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

Abstract

Background

Lyantonde District, Uganda, is 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 (p = 0.046) higher prevalence (46.27%) compared to sheep (33.62%). Age was significantly associated with seropositivity. Animals aged > 1–3 years (aPR = 1.86; 95% CI: 1.16–2.97, p = 0.009), and > 3–5 years (aPR = 2.56; 95% CI: 1.54–4.24, p < 0.0001) had higher prevalence ratios compared to those < 1 year. Goats also had higher prevalence ratios than sheep (aPR = 1.59; 95% CI: 1.01–2.53, p = 0.046). Farms reporting wildlife interactions had a 43% higher prevalence ratio (aPR = 1.43; 95% CI: 1.07–1.91, p = 0.015), and acquisition of new animals from other farmers was similarly associated with higher prevalence ratios (aPR = 1.32; 95% CI: 1.02–1.71, p = 0.029).

Conclusion

This study shows the high burden of PPR among small ruminants in Lyantonde district, with higher prevalence in goats than sheep and identification of risk factors associated with seropositivity. The study provides baseline evidence of PPR exposure and documents differences by species, age group, animal acquisition practices, and reported wildlife interactions. These findings contribute to improved characterization of PPR epidemiology in Lyantonde district and may inform the design of future surveillance and control activities aimed at reducing PPR burden.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12917-026-05432-9.

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]. In naive populations, PPR is associated with high morbidity and mortality reaching up to 100% and 90% respectively. In addition to reduced fertility, decreased milk and meat production directly affect the overall productivity of small ruminants [2].

The disease has substantial economic impact on communities that depend on small ruminants for sustenance and income [3]. In particular, PPR poses a major threat to the livelihoods of small-scale farmers and pastoralists, especially in developing countries, with global losses estimated at 2.1 billion US dollars annually [4]. As of 2021 Uganda had approximately 20 million goats and sheep that significantly contribute to the wellbeing of farming communities [5], with approximately 40% of households relying on these animals for livelihood and food security, particularly within the country’s cattle corridor [6]. In Lyantonde district specifically, goat and sheep production is an important livelihood activity practiced by 37.8% of households [7]. Since the first outbreak in 2007, the loss of about 500,000 animals valued at 15 million US Dollars has been attributed to PPR [5]. The disease continues to hinder the development of the goat and sheep-subsector, a key component in advancing the sustainable development goals (SDGs) including ending poverty, achieving zero hunger, promoting good health and wellbeing [8].

In response to the significant burden posed by PPR, global efforts including vaccination campaigns, enhanced surveillance programs, and strict quarantine measures have been implemented by international organizations such as the Food and Agriculture Organization (FAO) and the World Organization for Animal Health (WOAH). These initiatives aim to control and eradicate the disease by 2030 through the PPR Global Control and Eradication Strategy [9]. Uganda has committed to this global framework and consequently developed the national PPR control and eradication strategy aligned with the resolutions of international conference held in Abidjan of April 2015, with goals that included strengthening surveillance, improving diagnostic capacity, and implementing mass vaccination programs [5].

Despite extensive endeavors to control the disease, several challenges persist, necessitating targeted research and interventions [10]. Proximity to Lake Mburo wildlife national park may be one of the major drivers of PPR transmission, as wild angulates known to be disproportionately affected by PPR [11], are commonly sighted on farms located near the game park [12]. They have also been reported to play a significant role in interspecies transmission with domestic ruminants [11, 13]. Furthermore, the expanding livestock trade and frequent animal movements within the district have been associated with increased PPR occurrence and spread [5, 14]. Unfortunately, understanding of risk factors and occurrence patterns of PPR remains fragmented in Lyantonde district, even though the district is predicted to have more than a 50% probability of future outbreaks as well as an upsurge in trend of occurrence [15]. Additionally, the prevalence of PPR and associated risk factors have never been established, yet this information is important for veterinary health planning, and development of effective control strategies that address identified risk factors in high PPR prevalent animals. Therefore, 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. The study 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° South, 31.2168° East. 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. These are characterized by water limitation, extreme heat and shortage of pasture that may lead to 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 [16]. Three sub counties were purposively selected, namely; Lyantonde sub county, Lyakajula subcounty and Kinuuka subcounty (Fig. 1). Lyantonde sub county is characterized by goats and sheep farms with grazing areas 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.

Fig. 1.

A map showing the study area. The map was generated using QGIS 3.36 software using open-source datasets from the Uganda Bureau of Statistics

Study population

The study population were sheep and goats aged greater than 4 months with no history of vaccination against PPR virus. Goat and sheep farmers interviewed for this study were small to medium holder farms ranging from 15 to 203 animals in an agro-pastoral setting [17]. The predominant production employed at these farms were communal grazing and tethering. 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.

Sample size determination and sampling

The sample size for goats and sheep for this study was deduced using the formula by [18]. Using 31.7% expected prevalence from a study [17], 5% desired absolute precision and 95% confidence interval, sample size n was determined to be 333 (38 sheep and 313 goats).

The three sub-counties were purposively selected based on characteristics associated with high risk for PPR outbreaks, including proximity to the game park, presence of active animal markets, and the passage of animal trade route through 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. Although these lists constituted the most comprehensive sampling frame available, they may not have captured all eligible farms, particularly informal or newly established holdings. Consequently, the sample used in this study is representative of farms known to local veterinary services rather than all small ruminant-keeping households in the area. 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 [19, 20].

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.

Blood samples were stored at room temperature overnight in order to separate the serum. These were then briefly kept at the district laboratory at -20 °C 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 450 nm. For each sample, the competition percentage (S/N%) is calculated from:

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:

where Se is the test sensitivity and Sp is the test specificity [21].

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 [17]. 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 used to summarize the data at the univariate level, calculating the mean and standard deviation (SD) for numerical variables. Frequencies and percentages were utilized for categorical variables. 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 as prevalence ratios with corresponding 95% confidence intervals. It should be noted that whereas a stepwise model building technique was used to refine the final model, potential risk factors were pre-specified based on thorough literature review and epidemiological relevance to the outcome. Stepwise regression was used solely to assess model parsimony rather than identify associations from the beginning of analysis.

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%) compared to sheep (38 (10.83%)). 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) (Supplementary File 2).

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 43.45%, with an estimated true prevalence of 46.27% (95% CI: 33.67–61.35). In sheep, 12 of the 38 samples were positive, giving an apparent prevalence of 31.58% and an estimated true prevalence of 33.62% (95% CI: 23.54–47.51). When data were combined across species, the overall apparent prevalence of PPR in the sampled population was 42.17%, with an estimated true prevalence of 44.27% (95% CI: 32.82–60.21) (Table 3).

Table 3.

Seroprevalence of PPR among small ruminants

Variables	Number sampled	Number positive	Apparent Prevalence	Estimated True prevalence (95%CI)
Species
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)

Risk factors associated with PPR occurrence among small ruminants in the selected sub-counties of Lyantonde district

Individual animal factors significantly associated with the PPR seroprevalence included age, and species. Specifically, animals aged > 1–3 years and > 3–5 years had higher prevalence ratios of PPR antibodies compared to those less than one year (aPR = 1.86; 95% CI: 1.16, 2.97, p = 0.009) and (aPR = 2.56; 95% CI: 1.54, 4.24, p < 0.001), respectively. Furthermore, goats showed higher prevalence ratio of PPR seropositivity compared to sheep (aPR = 1.59; 95% CI: 1.01, 2.53, p = 0.046).

Farm characteristics that were significantly associated with PPR seroprevalence included reported wildlife sightings on the farm, source of newly acquired animals, and the practice of nursing kids not following their mothers to graze. Farms which reported presence of wildlife on the farm showed 43% higher prevalence ratios of PPR seropositivity compared to those that didn’t notice any wildlife (aPR = 1.43, 95% CI:1.07, 1.91). Additionally, acquiring new animals from fellow farmers was significantly (p = 0.029) associated with higher prevalence ratios of PPR seropositivity (aPR = 1.32; 95% CI: 1.02, 1.71). Lastly, the practice of not allowing nursing kids to follow their mothers to graze was significantly (p = 0.02) associated with higher prevalence ratios of PPR seropositivity (aPR = 1.36; 95% CI: 1.04, 1.76) Univariate, bivariate and multivariate analysis of exploratory risk factors is summarized in Table 4.

Table 4.

Individual animal and herd characteristics associated with sero prevalence of PPR

Variable	PPR Occurrence		uPR (95%CI) P-valuea	aPR (95%CI) P-valueb
	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

^auPR – Unadjusted Prevalence Ratio

^baPR – Adjusted Prevalence Ratio

*Statistically significant variables

(p < 0.05) are shown in bold

Discussion

This study set out to assess the seroprevalence of PPR antibodies and identify associated risk factors among small ruminants in Lyantonde district, Uganda. The results revealed a high overall estimated “true” prevalence with variability across the selected sub-counties. The highest seroprevalence was recorded in Lyakajula, followed by Kinuuka, and the lowest in Lyantonde. These findings highlight the general understanding that PPR remains a significant ever-looming threat in Lyantonde, but also show important local variations in exposure and risk factors.

The observed overall high prevalence is consistent with other studies conducted in Uganda [17] and across Africa [22]. For example, a study conducted in Nakapiripit Uganda [17] found PPR seroprevalence at 44.1%, 60% in the Afar region in Ethiopia [23], and 62% in South Sudan [24]. 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% [25]. This variation might be due to, local transmission dynamics [26].

The observed seroprevalence of PPR in the different sub-counties varied in relation to their geographical proximity to key locations such as livestock markets [26]. Higher seroprevalence levels have been observed in areas characterized by a high density of livestock trading, where frequent animal movements are common [17]. Additionally, a similar study [15] found a significant association between increase in road network, increased livestock numbers and the occurrence of PPR. The study also highlighted that reduced annual precipitation is associated with increased movement of animals over long distances in search for pastures and water [15]. This condition may increase opportunities for contact between infected and naive flock, and potentially facilitate PPRV transmission [27].

The higher prevalence ratios of PPR seropositivity in farms reporting frequent wildlife sightings is consistent with the hypothesis that wild animals may serve as a reservoir for PPR, and participate in transmission dynamics involving small domestic ruminants [28]. A study conducted at the wild life-livestock interface in the Northern Albertine Rift Valley and Nile Basin in East Africa reported an epidemiological linkage between epizootic cycles in livestock and exposure in wildlife [29]. Similarly, studies in Tanzania and Kenya within the Greater Serengeti eco-system have highlighted the potential role of wild ungulates, which may carry the virus without showing clinical signs, thereby acting as silent vectors for the disease [30]. This may in part explain the observed seroprevalence recorded in Lyantonde subcounty that is closest to Lake Mburo National Park, results that were quite similar to a previous study that reported seropositivity of 35% and 13% at two points of wildlife-livestock interface [31]. Mahapatra and co-workers reported cross infection of PPRV between domestic small ruminants and wild species like Impala and Grant’s gazelles [31].

The observation that goats exhibited higher seroprevalence of PPR antibodies than sheep is consistent with findings from studies conducted in Ethiopia [25] and Sudan [32], which also reported higher levels of PPR seropositivity in goats compared to sheep. Another study [1] reported the possibility of differential host susceptibility between species when goats and sheep are reared together. A different study [23] concluded that a higher seroprevalence in goats than in sheep was associated with higher fertility in goats than sheep.

Most studies [33–35] report that goats are affected more severely by PPR virus exposure compared to sheep, and they exhibit easily noticeable clinical signs while sheep undergo a mild form of the disease. In contrast, other studies have identified sheep as being more susceptible to PPR infection, for example in Kassala state Sudan the reported apparent prevalence was 68.1% in sheep and 43.5% in goats [36]. In the Punjab province of Pakistan, the reported seroprevalence of PPR in sheep was 56.80% versus 48.24% in goats [37]. Nonetheless, some studies [38] have not identified such clear differences between species, suggesting that local factors such as husbandry practices, and ecological settings and geospatial systems may influence species susceptibility.

The observed higher seroprevalence of PPR antibodies in older animals (> 1–3 years and > 3–5 years) compared to those under one year of age, aligns with several studies in Uganda [17] and Sudan [25]. This age-related distribution has been discussed in literature in relation to cumulative opportunities for exposure to the virus over time [39]. As older animals are more likely to have encountered infected animals or contaminated environments, the likelihood of seroconversion increases with age [40]. Additionally, very young animals may benefit from maternal immunity, temporarily protecting them from infection, further explaining the lower seroprevalence in younger age groups [17].

The significant association between acquiring new animals from fellow farmers and high prevalence ratios of PPR seropositivity aligns with findings that showed the introduction of new animals into the flock without following appropriate trading or animal exchange protocol is a risk factor for PPR transmission [25]. When farmers engage in unregulated exchanges without adequate biosecurity measures, such as quarantine or health screenings, the risk of introducing infected animals into previously unexposed herds increases [41]. This is particularly alarming for diseases like PPR, 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 [42].

Strengths and limitations

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 prevalence 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. At the farm level, animals were selected based on the first 11 small animals that met the inclusion criteria and could be successfully restrained. This introduces selection bias because true random selection of animals within farms was not feasible due to field constraints. The imbalance in sample size between species, with relatively fewer sheep (n = 38) than goats (n = 313), may have reduced statistical power for interspecies comparisons and limited the precision of prevalence estimates in sheep. 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. Furthermore, limitations related to the questionnaire design should be noted. Several relevant exposures were not directly measured, vaccination status was not verified, and potential key domains such as animal movement, biosecurity practices, herd composition, and wildlife exposure were not captured, potentially resulting into attenuated associations.

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. 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 associated with PPR seropositivity. These findings highlight the urgent need for control measures that address the observed risk factors aimed at reducing/eliminating PPR burden in Lyantonde, district.

Abbreviations

aPR

Apparent prevalence

APCs

Antigen-Presenting Cells

c-ELISA

Competitive Enzyme-Linked Immunosorbent Assay

ELISA

Enzyme-Linked Immunosorbent Assay

FAO

Food and Agriculture Organization

HPR

Horseradish Peroxidase

IACUC

Institutional Animal Care and Use Committee

ID screen

Innovative Diagnostics screen

IDvet

Innovative Diagnostics vet

IgG

Immunoglobulin G

MAAIF

Ministry of Agriculture, Animal Industry and Fisheries

NADDEC

National Animal Disease Diagnostics and Epidemiology Centre

Nm

Nanometers

NP

Nucleoprotein

OD

Optical Density

OD_NC

Optical Density of Negative Control

OD_PC

Optical Density of Positive Control

PPR

Peste des Petits Ruminants

PPRV

Peste des Petits Ruminants Virus

RT-PCR

Reverse Transcription-Polymerase Chain Reaction

RNA

Ribonucleic acid

SDGs

Sustainable Development Goals

UWA

Uganda Wildlife Authority

UBOS

Uganda Bureau of Statistics

VNT

Virus Neutralization Test

WOAH

World Organization for Animal Health

Authors’ contributions

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. OH, critically reviewed and revised the manuscripts, in response to reviewer and editor’s comments. All authors read and approved the final manuscript.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Data availability

The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable written request (edgarmusiimen@gmail.com).

Declarations

Ethics approval 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.