt;1–3 years (aPR=1.86; 95% CI: 1.16–2.97) and \u0026gt;3–5 years (aPR=2.56; 95% CI: 1.54–4.24) had higher risk compared to those \u0026lt;1 year. Goats were more likely than sheep to test positive (aPR=1.59; 95% CI: 1.01–2.53). Farms reporting wildlife interactions had a 43% higher prevalence (aPR=1.43; 95% CI: 1.07–1.91), and acquisition of new animals from other farmers also increased risk (aPR=1.32; 95% CI: 1.02–1.71).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e PPR transmission in Lyantonde District is driven by host factors, farm-level practices, and livestock-wildlife interactions. Risk-based control strategies, including targeted vaccination in areas with intense animal trade and movement, are essential. Strengthened collaboration between MAAIF and UWA is needed to minimize livestock–wildlife contact, alongside improved farm biosecurity practices.\u003c/p\u003e","manuscriptTitle":"Seroprevalence of Peste des Petits Ruminants (PPR) and associated risk factors in selected sub-counties of Lyantonde district, South Western Uganda","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-18 13:20:52","doi":"10.21203/rs.3.rs-7463240/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-12T14:37:41+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-12T12:37:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"306913894004406033775568530905315067815","date":"2025-10-20T11:34:08+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-30T17:13:29+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-24T11:54:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"222744508679221993753896578029776974123","date":"2025-09-13T11:03:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"106645516172359494472151765960431396899","date":"2025-09-11T08:47:38+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-11T08:14:08+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-11T06:53:45+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-11T06:22:12+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-10T11:15:47+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Veterinary Research","date":"2025-09-10T11:12:33+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":"539c2df6-985a-4778-83d2-d1d3d7f1a5e8","owner":[],"postedDate":"September 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-20T16:05:51+00:00","versionOfRecord":{"articleIdentity":"rs-7463240","link":"https://doi.org/10.1186/s12917-026-05432-9","journal":{"identity":"bmc-veterinary-research","isVorOnly":false,"title":"BMC Veterinary Research"},"publishedOn":"2026-04-13 15:58:07","publishedOnDateReadable":"April 13th, 2026"},"versionCreatedAt":"2025-09-18 13:20:52","video":"","vorDoi":"10.1186/s12917-026-05432-9","vorDoiUrl":"https://doi.org/10.1186/s12917-026-05432-9","workflowStages":[]},"version":"v1","identity":"rs-7463240","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7463240","identity":"rs-7463240","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}