Longitudinal study of foot-and-mouth disease virus in Northern Nigeria: implications for the roles of small ruminants and environmental contamination in endemic settings

Longitudinal study of foot-and-mouth disease virus in Northern Nigeria: implications for the roles of small ruminants and environmental contamination in endemic settings | 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 Longitudinal study of foot-and-mouth disease virus in Northern Nigeria: implications for the roles of small ruminants and environmental contamination in endemic settings Simon Gubbins, Emma Brown, Yiltawe Wungak, Olumuyiwa Oyekan, Adeyinka J. Adedeji, and 16 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4312238/v2 This work is licensed under a CC BY 4.0 License Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Abstract Foot-and-mouth disease (FMD) is a highly contagious disease affecting cloven-hoofed ungulates. This study aimed to enhance our understanding of the role of small ruminants and environmental contamination in the epidemiology and endemicity of FMD. A longitudinal study was conducted between March 2021 and October 2021 in northern Nigeria, where monthly samples were collected from five households, one livestock market and one transhumance location in two local government areas (LGA) identified as being at high risk of FMD. Serum samples (n = 783), oral swabs (n = 424) and environmental swabs (n = 458) were collected and tested for the presence of foot-and-mouth disease virus (FMDV) RNA by rRT-PCR. Serum samples (n = 780) were also tested for the presence of antibodies against FMDV non-structural proteins. The proportion of FMDV RNA positive samples increased in all sample types collected in one LGA during the period when an FMD outbreak was reported in the same LGA. In contrast, sero-positive samples did not differ by month but differed between LGAs and amongst species. The force of infection estimated from age-seroprevalence data for each household was significantly lower in goats compared with both cattle or sheep. Five O/EA-3 topotype sequences were obtained from selected FMDV RNA positive samples; findings which support the use of environmental swabs to detect circulating FMDV strains in endemic settings. These results show oral and environmental swabs are suitable sampling methods for early detection at animal and herd level, respectively and provide insights on the role of small ruminants on FMD epidemiology. Veterinary Epidemiology cattle sheep goats surveillance foot-and-mouth disease virus endemic disease environmental sampling Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Foot-and-mouth disease virus (FMDV) is an RNA virus (family Picornaviridae , genus Aphthovirus ) characterised by high genetic and antigenic heterogeneity [ 1 ]. FMDV is the causative agent of foot-and-mouth disease (FMD), a highly contagious disease affecting wild and domestic cloven-hoofed ungulates. Globally, small ruminants (sheep and goats) represent the largest population of FMD susceptible domestic livestock. Although small ruminants can act as silent shedders of FMDV, their role in the epidemiology of FMD is generally neglected and poorly understood, partly due to the inapparent nature of clinical disease in these hosts [ 2 , 3 ]. Vesicular signs in small ruminants are often subtle and less apparent than those observed in cattle or are absent [ 3 ]. Furthermore, common clinical signs of FMD in small ruminants (lameness, fever, depression) are not disease specific. In Nigeria, small ruminants account for 84.5% of total grazing domestic livestock (48.6 million sheep and 76.3 million goats) [ 4 ]. Sheep and goats are mainly of indigenous breeds and largely kept in mixed farming with cattle by pastoralist and subsistence farmers, predominantly in northern parts of the country. FMD is endemic in Nigeria and, as in other endemic countries, outbreaks are reported mainly in cattle. However, FMDV RNA has been detected in sera and epithelial tissues, and evidence of antibodies against FMDV non-structural proteins (NSP) antibodies has been reported in sheep and goats [ 5 – 9 ]. Higher seroprevalence has been reported in sheep compared to goats [ 5 , 9 ], and lower seroprevalence in sheep compared to cattle [ 9 ]. Serotypes O, A, SAT 1 and SAT 2 have been isolated and characterised from cattle samples in Nigeria [ 7 , 10 – 12 ]. These four serotypes belong to diverse topotypes or genotypes and are phylogenetically related to strains circulating in West, Central and Northern Africa [ 8 , 11 , 13 , 14 ]. Virus isolation from clinically affected animals is considered the gold standard to confirm that animals are infected with FMDV. This is often followed by antigen ELISA and sequencing of viral genomes to determine the FMDV serotype and topotype circulating in the area. However, these methods have specific laboratory requirements, and depend on farmers or field vets to identify and correctly sample clinically affected animals. This is particularly challenging in endemic countries with scarce resources and where multiple FMDV serotypes circulate. Therefore, alternative, yet reliable, methods are needed to efficiently identify infected animals in a timely manner, conduct outbreak investigations and characterise the circulating serotype. Oral and nasal swabs have been suggested as an alternative method to recover viral RNA from infected and clinically healthy animals [ 15 , 16 ]. These have the advantages of being less invasive compared to probang sampling and allow the detection of infected animals prior to the appearance of clinical signs and/or when clinical signs are not noticeable. Environmental sampling (i.e., taking swabs of any surfaces likely to have been contaminated by secretions and excretions of infected animals) also presents an opportunity for non-invasive sample collection, enabling FMD surveillance at herd level and beyond regular investigation of clinical cases [ 17 – 19 ]. This is of particular value in places where sampling individual animals is challenging or not feasible. In addition, environmental sampling presents an alternative to the collection of clinical samples, potentially allowing FMDV detection at herd level faster [ 20 ], is less stressful for the animals, and potentially more cost effective. Combining recovery of viral RNA from contaminated surfaces with sequencing of viral genomes allows for strain identification and outbreak tracing. The aims of this study were (i) to enhance our understanding of the role of small ruminants and environmental contamination in the maintenance of FMD, and (ii) to identify reliable and convenient sampling methods for surveillance in endemic settings. Methods Study period and location Samples were collected once a month from March 2021 to October 2021, apart from August 2021 when sampling was not possible for security reasons. Samples were collected from five households, one livestock market and one transhumance location in both Bassa and Jos South local government areas (LGAs) in Plateau State in northern Nigeria (Fig. 1 a). These LGAs had been identified as being at high risk of FMD based on serological testing of samples from small ruminants [ 5 ] and FMD outbreaks reported in 2020 [ 21 ]. To be eligible for recruitment, households had to raise both cattle and small ruminants (sheep and/or goats) and agree to participate in the study. Eligible households were identified and selected with input from local contacts in each LGA. All were subsistence farmers keeping mixed herds with indigenous or mixed breeds, often taking animals to communal grazing and water points during the day. Participation was voluntary and no incentives were given to take part in the study. Transhumance locations were defined as a location where herders settle for up to two weeks before continuing their journey. There is only one livestock market and transhumance site in Jos South. Livestock markets and transhumance sites in Bassa were selected based on location, access, and agreement from people in charge to take part in the study. For transhumance sites, animal availability at the time of the sampling was also a factor. Further samples were collected, by the same field teams, from households reporting clinical outbreaks in the study area and in neighbouring LGAs during the same study period. In both Bassa and Jos South, the households that were sampled changed between the March and April visits, after which samples were taken from the same households on all subsequent visits (i.e. May-October). Results are presented for all households, but statistical analysis was only carried out using the results from samples collected between April and October. Animal sampling During the first household visit nine animals (3 sheep, 3 goats and 3 cattle) were selected systematically for sampling. If fewer than three animals of a species were present, the total number was completed by sampling other species. At subsequent visits, the same animals were sampled, if possible, but this was often not the case as some animals were sold or slaughtered during the study period. At livestock markets and transhumance sites, animals were selected at each visit from various locations within the market or site. Each animal sampled was given a unique ID number which was linked to the site where the animal was held during the month of the visit. In addition, the age and sex of animals sampled were collected. Clinical examination was conducted on the animals sampled by a qualified veterinarian in the field team, and animals showing FMD-like lesions on the day of the visit were recorded. Five millilitres of blood were collected from the jugular vein of each selected animal using pre-labelled vacutainer tubes (Becton Dickinson, USA). A sterile swab stick (SkyHealth, China) was used to swab the oropharyngeal/oral cavity of an animal and immediately put into PBS, as the transport medium. Once an animal had been sampled it was identified with a line in the ear using a non-toxic colour marker to avoid double sampling. All samples were kept at 4°C and sent to the National Veterinary Research Institute (NVRI) in Vom as soon as possible. On arrival at NVRI serum was separated into Eppendorf tubes clearly labelled with the unique animal ID number. Individual serum samples and oral swabs were stored at -20°C and shipped on dry ice to The Pirbright Institute, UK for testing. Environmental sampling At each sampling site, electrostatic dust cloths were used to swab areas of the environment where contact with secretions and excretions from infected animals was deemed likely (e.g. food troughs, hard floor surfaces, boots and tether ropes, transport vehicles and herder’s sticks). Up to ten environmental samples per site per visit were collected. Each environmental sample was given a unique ID number which was linked to the site, place from which the sample was collected and month of the visit. The environmental samples were processed in the field by eluting the swabs in PBS and then adding the samples directly into lysis buffer (MagMAX Core or RLT buffer, Thermo Fisher Scientific, UK) at a ratio of 1:1. All samples were stored at 4°C and shipped on dry ice to The Pirbright Institute for testing. Sample processing Environmental samples, serum samples and oral swabs were tested for the presence of FMDV RNA by rRT-PCR. Viral RNA was extracted from samples using the KingFisher Flex automated extraction platform (Thermo Fisher Scientific, UK) with the MagMAX™ CORE Nucleic Acid Purification Kit (Thermo Fisher Scientific, UK). FMDV RNA was detected by rRT-PCR on the ABI 7500 system (Applied Biosystems, UK) using an assay that targets the 3D region of the FMDV genome (forward: ACTGGGTTTTACAAACCTGTGA, reverse: GCGAGTCCTGCCACGGA, probe: TCCTTTGCACGCCGTGGGAC) [ 22 ]. Serum samples were also tested for the presence of antibodies against FMDV non-structural proteins (NSP). Samples were heat inactivated (at 56°C for 30 minutes, using a heat block) before testing with a PrioCHECK FMDV NS Antibody ELISA kit (Thermo Fisher Scientific Prionics AG, Waltham, MA, USA). Kits were used as per the manufacturer’s instructions. Sequencing Selected FMDV-positive samples were subjected to probe-enriched Illumina-based next generation sequencing. The selected samples had a mean rRT-PCR C T value of 27.2 (range: 20.9 to 32.9; see Additional file 1 for sample details). First and second strand synthesis of total nucleic acid was performed as described previously using the SuperScript™ double-stranded cDNA synthesis kit (ThermoFisher) [ 23 ]. Libraries were prepared following the Illumina DNA prep with enrichment protocol. The first stage libraries were pooled and subsequently enriched using a library comprising 26,275 unique biotinylated oligos designed using 622 complete genomes available in GenBank [ 24 ]. Final libraries were diluted and run on the Illumina MiSeq using a V2 300 nano cartridge generating 2 × 150 paired-end reads. Reads were initially assembled into contigs using SPADES, and each contig in turn was queried against a database of FMDV sequences to identify FMDV-specific contigs. Each FMDV contig was further subjected to BLAST online to identify the closest related sequence available. The 1D region of each most closely related virus was selected and used as a reference sequence for the reference assembly using BWA-MEM of each sample. Consensus sequences were extracted following reference assembly using VCF tools. The evolutionary history was inferred by using the Maximum Likelihood method based on the Tamura 3-parameter model [ 25 ]. The tree with the highest log likelihood is shown. The percentage of trees in which the associated taxa clustered together is shown next to the branches. Initial tree(s) for the heuristic search were obtained automatically by applying Neighbor-Join and BioNJ algorithms to a matrix of pairwise distances estimated using the Maximum Composite Likelihood (MCL) approach, and then selecting the topology with the superior log likelihood value. A discrete Gamma distribution was used to model evolutionary rate differences among sites (5 categories (+ G, parameter = 0.5492)). The tree was drawn to scale, with branch lengths measured in the number of substitutions per site. The analysis involved 20 nucleotide sequences. All positions with less than 95% site coverage were eliminated (i.e. fewer than 5% alignment gaps, missing data, and ambiguous bases were allowed at any position). Evolutionary analyses were conducted in MEGA7 [ 26 ]. Statistical analysis Test performance McNemar's test for paired data and the kappa statistic were used to assess whether the results for oral swabs and serum samples tested by rRT-PCR from the same animals differed significantly from one another. A Bayesian latent class analysis was used to estimate the diagnostic sensitivity (Se) and specificity (Sp), under field conditions, for oral swabs and serum samples tested by rRT-PCR. For this, we adapted a previously published Hui-Walter model [ 27 ] implemented using JAGS and rjags R package [ 28 , 29 ] and R (version 4.4.0) [ 30 ]. Priors for Se and Sp were informed by previously published values (see Additional file 2 for details). A beta distribution with parameters α and β was assumed for the priors, and distribution parameters were estimated using the functions fitdist and epi.betabuster from the R packages fitdistrplus and epiR [ 31 , 32 ]. Test results were classified as either positive or negative, and animals were classified as originating from two subpopulations: households or livestock markets and transhumance sites. Proportion of positive samples The proportion of positive samples by rRT-PCR or by ELISA was analysed using a binomial generalised linear mixed model with a logit link function. Because of the marked difference in the number of positive rRT-PCR results between LGAs, data from Bassa and Jos South were analysed in separate models (see Additional file 3 for details of the models). By contrast, the number of positive ELISA results were similar in both LGAs and, accordingly, the ELISA results were analysed in a single model (see Additional file 3 for details of the models). In each model the response variable was whether or not a sample was positive. Explanatory variables considered in the models included the LGA, month the sample was taken, sample type and species as fixed effects, and sampling location as a random effect. Models were implemented in a Bayesian framework using OpenBUGS (version 3.2.3; https://www.mrc-bsu.cam.ac.uk/software/ ). Diffuse priors were used for model parameters: normal with mean 0 and standard deviation 10 for regression coefficients; and exponential with mean 100 for the random effect variance. Two chains of 60,000 iterations were run with the first 10,000 samples discarded to allow for burn-in of the chains. Chains were subsequently thinned by selecting every fifth iteration to reduce autocorrelation amongst the samples. Convergence was monitored visually and using the Gelman-Rubin statistic in OpenBUGS. Models including different explanatory variables were compared using the deviance information criterion (DIC) [ 33 ]. Basic reproduction number for FMDV The basic reproduction number ( R 0 ) was estimated in two ways, reflecting what is measured by the diagnostic tests used to infer R 0 . First, we used the NSP ELISA data to estimate the force of infection (i.e. the rate at which susceptible individuals acquire infection) for each household and, hence, R 0 , from age-seroprevalence data. This is not a measure of the first exposure of an animal to FMDV, because NSP ELISA reflects historic exposure to any strain or serotype of FMDV rather than recent seroconversion. Second, we used the rRT-PCR data for serum samples and oral swabs to estimate R 0 for individual outbreaks in households. This reflects the fact that rRT-PCR measures only recent exposure to FMDV, though the PCR target (3D gene of FMDV) used in the present study is not strain or serotype specific. To estimate the force of infection for each household from NSP ELISA data, the age and NSP status of each animal (i.e. negative or positive by NSP ELISA) at its first sampling was extracted from the data (see Additional files 4 and 5 for results for individual animals). To simplify the analysis, only home-bred animals (n = 306) were included in the analysis; those bought-in to the household (n = 18) were excluded. The force of infection was estimated using a catalytic model [ 34 , 35 ], so that $$\:p\left(a\right)=1-\text{exp}\left(-\lambda\:a\right),$$ where p ( a ) is the seroprevalence at age a and λ is the force of infection. The force of infection can then be used to estimate the basic reproduction number, R 0 , using the relationship R 0 = λL where L is the mean lifespan of an animal [ 35 ]. For cattle and sheep in the study area, the mean lifespans are 5 and 2 years, respectively. Heterogeneities in the force of infection were incorporated to allow it to vary amongst species and households/LGAs. In this case, the force of infection for an individual is given by $$\:\text{log}\lambda\:=\alpha\:+{\beta\:}_{SPP}+{\gamma\:}_{LGA,HOUSE},$$ where α is the baseline, β is the effect of species and γ is the effect of LGA and household. Here γ is treated as a random effect and drawn from a normal distribution with mean 0 and standard deviation σ γ . Parameters in the model were estimated in a Bayesian framework using OpenBUGS (version 2.3.2). A Bernoulli likelihood was used with diffuse priors for model parameters (normal with mean 0 and standard deviation 10 for α and β ; exponential with mean 100 for σ γ ). Two chains of 30,000 iterations were run with the first 5,000 samples discarded to allow for burn-in of the chains. Chains were subsequently thinned by selecting every fifth iteration to reduce autocorrelation amongst the samples. Convergence was monitored visually and using the Gelman-Rubin statistic in OpenBUGS. Models including different explanatory variables were compared using the DIC (see Additional file 6 for details of the models). The final model was checked by comparing the observed data to the posterior predictive distribution [ 36 ]. To estimate the basic reproduction number for individual outbreaks, we used the results of testing serum samples and oral swabs by rRT PCR collected from cattle and sheep. An outbreak was defined as any one or two consecutive monthly samplings when at least one sample from the household was positive by rRT-PCR (note: there were no three or more consecutive monthly samplings with positive PCR results). The proportion of animals positive by rRT-PCR was assumed to be an estimate of the final size of the outbreak. The final size of an outbreak, f , is related to the basic reproduction number, R 0 , by the following equation, $$\:1-f-{s}_{0}\text{exp}\left(-{R}_{0}f\right)=0,$$ where s 0 is the initial proportion of the household that is susceptible [ 37 ]. Parameters (i.e. R 0 and s 0 for each outbreak) were estimated in a Bayesian framework. A binomial likelihood was used for the data, that is, $$\:L=\prod\:_{h}{f}_{h}^{{P}_{h}}{(1-{f}_{h})}^{{N}_{h}-{P}_{h}},$$ where f h is the final size and P h and N h are the number of PCR positive animals (based on serum samples and oral swabs) and the number of animals tested, respectively, for outbreak h . A gamma prior with mean 10 and shape 2 was used for R 0 , while a uniform prior with range [0,1] was used for s 0 . Samples from the joint posterior distribution were generated using an adaptive Metropolis algorithm [ 38 ], modified so that the scaling factor was tuned during burn-in to ensure an acceptance rate of between 20% and 40% for more efficient sampling of the target distribution [ 39 ]. Two chains of 600,000 iterations were run with the first 100,000 samples discarded to allow for burn-in of the chains. Chains were subsequently thinned by selecting every fiftieth iteration to reduce autocorrelation amongst the samples. The adaptive Metropolis scheme was implemented using Matlab (version 2020b; The Mathworks, Inc.). Convergence was monitored visually and using the Gelman-Rubin statistic in the coda package [ 40 ] in R (version 4.4.0) [ 30 ]. The final model was checked by comparing the observed data to the posterior predictive distribution [ 36 ]. To explore differences in R 0 between cattle and sheep for individual outbreaks, we carried out three analyses: one using data for cattle and sheep combined; one using data for cattle only; and one using data for sheep only. Results Animal sampling for FMDV RNA A total of 783 serum samples were collected (591 from animals in households, 126 in livestock markets and 66 from transhumance sites) and tested for FMDV RNA by rRT-PCR (Table 1 ). Serum samples were collected from cattle (n = 284; 215 females and 69 males), sheep (n = 407; 332 females and 75 males) and goats (n = 92; 68 females and 24 males), of which nine (3.2%), nine (2.2%) and one (1.1%) were positive, respectively (Fig. 2 ; see also Additional files 4 and 5 for results for individual animals). The median age of animals sampled was 5 years (interquartile range (IQR): 3–5 years) in cattle; 2 years (IQR: 1–3 years) in sheep; and 2 years (IQR: 1-2.5 years) in goats. Between zero and four (0%-3.2%) serum samples were positive each month, except for October where nine (8.3%) were positive (Table 1 ). Positive samples were collected at households and transhumance locations in both Bassa and Jos South and at the livestock market in Jos South (Fig. 2 ). Table 1 Summary of environmental, serum and oral swab samples tested by rRT-PCR for foot-and-mouth disease virus (FMDV) RNA or for antibodies against FMDV non-structural proteins (NSP) by ELISA. month rRT-PCR NSP ELISA environmental swabs oral swabs serum samples serum samples tested positive tested positive tested positive tested positive March 71 0 38 0 90 1 (1.1%) 90 55 (61.1%) April 75 2 (2.7%) 77 0 125 4 (3.2%) 125 72 (57.6%) May 77 0 71 0 117 1 (0.9%) 117 73 (62.4%) June 75 0 71 0 123 0 122 71 (58.2%) July 74 0 68 0 111 2 (1.8%) 110 56 (50.9%) August* 0 - 0 - 0 - 0 - September 56 15 (26.8%) 65 7 (10.8%) 108 2 (1.9%) 108 51 (47.2%) October 30 1 (3.3%) 34 2 (5.9%) 109 9 (8.3%) 108 57 (52.8%) total 458 18 (3.9%) 424 9 (2.1%) 783 19 (2.4%) 780 435 (55.8%) * no sampling was carried out in August for security reasons Oral swabs were collected from a subset of the animals from which serum samples were taken (n = 424) and tested for FMDV RNA by rRT-PCR (Table 1 ). Samples were collected from cattle (n = 3; all female), sheep (n = 345; 278 females and 67 males) and goats (n = 76; 55 females and 21 males), of which zero (0%), seven (2.0%) and two (2.6%) were positive, respectively (Fig. 2 ; see also Additional files 4 and 5 for results for individual animals). All cattle sampled were 4 years old. The median age for sheep sampled was 2 years (IQR: 1–3 years), and for goats sampled was 2 years (IQR: 1-2.5 years). None of the oral swabs collected between March and July were positive for FMDV RNA, but nine collected during September and October were positive (Table 1 ). Of these positive samples, one was collected at a household in Bassa (a female goat, 18 months old), while the remaining eight were collected at four (of five) households (all sheep, seven females and one male) and at the livestock market (one female goat, 18 months old) in Jos South (Fig. 2 ). Although there was a no significant difference between the proportion of positives (p > 0.9), there was only a slight agreement (kappa = 0.08) between serum and oral swabs tested for FMDV RNA (see Additional file 7 for further details). FMDV RNA is detected only for a short period of time (both in serum samples and oral swabs), with a slight difference in timing and short period of overlap. Infected animals are likely to test positive by rRT-PCR in oral samples first, and slightly later in serum (Fig. 4 ) once the infection is systemic. Sensitivity and specificity were estimated (posterior median and 95% credible interval (CI)) to be 0.720 (0.551–0.855) and 0.980 (0.964–0.990) for rRT-PCR in serum, and 0.877 (0.780–0.943) and 0.979 (0.963–0.993) for rRT-PCR in oral swabs. Clinical signs of FMD One two-year old bovine from household H5 in Jos South showed clinical signs of FMD during the sampling visit in September. A serum sample taken from the animal was positive for viral RNA (C T value: 26.6) and negative by NSP ELISA. Two further cattle, one sampled in March (from household H2 in Bassa) and one sampled in October (at the livestock market in Bassa) also showed FMD-like signs, but serum samples from both animals were negative for viral RNA and by NSP ELISA. Environmental sampling for FMDV RNA A total of 458 environmental samples were collected (Table 1 ). For the samples collected between March and July, only two (0.5%) were positive for FMDV RNA (Table 1 ; Fig. 3 ). One positive swab was from a herder’s stick at a household in Bassa, and the other was from boots at a transhumance site in Jos South. By contrast, 16 (out of 86; 18.6%) environmental samples collected in September and October were positive for FMDV RNA (Table 1 ). These were all collected in Jos South, at four (of five) households and the livestock market (Fig. 3 ). Positive samples were collected from boots (n = 4), ropes (n = 3), pegs (n = 3), transport vehicles (n = 2) and hard floor surfaces (n = 4). Comparison of animal and environmental sampling for FMDV RNA Based on changes in DIC the proportion of positive samples in both regions differed by month and amongst sample types (see Additional file 3 for details of the models and their DICs). Specifically, the proportion of positive samples differed amongst environmental swabs, oral swabs and serum samples, but not with the species the animal samples were collected from. For samples collected in Bassa, the proportion of positive samples was low (posterior median < 2.9%) for all months and sample types (Figs. 2 – 4 ). For samples collected in Jos South, the proportion of positive samples was low (posterior median 3.5%) for samples collected in September or October (Fig. 4 ). In September, the proportion of positive samples was highest for environmental swabs, followed by oral swabs, then serum samples (posterior medians of 65.6%, 21.4% and 3.5%, respectively). In October, this pattern changed, with the highest proportion of positive samples in serum samples, followed by environmental swabs and then oral swabs (posterior medians of 16.8%, 5.9% and 5.0%, respectively). This pattern could be related to FMD outbreaks reported in LGAs near to the study area (see below). Antibodies to FMDV non-structural proteins (NSP) A total of 780 serum samples (283 from cattle, 406 from sheep and 91 from goats) were tested by ELISA for NSP antibodies, of which of which 219 (77.3%), 212 (52.2%) and three (3.3%) were seropositive for cattle, sheep and goats, respectively. Seropositive animals were detected throughout the study period with between 47.2% and 62.4% of animals seropositive each month (Table 1 ; Fig. 2 ; see also Additional files 4 and 5 for results for individual animals). For animals that were repeatedly sampled at different households, animals that seroconverted for the first time during the study period tend to remain seropositive for the rest of the study period, which is expected for FMDV-NSP antibodies. For those animals that became seronegative during the study period, it was unknown how long they had been seropositive, as they were already seropositive at the time of the first sampling. Based on changes in DIC the proportion of NSP ELISA positive samples did not differ by month but did differ between regions and amongst species (see Additional file 3 for details of the models compared and their DICs). In both regions, the proportion of NSP ELISA positive samples was highest in cattle, followed by sheep, and lowest in goats (Fig. 4 ). The proportion of seropositive cattle did not differ between regions (posterior median: 79.4% and 78.9% for Bassa and Jos South, respectively). However, the proportion of seropositive sheep was lower in Jos South compared with Bassa (posterior median: 64.3% and 47.5% for Bassa and Jos South, respectively), while the proportion in goats was lower in Bassa compared with Jos South (posterior median: <0.1% and 13.3% for Bassa and Jos South, respectively). Basic reproduction number for FMDV Seroprevalence increased with age for both cattle and sheep in all five households sampled in Bassa and Jos South (Fig. 5 ). Goats were only sampled in two households (H4 and H5) in Bassa, all of which were negative by NSP ELISA (Fig. 2 ). Changes in DIC show that the corresponding force of infection differed amongst species and amongst households (see Additional file 6 for details of the models compared and their DICs). Posterior predictive checking showed that the model for the seroprevalence data provided an acceptable fit to the data, with all but one observation lying in the 95% prediction intervals (see Additional file 8 for model checking). The force of infection in goats was significantly lower than that in cattle or sheep (goats relative to cattle (posterior median and 95% CI: 1.8×10 − 4 (9.1×10 − 11 -0.082); goats relative to sheep: 1.9×10 − 4 (1.0×10 − 10 -0.085); Table 2 ). However, the force of infection in sheep was comparable to that in cattle (that in sheep relative to cattle: 0.96 (0.68–1.39); Table 2 ). The force of infection differed amongst households, but there were no systematic differences between the two regions (Fig. 5 ). The basic reproduction number ( R 0 ) for cattle and sheep was calculated from the force of infection by multiplying it by the mean lifespan of the species. This indicated that the basic reproduction number was higher for cattle (median: 1.75; range: 1.00-2.31) than for sheep (median: 0.68; range: 0.39–0.89) across all ten households (Table 3 ). For all ten households the median R 0 for cattle was ≥ 1 and for four households it was significantly ( P < 0.05) ≥ 1 (Table 3 ). By contrast, the median R 0 for sheep was < 1 for all ten households and for four households it was significantly ( P < 0.05) < 1 (Table 3 ). Table 2 Estimated parameters for the force of infection for foot-and-mouth disease virus in households in Northern Nigeria. parameter* posterior median 95% credible limit lower upper baseline ( α ) -1.11 -1.47 -0.73 species ( β ) cattle 0 (baseline) - - sheep -0.04 -0.38 0.33 goats -8.60 -23.12 -2.50 household random effect ( σ γ ) 0.34 0.11 0.70 * note that these parameters and on the natural logarithm scale Table 3 Basic reproduction number ( R 0 ) for foot-and-mouth disease virus in households in Northern Nigeria. region household outbreak cattle sheep cattle and sheep estimate* 95% credible limit estimate 95% credible limit estimate 95% credible limit lower upper lower upper lower upper PCR results for individual outbreaks Bassa H1 July 1.07 0.21 2.57 - - - 0.63 0.12 1.20 H2 July - - - 0.71 0.13 1.43 0.64 0.12 1.18 H5 April - - - 1.06 0.20 2.51 0.73 0.14 1.42 Jos South H1 April - - - 0.72 0.14 1.43 0.63 0.11 1.19 Sep-Oct 1.01 0.20 2.16 0.61 0.12 1.14 0.67 0.13 1.19 H2 Sep-Oct 0.78 0.15 1.63 0.75 0.15 1.43 0.71 0.14 1.26 H3 Sep-Oct 0.71 0.13 1.43 0.61 0.11 1.16 0.61 0.12 1.11 H4 May 1.07 0.20 2.55 - - - 0.62 0.12 1.19 Sep-Oct - - - 0.69 0.13 1.28 0.61 0.11 1.10 H5 Sep-Oct 0.71 0.13 1.44 0.84 0.16 1.53 0.72 0.15 1.29 Kanke P7 Dec - - - 2.05 0.51 4.40 - - - age-dependent seroprevalence (NSP ELISA) Bassa H1 - 2.31 1.34 4.36 0.89 0.53 1.64 - - - H2 - 1.00 0.55 1.64 0.39 0.21 0.62 - - - H3 - 2.06 1.22 3.70 0.80 0.50 1.34 - - - H4 - 1.30 0.80 1.96 0.50 0.32 0.73 - - - H5 - 1.67 0.96 2.85 0.65 0.37 1.09 - - - Jos South H1 - 1.58 0.99 2.47 0.61 0.39 0.92 - - - H2 - 1.82 0.95 4.45 0.70 0.37 1.70 - - - H3 - 1.82 1.19 2.83 0.70 0.47 1.05 - - - H4 - 1.17 0.66 1.85 0.46 0.25 0.72 - - - H5 - 2.27 1.44 3.63 0.88 0.58 1.34 - - - Ten outbreaks were identified during the study period, where an outbreak was defined as any one or two consecutive monthly samplings when at least one sample was positive by rRT-PCR. Three outbreaks occurred in households in Bassa and five in households in Jos South (Fig. 2 ). Six of the households had a single outbreak, while two (both in Jos South) had two outbreaks each with gaps of three or four months between outbreaks (Fig. 2 ). The proportion of PCR positive animals was assumed to be a measure of the final size of the outbreak and used to estimate R 0 for each outbreak (Table 3 ; see Additional file 9 for posterior densities for R 0 for each outbreak). Posterior predictive checking showed that this model provided an acceptable fit to the data, with all observations lying in the 95% prediction intervals for each outbreak (see Additional file 10 for model checking). Across the outbreaks the median estimate for the basic reproduction number was higher for cattle (median: 0.90; range: 0.71–1.07) than for sheep (median: 0.71; range: 0.61–1.06) (Table 3 ). However, there was substantial overlap of the posterior distributions, indicating the estimates did not differ significantly between cattle and sheep. For all outbreaks, the 95% credible intervals for R 0 included the threshold at R 0 = 1. When the results for cattle and sheep were combined, the estimates for R 0 were lower than those for the individual species (Table 3 ). In addition to R 0 , the initial proportion of the household that was susceptible ( s 0 ) was also estimated for each outbreak (see Additional file 11 for posterior densities for s 0 for each outbreak). Across the outbreaks the median initial proportion susceptible was the same for cattle (median: 0.82; range: 0.77–0.88) and sheep (median: 0.88; range: 0.76–0.93). Sampling at households with reported outbreaks Additional environmental sampling was also carried out at three households with reported outbreaks in neighbouring LGAs, two in Wase LGA and one in Kanke LGA, both of which are in Plateau State (Fig. 1 b). Environmental swabs taken at all three households were positive for FMDV RNA (Table 4 ). All cattle from the three households had FMD-like lesions in their oral cavities (one bull from household P1, five cattle from household P2, and three cattle from household P7). Tissue samples were collected from five and two cattle from households P2 and P7 respectively, all of which tested positive by rRT-PCR and virus isolation. In addition, serum samples collected from sheep at all three households were positive by NSP ELISA or rRT-PCR (Table 4 ). Table 4 Sampling carried out at households with reported outbreaks. outbreak month sampled environmental samples positive locations animal samples collected positive Wase P1 September 4 4 (100%) fence (×2), feeding trough, rope 3/3 sheep NSP ELISA positive† Wase P2 September 4 3 (75%) fence, rope (×2) 2/3 sheep NSP ELISA positive† Kanke P7 December 5 2 (40%) pegs, ropes 5/6 sheep rRT-PCR positive; 3/6 sheep NSP ELISA positive †rRT- PCR results are not available for these animals Supporting information Assuming the 5 (out of 6) samples from sheep positive by rRT-PCR (Table 4 ) are a measure of the final size for the outbreak in Kanke LGA, the basic reproduction number was estimated to be 2.05 (95% credible interval: 0.51–4.40) (Table 3 ). Sequencing Eight samples were selected for sequencing using the probe enrichment technique; samples were selected to cover a range of C T values, locations and sample types (animal and environmental) (see Additional file 1 for sample details). Sequences for the VP1 region of the FMDV genome were obtained for five of these samples which had C T values that ranged between 20.9 and 26.6. All five samples for which VP1 sequence data were generated were serotype O, EA-3 topotype. There was a total of 633 positions in the final dataset. VP1 sequences were closely related to other samples collected in Nigeria and submitted to World Reference Laboratory for FMD (WRLFMD) over the same time period as the study (Fig. 6 ). Samples H5-V6-R2-J/NIG/2021, H2-V7-HFS-J/NIG/2021, H5-V6-C3-J/NIG/2021 and H5-V6-HFS-J/NIG/2021 were all 99.7–100% identical to NIG/9/2021 (accession no. PP101561), whereas sample P7-21/KK-R2/NIG/2021 was 100% identical to NIG/15/2021 (accession no. PP101563) (Fig. 6 ). The VP1 sequences for the three environmental samples from the longitudinal study (two taken from household H5 in Jos South in September and one taken from household H2 in Jos South in October) were identical. The VP1 sequence for the serum sample taken from a bovine at household H5 in Jos South in September differed from the environmental samples taken at the same location. Finally, the VP1 sequence for the environmental sample collected at the outbreak in Kanke in December (Table 4 ) was more distantly related to those collected during the longitudinal study. Discussion We conducted a longitudinal study to investigate the role that small ruminants and contaminated environments have on the endemicity of FMD, and the implications for disease transmission, surveillance and control. We found that seropositivity varied amongst households and animal species, with a higher proportion of NSP ELISA positive samples in cattle, followed by sheep, and lowest in goats (Fig. 4 ), which is aligned with findings in previous studies [ 5 , 6 , 9 , 41 , 42 ]. However, the seroprevalence did not change significantly over the study period (Fig. 2 ). When the age-seroprevalence data were used to estimate the force of infection, we found that it was significantly lower in goats compared to sheep and cattle. By contrast, the force of infection was similar in sheep and cattle, suggesting sheep might play an important role in FMD transmission. The model used to estimate the force of infection assumes that FMDV is at equilibrium, which is reasonable given that the virus is endemic in the region. Although the strains and serotypes circulating have changed over time, one or more strains have been present in the study region [ 7 , 8 , 14 ]. Furthermore, because detection of antibodies against FMDV NSP reflects historic infection with any strain or serotype, it also means the force of infection is essentially related to an animal's first exposure to FMDV, which may not be recent. The force of infection inferred from age-seroprevalence data can be used to estimate the basic reproduction number ( R 0 ) by multiplying it by the mean lifespan of an animal [ 35 ]. This yield estimates for R 0 for each household of between 1.00 and 2.31 for cattle and 0.39 and 0.89 for sheep in each household (Table 3 ), with the differences in R 0 between species mostly a result of differences in longevity (mean of five and two years for cattle and sheep, respectively). The estimates of R 0 for cattle are similar to those reported based on age-seroprevalence data from other endemic areas, for example, Ethiopia [ 43 ], but are much lower than is often reported for outbreaks in disease-free settings [ 44 , 45 ]. In addition to using age-seroprevalence data we also estimated R 0 for individual outbreaks based on rRT-PCR data, which measures only recent infection with any strain or serotype (for the target of the PCR used in this study). To do this we assumed the proportion of PCR positive cattle and/or sheep gave an estimate for the final size of the outbreak. For the four outbreaks in the earlier part of the study, this is reasonable as there was at least a three-month gap before other PCR positive animals were detected. For the six outbreaks that occurred later in the study, this may not be the case as PCR positive animals were detected when the last samples were taken in October. If the outbreaks had not finished, we would underestimate the final size and, hence, R 0 . Bearing this caveat in mind, the estimates for the basic reproduction number were mostly below one for both cattle and sheep, though the 95% credible intervals included the threshold at R 0 = 1 (Table 3 ). An exception to this was the outbreak in Kanke for which R 0 in sheep was estimated to be 2.05 (95% CI: 0.51–4.40) (Table 3 ). The estimates for R 0 are lower those obtained using outbreak data from Ethiopia [ 46 ], but are similar to those obtained using outbreak data from Thailand [ 47 ]. Our results have found little difference in the transmission of FMDV by sheep compared with by cattle, at least in our study setting. Only one study has quantified transmission from infected sheep to cattle under controlled conditions [ 48 ], which found the basic reproduction number of transmission from sheep to cattle (1.4) was similar to that for transmission from sheep to sheep (1.1), but was lower than that for transmission cattle to cattle. Further studies are needed to quantify transmission from small ruminants to other susceptible species to understand differences at host level. The sheep and goats sampled in households were kept in the same premises and potentially had a similar level of exposure to FMDV, although anecdotal observations suggest that sheep and goats are kept separately, with sheep maintained in closer contact with cattle. A limitation of this study is that we did not collect detailed information regarding management practices. Further studies, with more detailed information about the management practices used in different geographic areas and production systems, are needed to better understand the extent to which this plays a role on the differences observed between seroconversion in sheep and goats and force of infection. We also evaluated different sampling and testing methods in order to identify those which are reliable and convenient for surveillance in endemic settings. All oral swabs positive for FMDV RNA were collected during a month in which FMD outbreaks were reported (either in nearby locations or in one of the households that was part of the study). In contrast, no positive oral swabs were obtained during the months in which FMD outbreaks were not reported. These data suggest that FMDV positive oral swabs, as tested by rRT-PCR, are a good indicator of ongoing infection in a geographic region. Detecting FMDV RNA in serum was also evaluated as an alternative method. However, FMDV RNA in blood was found a few days after clinical signs were present and therefore it may be that animals recently infected were missed. However, FMDV RNA was detected in blood samples after the clinical window, demonstrating animals can still be infected after this time and clinical evaluation alone is a poor indicator of the presence of FMDV as viraemia is transient. In addition, this study showed that environmental swabs are a good herd level indicator of FMDV circulating in the area. FMDV RNA was detected in environmental samples in all households that reported an outbreak. In addition, premises where FMDV RNA was detected in environmental samples as part of the longitudinal study had at least one animal in which FMDV RNA was detected in serum samples and/or oral swabs. This is aligned with previous studies that showed that environmental sampling can detect FMDV in a herd more quickly than clinical inspection if sufficient samples are collected often enough [ 20 ]. To increase the likelihood of detection of FMDV in the environment, it is important to sample at areas likely to have come into contact with secretions or excretions from infected animals. Our results suggest that good targets are boots, ropes, pegs, transport vehicles or hard floor surfaces. Fragments of FMDV RNA may survive in the environment for prolonged periods of time [ 49 ], although persistence in the environment is likely to exhibit some seasonality based on changes in relative humidity and temperature [ 19 ]. As survival and persistence times for FMDV RNA in this environment are unknown, it is difficult to attribute positive samples to a particular outbreak in an endemic setting. However, our results suggest that the number of positive environmental swabs does match well with the number of positive oral swabs from animals. This suggests that an increase in the number of positive samples would be indicative of virus circulating in a region. Our study showed that VP1 sequences could be obtained from environmental samples (including hard floor and ropes) with low C T values (between 20.9 and 25.3). However, the RNA derived from environmental samples is likely to be fragmented and difficult to sequence by conventional methods. The incorporation of a probe enrichment step in a next generation sequencing library workflow enabled the recovery of viral genomic data from poor quality samples, as described previously [ 24 ]. When comparing the sequences obtained from environmental samples with sequences from other samples collected during outbreaks in nearby areas over the same time period as the study, we found that sequences obtained from environmental samples were closely related to those submitted from clinical samples. These findings confirm environmental sampling can be used as a surveillance method in endemic settings, especially when combined with sequencing. Conclusions This study sheds new light on the role of small ruminants on the epidemiology and endemicity of FMD. In addition, this study demonstrates the utility of oral and environmental swabs as suitable sampling methods for the early detection of FMDV infection at the animal and herd level, respectively. Furthermore, when combined with sequencing they allow for outbreak tracing. Declarations Author contributions SG, FR, ABL, CC, GL and DOE conceptualised and planned the study. YW, OO, AJA, SII, RBA, MOO, MS and BBD implemented field study, collected and processed samples. EB, HH, BAW, JW, NK, CC and AES tested the samples collected. SG, NK AES and GL conducted data analysis. SG, DPK, ABL, CC, AES, GL, DOE contributed to the interpretation of results. SG and GL drafted the first version of the manuscript. All authors read and approved the final manuscript. Funding This project was funded by the European Commission for the Control of Foot-and-Mouth Disease (EuFMD) through their Fund for Applied Research 8th and 9th Calls. The authors acknowledge additional support from the Department for Environment, Food and Rural Affairs (grant codes SE2722, SE2723 and SE2945) and UKRI Biotechnology and Biological Sciences Research Council (grant codes BBS/E/PI/230002C and BBS/E/PI/23NB0004). Availability of data The data from the current study are available in Additional file 12. Ethical approval Ethical approval for the study was obtained from the Animal Welfare Ethical Review Board (AWERB) of The Pirbright Institute. Conflict of interest The authors declare they have no competing interests. Acknowledgements The authors are grateful to the livestock keepers for allowing us to sample their animals and households. We thank Etienne Chevanne for providing helpful comments during the internal review process of the European Commission for the Control of Foot-and-Mouth Disease. References Knowles NJ, Samuel AR (2003) Molecular epidemiology of foot-and-mouth disease virus. Virus Res 91(1):65–80 Barnett PV, Cox SJ (1999) The role of small ruminants in the epidemiology and transmission of foot-and-mouth disease. Vet J 158(1):6–13 Kitching RP, Hughes GJ (2002) Clinical variation in foot and mouth disease: sheep and goats. Rev sci tech Off int Epiz 21(3):505–512 FAOSTAT (2021) FAOSTAT Crop and livestock products. Food and Agricultural Organization of the United Nations. https://www.fao.org/faostat/en/#data/QCL Begovoeva M, Ehizibolo DO, Adedeji AJ, Oguche MO, Oyekan O, Ijoma SI, Atai RB, Wungak Y, Dogonyaro BB, Lazarus DD et al (2023) Factors associated with foot-and-mouth disease seroprevalence in small ruminants and identification of hot-spot areas in northern Nigeria. Prev Vet Med 212:105842 Ehizibolo DO, De Vleeschauwer AR, Haegeman A, Lefebvre D, Nwosuh CI, Umoh JU, Okolocha EC, Kazeem HM, Van Borm S, De Clercq K (2019) Serological and molecular epidemiology of foot-and-mouth disease viruses in agro-pastoralist livestock herds in the kachia grazing reserve, Nigeria. Transbound Emerg Dis 66(4):1575–1586 Ehizibolo DO, Haegeman A, De Vleeschauwer AR, Umoh JU, Kazeem HM, Okolocha EC, Van Borm S, De Clercq K (2017) Detection and Molecular Characterization of Foot and Mouth Disease Viruses from Outbreaks in Some States of Northern Nigeria 2013–2015. Transbound Emerg Dis 64(6):1979–1990 Ehizibolo DO, Perez AM, Carrillo C, Pauszek S, AlKhamis M, Ajogi I, Umoh JU, Kazeem HM, Ehizibolo PO, Fabian A et al (2014) Epidemiological analysis, serological prevalence and genotypic analysis of foot-and-mouth disease in Nigeria 2008–2009. Transbound Emerg Dis 61(6):500–510 Lazarus DD, Schielen WJG, Wungak Y, Kwange D, Fasina FO (2012) Sero-epidemiology of foot-and-mouth disease in some Border States of Nigeria. Afr J Microbiol Res 6(8):1756–1761 Ehizibolo DO, Haegeman A, De Vleeschauwer AR, Umoh JU, Kazeem HM, Okolocha EC, Van Borm S, De Clercq K (2017) Foot-and-mouth disease virus serotype SAT1 in cattle, Nigeria. Transbound Emerg Dis 64(3):683–690 Fomenky B, Hole K, Ularamu H, Wungak Y, Ehizibolo D, Nebroski M, Kruczkiewicz P, Buchanan C, Lung O, Nfon C (2021) Molecular Characterization of Southern African Territories 2 (SAT2) Serotype of Foot-and-Mouth Disease Virus from Nigeria in 2017 to 2018. Microbiol Resour Announc 10(27):e0036221 Ularamu HG, Ibu JO, Wood BA, Abenga JN, Lazarus DD, Wungak YS, Knowles NJ, Wadsworth J, Mioulet V, King DP et al (2017) Characterization of Foot-and-Mouth Disease Viruses Collected in Nigeria Between 2007 and 2014: Evidence for Epidemiological Links Between West and East Africa. Transbound Emerg Dis 64(6):1867–1876 Ehizibolo DO, Fish IH, Brito B, Bertram MR, Ardo A, Ularamu HG, Lazarus DD, Wungak YS, Nwosuh CI, Smoliga GR et al (2020) Characterization of transboundary foot-and-mouth disease viruses in Nigeria and Cameroon during 2016. Transbound Emerg Dis 67(3):1257–1270 Ularamu HG, Lefebvre DJ, Haegeman A, Wungak YS, Ehizibolo DO, Lazarus DD, De Vleeschauwer AR, De Clercq K (2020) Complex Circulation of Foot-and-Mouth Disease Virus in Cattle in Nigeria. Front Vet Sci 7:466 Buckle K, Bueno R, McFadden A, van Andel M, Spence R, Hamill C, Roe W, Vallee E, Castillo-Alcala F, Abila R et al (2021) Detection of Foot-and-Mouth Disease Virus in the Absence of Clinical Disease in Cattle and Buffalo in South East Asia. Front Vet Sci 8:691308 Stenfeldt C, Lohse L, Belsham GJ (2013) The comparative utility of oral swabs and probang samples for detection of foot-and-mouth disease virus infection in cattle and pigs. Vet Microbiol 162(2–4):330–337 Colenutt C, Brown E, Nelson N, Wadsworth J, Maud J, Adhikari B, Chapagain Kafle S, Upadhyaya M, Kafle Pandey S, Paton DJ et al (2018) Environmental Sampling as a Low-Technology Method for Surveillance of Foot-and-Mouth Disease Virus in an Area of Endemicity. Appl Environ Microbiol 84(16) Colenutt C, Brown E, Paton DJ, Mahapatra M, Parida S, Nelson N, Maud J, Motta P, Sumption K, Adhikari B et al (2022) Environmental sampling for the detection of foot-and-mouth disease virus and peste des petits ruminants virus in a live goat market, Nepal. Transbound Emerg Dis 69(5):3041–3046 Mielke SR, Lendzele S, Delgado AH, Abdoulmoumini M, Dickmu S, Garabed R (2023) Patterns of foot-and-mouth disease virus detection in environmental samples in an endemic setting. Front Vet Sci 10:1157538 Ellis J, Brown E, Colenutt C, Gubbins S (2023) Assessing the effectiveness of environmental sampling for surveillance of foot-and-mouth disease virus in a cattle herd. Front Vet Sci 10:1074264 EuFMD WRL Foot-and-Mouth disease October-December 2020 quartely report. In: FAST reports - Foot-and-mouth And Similar Transboundary (FAST) animal diseases 2020 Callahan JD, Brown F, Osorio FA, Sur JH, Kramer E, Long GW, Lubroth J, Ellis SJ, Shoulars KS, Gaffney KL et al (2002) Use of a portable real-time reverse transcriptase-polymerase chain reaction assay for rapid detection of foot-and-mouth disease virus. J Am Vet Med Assoc 220(11):1636–1642 Logan G, Freimanis GL, King DJ, Valdazo-Gonzalez B, Bachanek-Bankowska K, Sanderson ND, Knowles NJ, King DP, Cottam EM (2014) A universal protocol to generate consensus level genome sequences for foot-and-mouth disease virus and other positive-sense polyadenylated RNA viruses using the Illumina MiSeq. BMC Genomics 15(1):828 Colenutt C, Shaw A, Esemu S, Kfusib A, Besong W, Brown E, Wadsworth J, Knowles N, King D, Ndip L et al Characterising foot-and-mouth disease viruses circulating in Cameroon using environmental sampling at livestock markets and abattoirs. submitted Tamura K (1992) Estimation of the number of nucleotide substitutions when there are strong transition-transversion and G + C-content biases. Mol Biol Evol 9:678–687 Kumar S, Stecher G, Tamura K (2016) MEGA7: Molecular Evolutionary Genetics Analysis version 7.0 for bigger datasets. Mol Biol Evol 33:1870–1874 Bodenham RF, Mazeri S, Cleaveland S, Crump JA, Fasina FO, de Glanville WA, Haydon DT, Kazwala RR, Kibona TJ, Maro VP et al (2021) Latent class evaluation of the performance of serological tests for exposure to Brucella spp. in cattle, sheep, and goats in Tanzania. PLoS Negl Trop Dis 15(8):e0009630 Plummer M (2003) A program for analysis of Bayesian graphical models using Gibbs sampling. In: Proc 3rd Int Work Distrib Stat Comput Plummer M, Stukalov A, Denwood M (2019) rjags: Bayesian grafical models using MCMC. In, 4–9 edn R Core Team (2024) R: A language and Environment for Statistical Computing In., vol. Version 4.4.0 Delignette-Muller ML, Dutang C (2015) fitdistrplus: An R Package for Fitting Distributions. J Stat Softw 64(4) Stevenson M, Sergeant E epiR: Tools for the Analysis of Epidemiological Data. R package version 2062 2023 Spiegelhalter DJ, Best NG, Carlin BP, van der Linde A (2002) Bayesian measures of model complexity and fit (with discussion). J Royal Stat Soc Ser B 64:583–639 Hens N, Aerts M, Faes C, Shkedy Z, Lejeune O, Van Damme P, Beutels P (2010) Seventy-five years of estimating the force of infection from current status data. Epidemiol Infect 138(6):802–812 Anderson RM, May RM (1991) Infectious diseases of humans. Oxford University Press, Oxford UK Gelman A, Carlin JB, Stern HS, Rubin DB (2004) Bayesian data analysis, 2nd edn. Chapman Hall/CRC, Boca Raton, FL, U.S.A. Keeling M, Rohani P (2008) Modeling infectious diseases in humans and animals. Princeton University Press, Princeton, NJ Haario H, Saksman E, Tamminen J (2001) An adaptive Metropolis algorithm. Bernoulli 7:223–242 Andrieu C, Thoms J (2008) A tutorial on adaptive MCMC. Stats Comput 18:343–373 Plummer M, Best N, Cowles K, Vines K (2006) Diagnosis and Output Analysis for MCMC. R News 6:7–11 Beyene B, Tolosa T, Rufael T, Hailu B, Teklue T (2015) Foot and mouth disease in selected districts of western Ethiopia: seroprevalence and associated risk factors. Rev Sci Tech 34(3):939–952 Mesfine M, Nigatu S, Belayneh N, Jemberu WT (2019) Sero-Epidemiology of Foot and Mouth Disease in Domestic Ruminants in Amhara Region, Ethiopia. Front Vet Sci 6:130 Belayneh N, Molla W, Mesfine M, Jemberu WT (2020) Modeling the transmission dynamics of foot and mouth disease in Amhara region, Ethiopia. Prev Vet Med 181:104673 Woolhouse ME, Haydon DT, Pearson A, Kitching RP (1996) Failure of vaccination to prevent outbreaks of foot-and-mouth disease. Epidemiol Infect 116(3):363–371 Chis Ster I, Dodd PJ, Ferguson NM (2012) Within-farm transmission dynamics of foot and mouth disease as revealed by the 2001 epidemic in Great Britain. Epidemics 4(3):158–169 Tadesse B, Molla W, Mengsitu A, Jemberu WT (2019) Transmission dynamics of foot and mouth disease in selected outbreak areas of northwest Ethiopia. Epidemiol Infect 147:e189 Arjkumpa O, Picasso-Risso C, Perez A, Punyapornwithaya V (2021) Subdistrict-Level Reproductive Number for Foot and Mouth Disease in Cattle in Northern Thailand. Front Vet Sci 8:757132 de Bravo C, de Jong MC, Eble PL, Dekker A (2014) Estimation of the transmission of foot-and-mouth disease virus from infected sheep to cattle. Vet Res 45(1):58 Bartley LM, Donnelly CA, Anderson RM (2002) Review of foot-and-mouth disease virus survival in animal excretions and on fomites. Vet Rec 151(22):667–669 Additional Declarations The authors declare no competing interests. Supplementary Files SupplementarymaterialResearchSquare.docx Cite Share Download PDF Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4312238","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":294908770,"identity":"45eeb010-51b0-43f1-a290-228e31bf0dc6","order_by":0,"name":"Simon Gubbins","email":"","orcid":"","institution":"The Pirbright Institute","correspondingAuthor":false,"prefix":"","firstName":"Simon","middleName":"","lastName":"Gubbins","suffix":""},{"id":294908771,"identity":"b14206ec-a2d8-474c-bcd1-bac393298704","order_by":1,"name":"Emma Brown","email":"","orcid":"","institution":"The Pirbright Institute","correspondingAuthor":false,"prefix":"","firstName":"Emma","middleName":"","lastName":"Brown","suffix":""},{"id":294908772,"identity":"ff7519ec-e74d-4cde-9bec-e46ff49bb0f1","order_by":2,"name":"Yiltawe Wungak","email":"","orcid":"","institution":"National Veterinary Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Yiltawe","middleName":"","lastName":"Wungak","suffix":""},{"id":294908773,"identity":"f00d63b2-d403-4725-bbaa-5d9d44363c0c","order_by":3,"name":"Olumuyiwa Oyekan","email":"","orcid":"","institution":"National Veterinary Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Olumuyiwa","middleName":"","lastName":"Oyekan","suffix":""},{"id":294908774,"identity":"79532149-c0ff-4545-aaeb-e2c4fb1c03d5","order_by":4,"name":"Adeyinka J. Adedeji","email":"","orcid":"","institution":"National Veterinary Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Adeyinka","middleName":"J.","lastName":"Adedeji","suffix":""},{"id":294908775,"identity":"5125b6c1-00fa-4128-a27d-261af8b18914","order_by":5,"name":"Sandra I. Ijoma","email":"","orcid":"","institution":"National Veterinary Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Sandra","middleName":"I.","lastName":"Ijoma","suffix":""},{"id":294908776,"identity":"141b7908-5c37-4ed2-994a-921792b622bb","order_by":6,"name":"Rebecca B. Atai","email":"","orcid":"","institution":"National Veterinary Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Rebecca","middleName":"B.","lastName":"Atai","suffix":""},{"id":294908777,"identity":"7725cb9d-a9a9-4f90-8c9a-f70aec405aff","order_by":7,"name":"Moses O. Oguche","email":"","orcid":"","institution":"National Veterinary Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Moses","middleName":"O.","lastName":"Oguche","suffix":""},{"id":294908778,"identity":"9a9b5118-87a7-43c9-930f-984f67275ca2","order_by":8,"name":"Mark Samson","email":"","orcid":"","institution":"National Veterinary Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Mark","middleName":"","lastName":"Samson","suffix":""},{"id":294908779,"identity":"b5329393-7b3d-4455-b18b-09f8580e91eb","order_by":9,"name":"Banenat B. Dogonyaro","email":"","orcid":"","institution":"National Veterinary Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Banenat","middleName":"B.","lastName":"Dogonyaro","suffix":""},{"id":294908780,"identity":"eaf56e59-d924-4dc6-bb42-645f1dc6f7b9","order_by":10,"name":"Fabrizio Rosso","email":"","orcid":"","institution":"European Commission for the Control of Foot-and-Mouth Disease, Food and Agriculture Organisation of the United Nations","correspondingAuthor":false,"prefix":"","firstName":"Fabrizio","middleName":"","lastName":"Rosso","suffix":""},{"id":294908781,"identity":"238d7d87-0833-44de-ae2c-71aafc395d23","order_by":11,"name":"Hayley Hicks","email":"","orcid":"","institution":"The Pirbright Institute","correspondingAuthor":false,"prefix":"","firstName":"Hayley","middleName":"","lastName":"Hicks","suffix":""},{"id":294908782,"identity":"ac174b02-bec2-4ecb-9cbf-677850afd867","order_by":12,"name":"Britta A. Wood","email":"","orcid":"","institution":"The Pirbright Institute","correspondingAuthor":false,"prefix":"","firstName":"Britta","middleName":"A.","lastName":"Wood","suffix":""},{"id":294908783,"identity":"64b4feec-f44f-4656-848e-d2d15097d9ee","order_by":13,"name":"Jemma Wadsworth","email":"","orcid":"","institution":"The Pirbright Institute","correspondingAuthor":false,"prefix":"","firstName":"Jemma","middleName":"","lastName":"Wadsworth","suffix":""},{"id":294908784,"identity":"90e6d88b-3a96-48c1-bbca-d98945f99e52","order_by":14,"name":"Nick Knowles","email":"","orcid":"","institution":"The Pirbright Institute","correspondingAuthor":false,"prefix":"","firstName":"Nick","middleName":"","lastName":"Knowles","suffix":""},{"id":294908785,"identity":"5332ef42-45d3-4250-94fd-5b2799089bc3","order_by":15,"name":"Donald P. King","email":"","orcid":"","institution":"The Pirbright Institute","correspondingAuthor":false,"prefix":"","firstName":"Donald","middleName":"P.","lastName":"King","suffix":""},{"id":294908786,"identity":"a46f456a-36b3-41d3-917c-77761afbe977","order_by":16,"name":"Anna B. Ludi","email":"","orcid":"","institution":"The Pirbright Institute","correspondingAuthor":false,"prefix":"","firstName":"Anna","middleName":"B.","lastName":"Ludi","suffix":""},{"id":294908787,"identity":"31bd0fdd-3685-41ba-ad60-21388c59bc76","order_by":17,"name":"Claire Colenutt","email":"","orcid":"","institution":"The Pirbright Institute","correspondingAuthor":false,"prefix":"","firstName":"Claire","middleName":"","lastName":"Colenutt","suffix":""},{"id":294908788,"identity":"097a553f-ab54-400a-836d-95fee69172b1","order_by":18,"name":"Andrew E. Shaw","email":"","orcid":"","institution":"The Pirbright Institute","correspondingAuthor":false,"prefix":"","firstName":"Andrew","middleName":"E.","lastName":"Shaw","suffix":""},{"id":294908789,"identity":"b9ac929f-c172-438c-b444-f4e1c54b1520","order_by":19,"name":"Georgina Limon","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0001-6230-2475","institution":"The Pirbright Institute","correspondingAuthor":true,"prefix":"","firstName":"Georgina","middleName":"","lastName":"Limon","suffix":""},{"id":294908790,"identity":"1ab6c553-7737-43b4-b426-ca589dc7eb15","order_by":20,"name":"David O. Ehizibolo","email":"","orcid":"","institution":"National Veterinary Research Institute","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"O.","lastName":"Ehizibolo","suffix":""}],"badges":[],"createdAt":"2024-04-23 13:13:46","currentVersionCode":2,"declarations":{"humanSubjects":false,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-4312238/v2","doiUrl":"https://doi.org/10.21203/rs.3.rs-4312238/v2","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":74598291,"identity":"bfa0a0b1-d7b0-47ea-b1c2-0ceff963b94b","added_by":"auto","created_at":"2025-01-23 20:55:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1007423,"visible":true,"origin":"","legend":"\u003cp\u003eMaps showing the location of: (a) Bassa and Jos South local government areas (study sites) in Plateau State, Nigeria where sampling for foot-and-mouth disease virus was carried out between March and October 2021; and (b) Kanke and Wase local government areas in Plateau State where FMD outbreaks were reported during the study period.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4312238/v2/a85461b0b8f02d3e87996dc4.png"},{"id":74598290,"identity":"06f429a5-26d8-43a3-804b-42fd327d9c7e","added_by":"auto","created_at":"2025-01-23 20:55:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":111262,"visible":true,"origin":"","legend":"\u003cp\u003eLongitudinal oral swab and serum sampling for the presence of foot-and-mouth disease virus (FMDV) in cattle, sheep and goats in northern Nigeria between March and October 2021. FMD viral genome was detected by rRT-PCR (PCR), and antibodies to FMDV non-structural proteins (NSP) were detected by ELISA. Sera and oral swabs were paired (i.e. collected from the same animal). Samples were collected from animals at households (H1-H5), one livestock market (LM) and one transhumance location (T) in both Bassa and Jos South local government areas. Coloured shading indicates the proportion (%) of positive samples; grey shading indicates that no samples were collected. Note: households sampled in March are not the same as those sampled in April-October.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4312238/v2/f69fe8f5d62cda419c8a878e.png"},{"id":74598637,"identity":"ed0b4380-f2b3-4849-ac12-4ad13041f443","added_by":"auto","created_at":"2025-01-23 21:11:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":39551,"visible":true,"origin":"","legend":"\u003cp\u003eLongitudinal environmental sampling for the presence of foot-and-mouth disease virus (FMDV) in northern Nigeria between March and October 2021. Samples collected from households (H1-H5), one livestock market (LM) and one transhumance location (T) in both Bassa and Jos South local government areas. Coloured shading indicates the proportion (%) of samples positive for FMDV RNA by rRT-PCR; grey shading indicates that no samples were collected. Note: households sampled in March are not the same as those sampled in April-October.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4312238/v2/a5538b10920b568a858f695a.png"},{"id":74598326,"identity":"7ee9384a-a29e-40e1-9734-59541b7e1427","added_by":"auto","created_at":"2025-01-23 21:03:36","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":122930,"visible":true,"origin":"","legend":"\u003cp\u003eProbability of a sample being positive for foot-and-mouth disease virus (FMDV) RNA by rRT-PCR or antibodies against FMDV non-structural proteins (NSP) for samples collected during a longitudinal study in Bassa and Jos South local government areas between April and October 2021. Sample types tested by rRT-PCR were serum (S, red), oral swabs (O, blue) and environmental swabs (E, magenta). Samples tested by NSP ELISA were from cattle (green), sheep (cyan) or goats (yellow). Violin plots show the posterior median (circle), interquartile range (line) and density (shape) for the probability.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4312238/v2/22f5c03653fc37758a6dd20e.png"},{"id":74598327,"identity":"def2e0ac-4ac1-43a6-9af6-f379a74c0fd1","added_by":"auto","created_at":"2025-01-23 21:03:36","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":224797,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between age and seroprevalence for foot-and-mouth disease virus (FMDV) in cattle and sheep, by household, in Bassa and Jos South local government areas. Each plot shows the observed seroprevalence (i.e. proportion (%) of animals positive for antibodies to FMDV non-structural proteins by ELISA) at each age (triangles) and the expected seroprevalence based on the estimated force of infection (posterior median: black line; 95% credible region: coloured shading) for cattle (green) and sheep (cyan).\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4312238/v2/9da0bb73a86a2cc32299b012.png"},{"id":74598297,"identity":"fbd28e5d-11b0-4973-831b-055fa5447a15","added_by":"auto","created_at":"2025-01-23 20:55:36","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":687423,"visible":true,"origin":"","legend":"\u003cp\u003ePhylogenetic tree showing the relationship amongst foot-and-mouth disease virus VP1 sequences generated from five samples collected during the present longitudinal study in northern Nigeria in 2021 (highlighted in yellow) and other sequences in the FAO World Reference Laboratory for FMD database. An asterisk (*) indicates virus designations not assigned by the WRLFMD.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4312238/v2/52cb0a5db7784621ea68a5e0.png"},{"id":74598692,"identity":"47a68c3f-d653-4d40-b377-21705d72f74c","added_by":"auto","created_at":"2025-01-23 21:19:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3600874,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4312238/v2/94238181-5503-413b-a423-c2f3e8a0bcf0.pdf"},{"id":74598293,"identity":"bfdb2d6e-f850-415c-bb7e-7d59e21282d4","added_by":"auto","created_at":"2025-01-23 20:55:36","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1372709,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementarymaterialResearchSquare.docx","url":"https://assets-eu.researchsquare.com/files/rs-4312238/v2/5946214409cba867b3ddfe65.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"Longitudinal study of foot-and-mouth disease virus in Northern Nigeria: implications for the roles of small ruminants and environmental contamination in endemic settings","fulltext":[{"header":"Introduction","content":"\u003cp\u003eFoot-and-mouth disease virus (FMDV) is an RNA virus (family \u003cem\u003ePicornaviridae\u003c/em\u003e, genus \u003cem\u003eAphthovirus\u003c/em\u003e) characterised by high genetic and antigenic heterogeneity [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. FMDV is the causative agent of foot-and-mouth disease (FMD), a highly contagious disease affecting wild and domestic cloven-hoofed ungulates. Globally, small ruminants (sheep and goats) represent the largest population of FMD susceptible domestic livestock. Although small ruminants can act as silent shedders of FMDV, their role in the epidemiology of FMD is generally neglected and poorly understood, partly due to the inapparent nature of clinical disease in these hosts [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Vesicular signs in small ruminants are often subtle and less apparent than those observed in cattle or are absent [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Furthermore, common clinical signs of FMD in small ruminants (lameness, fever, depression) are not disease specific.\u003c/p\u003e \u003cp\u003eIn Nigeria, small ruminants account for 84.5% of total grazing domestic livestock (48.6\u0026nbsp;million sheep and 76.3\u0026nbsp;million goats) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Sheep and goats are mainly of indigenous breeds and largely kept in mixed farming with cattle by pastoralist and subsistence farmers, predominantly in northern parts of the country. FMD is endemic in Nigeria and, as in other endemic countries, outbreaks are reported mainly in cattle. However, FMDV RNA has been detected in sera and epithelial tissues, and evidence of antibodies against FMDV non-structural proteins (NSP) antibodies has been reported in sheep and goats [\u003cspan additionalcitationids=\"CR6 CR7 CR8\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Higher seroprevalence has been reported in sheep compared to goats [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], and lower seroprevalence in sheep compared to cattle [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Serotypes O, A, SAT 1 and SAT 2 have been isolated and characterised from cattle samples in Nigeria [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. These four serotypes belong to diverse topotypes or genotypes and are phylogenetically related to strains circulating in West, Central and Northern Africa [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eVirus isolation from clinically affected animals is considered the gold standard to confirm that animals are infected with FMDV. This is often followed by antigen ELISA and sequencing of viral genomes to determine the FMDV serotype and topotype circulating in the area. However, these methods have specific laboratory requirements, and depend on farmers or field vets to identify and correctly sample clinically affected animals. This is particularly challenging in endemic countries with scarce resources and where multiple FMDV serotypes circulate. Therefore, alternative, yet reliable, methods are needed to efficiently identify infected animals in a timely manner, conduct outbreak investigations and characterise the circulating serotype. Oral and nasal swabs have been suggested as an alternative method to recover viral RNA from infected and clinically healthy animals [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. These have the advantages of being less invasive compared to probang sampling and allow the detection of infected animals prior to the appearance of clinical signs and/or when clinical signs are not noticeable.\u003c/p\u003e \u003cp\u003eEnvironmental sampling (i.e., taking swabs of any surfaces likely to have been contaminated by secretions and excretions of infected animals) also presents an opportunity for non-invasive sample collection, enabling FMD surveillance at herd level and beyond regular investigation of clinical cases [\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. This is of particular value in places where sampling individual animals is challenging or not feasible. In addition, environmental sampling presents an alternative to the collection of clinical samples, potentially allowing FMDV detection at herd level faster [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], is less stressful for the animals, and potentially more cost effective. Combining recovery of viral RNA from contaminated surfaces with sequencing of viral genomes allows for strain identification and outbreak tracing.\u003c/p\u003e \u003cp\u003eThe aims of this study were (i) to enhance our understanding of the role of small ruminants and environmental contamination in the maintenance of FMD, and (ii) to identify reliable and convenient sampling methods for surveillance in endemic settings.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy period and location\u003c/h2\u003e \u003cp\u003eSamples were collected once a month from March 2021 to October 2021, apart from August 2021 when sampling was not possible for security reasons. Samples were collected from five households, one livestock market and one transhumance location in both Bassa and Jos South local government areas (LGAs) in Plateau State in northern Nigeria (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). These LGAs had been identified as being at high risk of FMD based on serological testing of samples from small ruminants [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] and FMD outbreaks reported in 2020 [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. To be eligible for recruitment, households had to raise both cattle and small ruminants (sheep and/or goats) and agree to participate in the study. Eligible households were identified and selected with input from local contacts in each LGA. All were subsistence farmers keeping mixed herds with indigenous or mixed breeds, often taking animals to communal grazing and water points during the day. Participation was voluntary and no incentives were given to take part in the study. Transhumance locations were defined as a location where herders settle for up to two weeks before continuing their journey. There is only one livestock market and transhumance site in Jos South. Livestock markets and transhumance sites in Bassa were selected based on location, access, and agreement from people in charge to take part in the study. For transhumance sites, animal availability at the time of the sampling was also a factor.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFurther samples were collected, by the same field teams, from households reporting clinical outbreaks in the study area and in neighbouring LGAs during the same study period.\u003c/p\u003e \u003cp\u003eIn both Bassa and Jos South, the households that were sampled changed between the March and April visits, after which samples were taken from the same households on all subsequent visits (i.e. May-October). Results are presented for all households, but statistical analysis was only carried out using the results from samples collected between April and October.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAnimal sampling\u003c/h3\u003e\n\u003cp\u003eDuring the first household visit nine animals (3 sheep, 3 goats and 3 cattle) were selected systematically for sampling. If fewer than three animals of a species were present, the total number was completed by sampling other species. At subsequent visits, the same animals were sampled, if possible, but this was often not the case as some animals were sold or slaughtered during the study period. At livestock markets and transhumance sites, animals were selected at each visit from various locations within the market or site. Each animal sampled was given a unique ID number which was linked to the site where the animal was held during the month of the visit. In addition, the age and sex of animals sampled were collected. Clinical examination was conducted on the animals sampled by a qualified veterinarian in the field team, and animals showing FMD-like lesions on the day of the visit were recorded.\u003c/p\u003e \u003cp\u003eFive millilitres of blood were collected from the jugular vein of each selected animal using pre-labelled vacutainer tubes (Becton Dickinson, USA). A sterile swab stick (SkyHealth, China) was used to swab the oropharyngeal/oral cavity of an animal and immediately put into PBS, as the transport medium. Once an animal had been sampled it was identified with a line in the ear using a non-toxic colour marker to avoid double sampling. All samples were kept at 4\u0026deg;C and sent to the National Veterinary Research Institute (NVRI) in Vom as soon as possible. On arrival at NVRI serum was separated into Eppendorf tubes clearly labelled with the unique animal ID number. Individual serum samples and oral swabs were stored at -20\u0026deg;C and shipped on dry ice to The Pirbright Institute, UK for testing.\u003c/p\u003e\n\u003ch3\u003eEnvironmental sampling\u003c/h3\u003e\n\u003cp\u003eAt each sampling site, electrostatic dust cloths were used to swab areas of the environment where contact with secretions and excretions from infected animals was deemed likely (e.g. food troughs, hard floor surfaces, boots and tether ropes, transport vehicles and herder\u0026rsquo;s sticks). Up to ten environmental samples per site per visit were collected. Each environmental sample was given a unique ID number which was linked to the site, place from which the sample was collected and month of the visit. The environmental samples were processed in the field by eluting the swabs in PBS and then adding the samples directly into lysis buffer (MagMAX Core or RLT buffer, Thermo Fisher Scientific, UK) at a ratio of 1:1. All samples were stored at 4\u0026deg;C and shipped on dry ice to The Pirbright Institute for testing.\u003c/p\u003e\n\u003ch3\u003eSample processing\u003c/h3\u003e\n\u003cp\u003eEnvironmental samples, serum samples and oral swabs were tested for the presence of FMDV RNA by rRT-PCR. Viral RNA was extracted from samples using the KingFisher Flex automated extraction platform (Thermo Fisher Scientific, UK) with the MagMAX\u0026trade; CORE Nucleic Acid Purification Kit (Thermo Fisher Scientific, UK). FMDV RNA was detected by rRT-PCR on the ABI 7500 system (Applied Biosystems, UK) using an assay that targets the 3D region of the FMDV genome (forward: ACTGGGTTTTACAAACCTGTGA, reverse: GCGAGTCCTGCCACGGA, probe: TCCTTTGCACGCCGTGGGAC) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Serum samples were also tested for the presence of antibodies against FMDV non-structural proteins (NSP). Samples were heat inactivated (at 56\u0026deg;C for 30 minutes, using a heat block) before testing with a PrioCHECK FMDV NS Antibody ELISA kit (Thermo Fisher Scientific Prionics AG, Waltham, MA, USA). Kits were used as per the manufacturer\u0026rsquo;s instructions.\u003c/p\u003e\n\u003ch3\u003eSequencing\u003c/h3\u003e\n\u003cp\u003eSelected FMDV-positive samples were subjected to probe-enriched Illumina-based next generation sequencing. The selected samples had a mean rRT-PCR C\u003csub\u003eT\u003c/sub\u003e value of 27.2 (range: 20.9 to 32.9; see Additional file 1 for sample details). First and second strand synthesis of total nucleic acid was performed as described previously using the SuperScript\u0026trade; double-stranded cDNA synthesis kit (ThermoFisher) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Libraries were prepared following the Illumina DNA prep with enrichment protocol. The first stage libraries were pooled and subsequently enriched using a library comprising 26,275 unique biotinylated oligos designed using 622 complete genomes available in GenBank [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Final libraries were diluted and run on the Illumina MiSeq using a V2 300 nano cartridge generating 2 \u0026times; 150 paired-end reads.\u003c/p\u003e \u003cp\u003eReads were initially assembled into contigs using SPADES, and each contig in turn was queried against a database of FMDV sequences to identify FMDV-specific contigs. Each FMDV contig was further subjected to BLAST online to identify the closest related sequence available. The 1D region of each most closely related virus was selected and used as a reference sequence for the reference assembly using BWA-MEM of each sample. Consensus sequences were extracted following reference assembly using VCF tools. The evolutionary history was inferred by using the Maximum Likelihood method based on the Tamura 3-parameter model [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The tree with the highest log likelihood is shown. The percentage of trees in which the associated taxa clustered together is shown next to the branches. Initial tree(s) for the heuristic search were obtained automatically by applying Neighbor-Join and BioNJ algorithms to a matrix of pairwise distances estimated using the Maximum Composite Likelihood (MCL) approach, and then selecting the topology with the superior log likelihood value. A discrete Gamma distribution was used to model evolutionary rate differences among sites (5 categories (+\u0026thinsp;G, parameter\u0026thinsp;=\u0026thinsp;0.5492)). The tree was drawn to scale, with branch lengths measured in the number of substitutions per site. The analysis involved 20 nucleotide sequences. All positions with less than 95% site coverage were eliminated (i.e. fewer than 5% alignment gaps, missing data, and ambiguous bases were allowed at any position). Evolutionary analyses were conducted in MEGA7 [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003eTest performance\u003c/h2\u003e \u003cp\u003eMcNemar's test for paired data and the kappa statistic were used to assess whether the results for oral swabs and serum samples tested by rRT-PCR from the same animals differed significantly from one another. A Bayesian latent class analysis was used to estimate the diagnostic sensitivity (Se) and specificity (Sp), under field conditions, for oral swabs and serum samples tested by rRT-PCR. For this, we adapted a previously published Hui-Walter model [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] implemented using JAGS and \u003cem\u003erjags\u003c/em\u003e R package [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] and R (version 4.4.0) [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Priors for Se and Sp were informed by previously published values (see Additional file 2 for details). A beta distribution with parameters α and β was assumed for the priors, and distribution parameters were estimated using the functions \u003cem\u003efitdist\u003c/em\u003e and \u003cem\u003eepi.betabuster\u003c/em\u003e from the R packages \u003cem\u003efitdistrplus\u003c/em\u003e and \u003cem\u003eepiR\u003c/em\u003e [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Test results were classified as either positive or negative, and animals were classified as originating from two subpopulations: households or livestock markets and transhumance sites.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003eProportion of positive samples\u003c/h3\u003e\n\u003cp\u003eThe proportion of positive samples by rRT-PCR or by ELISA was analysed using a binomial generalised linear mixed model with a logit link function. Because of the marked difference in the number of positive rRT-PCR results between LGAs, data from Bassa and Jos South were analysed in separate models (see Additional file 3 for details of the models). By contrast, the number of positive ELISA results were similar in both LGAs and, accordingly, the ELISA results were analysed in a single model (see Additional file 3 for details of the models). In each model the response variable was whether or not a sample was positive. Explanatory variables considered in the models included the LGA, month the sample was taken, sample type and species as fixed effects, and sampling location as a random effect. Models were implemented in a Bayesian framework using OpenBUGS (version 3.2.3; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.mrc-bsu.cam.ac.uk/software/\u003c/span\u003e\u003cspan address=\"https://www.mrc-bsu.cam.ac.uk/software/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Diffuse priors were used for model parameters: normal with mean 0 and standard deviation 10 for regression coefficients; and exponential with mean 100 for the random effect variance. Two chains of 60,000 iterations were run with the first 10,000 samples discarded to allow for burn-in of the chains. Chains were subsequently thinned by selecting every fifth iteration to reduce autocorrelation amongst the samples. Convergence was monitored visually and using the Gelman-Rubin statistic in OpenBUGS. Models including different explanatory variables were compared using the deviance information criterion (DIC) [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eBasic reproduction number for FMDV\u003c/h2\u003e \u003cp\u003eThe basic reproduction number (\u003cem\u003eR\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e) was estimated in two ways, reflecting what is measured by the diagnostic tests used to infer \u003cem\u003eR\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e. First, we used the NSP ELISA data to estimate the force of infection (i.e. the rate at which susceptible individuals acquire infection) for each household and, hence, \u003cem\u003eR\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e, from age-seroprevalence data. This is not a measure of the first exposure of an animal to FMDV, because NSP ELISA reflects historic exposure to any strain or serotype of FMDV rather than recent seroconversion. Second, we used the rRT-PCR data for serum samples and oral swabs to estimate \u003cem\u003eR\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e for individual outbreaks in households. This reflects the fact that rRT-PCR measures only recent exposure to FMDV, though the PCR target (3D gene of FMDV) used in the present study is not strain or serotype specific.\u003c/p\u003e \u003cp\u003eTo estimate the force of infection for each household from NSP ELISA data, the age and NSP status of each animal (i.e. negative or positive by NSP ELISA) at its first sampling was extracted from the data (see Additional files 4 and 5 for results for individual animals). To simplify the analysis, only home-bred animals (n\u0026thinsp;=\u0026thinsp;306) were included in the analysis; those bought-in to the household (n\u0026thinsp;=\u0026thinsp;18) were excluded. The force of infection was estimated using a catalytic model [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], so that\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:p\\left(a\\right)=1-\\text{exp}\\left(-\\lambda\\:a\\right),$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003ep\u003c/em\u003e(\u003cem\u003ea\u003c/em\u003e) is the seroprevalence at age \u003cem\u003ea\u003c/em\u003e and \u003cem\u003eλ\u003c/em\u003e is the force of infection. The force of infection can then be used to estimate the basic reproduction number, \u003cem\u003eR\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e, using the relationship \u003cem\u003eR\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;\u003cem\u003eλL\u003c/em\u003e where \u003cem\u003eL\u003c/em\u003e is the mean lifespan of an animal [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. For cattle and sheep in the study area, the mean lifespans are 5 and 2 years, respectively.\u003c/p\u003e \u003cp\u003eHeterogeneities in the force of infection were incorporated to allow it to vary amongst species and households/LGAs. In this case, the force of infection for an individual is given by\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:\\text{log}\\lambda\\:=\\alpha\\:+{\\beta\\:}_{SPP}+{\\gamma\\:}_{LGA,HOUSE},$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003eα\u003c/em\u003e is the baseline, \u003cem\u003eβ\u003c/em\u003e is the effect of species and \u003cem\u003eγ\u003c/em\u003e is the effect of LGA and household. Here γ is treated as a random effect and drawn from a normal distribution with mean 0 and standard deviation \u003cem\u003eσ\u003c/em\u003e\u003csub\u003e\u003cem\u003eγ\u003c/em\u003e\u003c/sub\u003e.\u003c/p\u003e \u003cp\u003eParameters in the model were estimated in a Bayesian framework using OpenBUGS (version 2.3.2). A Bernoulli likelihood was used with diffuse priors for model parameters (normal with mean 0 and standard deviation 10 for \u003cem\u003eα\u003c/em\u003e and \u003cem\u003eβ\u003c/em\u003e; exponential with mean 100 for \u003cem\u003eσ\u003c/em\u003e\u003csub\u003e\u003cem\u003eγ\u003c/em\u003e\u003c/sub\u003e). Two chains of 30,000 iterations were run with the first 5,000 samples discarded to allow for burn-in of the chains. Chains were subsequently thinned by selecting every fifth iteration to reduce autocorrelation amongst the samples. Convergence was monitored visually and using the Gelman-Rubin statistic in OpenBUGS. Models including different explanatory variables were compared using the DIC (see Additional file 6 for details of the models). The final model was checked by comparing the observed data to the posterior predictive distribution [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo estimate the basic reproduction number for individual outbreaks, we used the results of testing serum samples and oral swabs by rRT PCR collected from cattle and sheep. An outbreak was defined as any one or two consecutive monthly samplings when at least one sample from the household was positive by rRT-PCR (note: there were no three or more consecutive monthly samplings with positive PCR results). The proportion of animals positive by rRT-PCR was assumed to be an estimate of the final size of the outbreak. The final size of an outbreak, \u003cem\u003ef\u003c/em\u003e, is related to the basic reproduction number, \u003cem\u003eR\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e, by the following equation,\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:1-f-{s}_{0}\\text{exp}\\left(-{R}_{0}f\\right)=0,$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003es\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e is the initial proportion of the household that is susceptible [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eParameters (i.e. \u003cem\u003eR\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e and \u003cem\u003es\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e for each outbreak) were estimated in a Bayesian framework. A binomial likelihood was used for the data, that is,\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\:L=\\prod\\:_{h}{f}_{h}^{{P}_{h}}{(1-{f}_{h})}^{{N}_{h}-{P}_{h}},$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003ef\u003c/em\u003e\u003csub\u003e\u003cem\u003eh\u003c/em\u003e\u003c/sub\u003e is the final size and \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eh\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eN\u003c/em\u003e\u003csub\u003e\u003cem\u003eh\u003c/em\u003e\u003c/sub\u003e are the number of PCR positive animals (based on serum samples and oral swabs) and the number of animals tested, respectively, for outbreak \u003cem\u003eh\u003c/em\u003e. A gamma prior with mean 10 and shape 2 was used for \u003cem\u003eR\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e, while a uniform prior with range [0,1] was used for \u003cem\u003es\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e. Samples from the joint posterior distribution were generated using an adaptive Metropolis algorithm [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], modified so that the scaling factor was tuned during burn-in to ensure an acceptance rate of between 20% and 40% for more efficient sampling of the target distribution [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Two chains of 600,000 iterations were run with the first 100,000 samples discarded to allow for burn-in of the chains. Chains were subsequently thinned by selecting every fiftieth iteration to reduce autocorrelation amongst the samples. The adaptive Metropolis scheme was implemented using Matlab (version 2020b; The Mathworks, Inc.). Convergence was monitored visually and using the Gelman-Rubin statistic in the coda package [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] in R (version 4.4.0) [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The final model was checked by comparing the observed data to the posterior predictive distribution [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo explore differences in \u003cem\u003eR\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e between cattle and sheep for individual outbreaks, we carried out three analyses: one using data for cattle and sheep combined; one using data for cattle only; and one using data for sheep only.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eAnimal sampling for FMDV RNA\u003c/h2\u003e \u003cp\u003eA total of 783 serum samples were collected (591 from animals in households, 126 in livestock markets and 66 from transhumance sites) and tested for FMDV RNA by rRT-PCR (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Serum samples were collected from cattle (n\u0026thinsp;=\u0026thinsp;284; 215 females and 69 males), sheep (n\u0026thinsp;=\u0026thinsp;407; 332 females and 75 males) and goats (n\u0026thinsp;=\u0026thinsp;92; 68 females and 24 males), of which nine (3.2%), nine (2.2%) and one (1.1%) were positive, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; see also Additional files 4 and 5 for results for individual animals). The median age of animals sampled was 5 years (interquartile range (IQR): 3\u0026ndash;5 years) in cattle; 2 years (IQR: 1\u0026ndash;3 years) in sheep; and 2 years (IQR: 1-2.5 years) in goats. Between zero and four (0%-3.2%) serum samples were positive each month, except for October where nine (8.3%) were positive (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Positive samples were collected at households and transhumance locations in both Bassa and Jos South and at the livestock market in Jos South (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\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\u003eSummary of environmental, serum and oral swab samples tested by rRT-PCR for foot-and-mouth disease virus (FMDV) RNA or for antibodies against FMDV non-structural proteins (NSP) by ELISA.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003emonth\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e \u003cp\u003erRT-PCR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eNSP ELISA\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eenvironmental swabs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eoral swabs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eserum samples\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eserum samples\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003etested\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003epositive\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003etested\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003epositive\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003etested\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003epositive\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003etested\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003epositive\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (1.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e55 (61.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApril\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (2.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4 (3.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e72 (57.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (0.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e73 (62.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJune\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e71 (58.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJuly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2 (1.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e56 (50.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAugust*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeptember\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (26.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7 (10.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2 (1.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e51 (47.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOctober\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (5.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9 (8.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e57 (52.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (3.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e424\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9 (2.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e783\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e19 (2.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e435 (55.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e* no sampling was carried out in August for security reasons\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOral swabs were collected from a subset of the animals from which serum samples were taken (n\u0026thinsp;=\u0026thinsp;424) and tested for FMDV RNA by rRT-PCR (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Samples were collected from cattle (n\u0026thinsp;=\u0026thinsp;3; all female), sheep (n\u0026thinsp;=\u0026thinsp;345; 278 females and 67 males) and goats (n\u0026thinsp;=\u0026thinsp;76; 55 females and 21 males), of which zero (0%), seven (2.0%) and two (2.6%) were positive, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; see also Additional files 4 and 5 for results for individual animals). All cattle sampled were 4 years old. The median age for sheep sampled was 2 years (IQR: 1\u0026ndash;3 years), and for goats sampled was 2 years (IQR: 1-2.5 years). None of the oral swabs collected between March and July were positive for FMDV RNA, but nine collected during September and October were positive (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Of these positive samples, one was collected at a household in Bassa (a female goat, 18 months old), while the remaining eight were collected at four (of five) households (all sheep, seven females and one male) and at the livestock market (one female goat, 18 months old) in Jos South (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough there was a no significant difference between the proportion of positives (p\u0026thinsp;\u0026gt;\u0026thinsp;0.9), there was only a slight agreement (kappa\u0026thinsp;=\u0026thinsp;0.08) between serum and oral swabs tested for FMDV RNA (see Additional file 7 for further details). FMDV RNA is detected only for a short period of time (both in serum samples and oral swabs), with a slight difference in timing and short period of overlap. Infected animals are likely to test positive by rRT-PCR in oral samples first, and slightly later in serum (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e) once the infection is systemic. Sensitivity and specificity were estimated (posterior median and 95% credible interval (CI)) to be 0.720 (0.551\u0026ndash;0.855) and 0.980 (0.964\u0026ndash;0.990) for rRT-PCR in serum, and 0.877 (0.780\u0026ndash;0.943) and 0.979 (0.963\u0026ndash;0.993) for rRT-PCR in oral swabs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eClinical signs of FMD\u003c/h2\u003e \u003cp\u003eOne two-year old bovine from household H5 in Jos South showed clinical signs of FMD during the sampling visit in September. A serum sample taken from the animal was positive for viral RNA (C\u003csub\u003eT\u003c/sub\u003e value: 26.6) and negative by NSP ELISA. Two further cattle, one sampled in March (from household H2 in Bassa) and one sampled in October (at the livestock market in Bassa) also showed FMD-like signs, but serum samples from both animals were negative for viral RNA and by NSP ELISA.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eEnvironmental sampling for FMDV RNA\u003c/h2\u003e \u003cp\u003eA total of 458 environmental samples were collected (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). For the samples collected between March and July, only two (0.5%) were positive for FMDV RNA (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e). One positive swab was from a herder\u0026rsquo;s stick at a household in Bassa, and the other was from boots at a transhumance site in Jos South. By contrast, 16 (out of 86; 18.6%) environmental samples collected in September and October were positive for FMDV RNA (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These were all collected in Jos South, at four (of five) households and the livestock market (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Positive samples were collected from boots (n\u0026thinsp;=\u0026thinsp;4), ropes (n\u0026thinsp;=\u0026thinsp;3), pegs (n\u0026thinsp;=\u0026thinsp;3), transport vehicles (n\u0026thinsp;=\u0026thinsp;2) and hard floor surfaces (n\u0026thinsp;=\u0026thinsp;4).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eComparison of animal and environmental sampling for FMDV RNA\u003c/h2\u003e \u003cp\u003eBased on changes in DIC the proportion of positive samples in both regions differed by month and amongst sample types (see Additional file 3 for details of the models and their DICs). Specifically, the proportion of positive samples differed amongst environmental swabs, oral swabs and serum samples, but not with the species the animal samples were collected from. For samples collected in Bassa, the proportion of positive samples was low (posterior median\u0026thinsp;\u0026lt;\u0026thinsp;2.9%) for all months and sample types (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e). For samples collected in Jos South, the proportion of positive samples was low (posterior median\u0026thinsp;\u0026lt;\u0026thinsp;2.6%) for all sample types collected between May and July, and then increased (posterior median\u0026thinsp;\u0026gt;\u0026thinsp;3.5%) for samples collected in September or October (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In September, the proportion of positive samples was highest for environmental swabs, followed by oral swabs, then serum samples (posterior medians of 65.6%, 21.4% and 3.5%, respectively). In October, this pattern changed, with the highest proportion of positive samples in serum samples, followed by environmental swabs and then oral swabs (posterior medians of 16.8%, 5.9% and 5.0%, respectively). This pattern could be related to FMD outbreaks reported in LGAs near to the study area (see below).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eAntibodies to FMDV non-structural proteins (NSP)\u003c/h2\u003e \u003cp\u003eA total of 780 serum samples (283 from cattle, 406 from sheep and 91 from goats) were tested by ELISA for NSP antibodies, of which of which 219 (77.3%), 212 (52.2%) and three (3.3%) were seropositive for cattle, sheep and goats, respectively. Seropositive animals were detected throughout the study period with between 47.2% and 62.4% of animals seropositive each month (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; see also Additional files 4 and 5 for results for individual animals). For animals that were repeatedly sampled at different households, animals that seroconverted for the first time during the study period tend to remain seropositive for the rest of the study period, which is expected for FMDV-NSP antibodies. For those animals that became seronegative during the study period, it was unknown how long they had been seropositive, as they were already seropositive at the time of the first sampling.\u003c/p\u003e \u003cp\u003eBased on changes in DIC the proportion of NSP ELISA positive samples did not differ by month but did differ between regions and amongst species (see Additional file 3 for details of the models compared and their DICs). In both regions, the proportion of NSP ELISA positive samples was highest in cattle, followed by sheep, and lowest in goats (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The proportion of seropositive cattle did not differ between regions (posterior median: 79.4% and 78.9% for Bassa and Jos South, respectively). However, the proportion of seropositive sheep was lower in Jos South compared with Bassa (posterior median: 64.3% and 47.5% for Bassa and Jos South, respectively), while the proportion in goats was lower in Bassa compared with Jos South (posterior median: \u0026lt;0.1% and 13.3% for Bassa and Jos South, respectively).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eBasic reproduction number for FMDV\u003c/h2\u003e \u003cp\u003eSeroprevalence increased with age for both cattle and sheep in all five households sampled in Bassa and Jos South (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Goats were only sampled in two households (H4 and H5) in Bassa, all of which were negative by NSP ELISA (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Changes in DIC show that the corresponding force of infection differed amongst species and amongst households (see Additional file 6 for details of the models compared and their DICs). Posterior predictive checking showed that the model for the seroprevalence data provided an acceptable fit to the data, with all but one observation lying in the 95% prediction intervals (see Additional file 8 for model checking).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe force of infection in goats was significantly lower than that in cattle or sheep (goats relative to cattle (posterior median and 95% CI: 1.8\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e (9.1\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;11\u003c/sup\u003e-0.082); goats relative to sheep: 1.9\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e (1.0\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;10\u003c/sup\u003e-0.085); Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). However, the force of infection in sheep was comparable to that in cattle (that in sheep relative to cattle: 0.96 (0.68\u0026ndash;1.39); Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The force of infection differed amongst households, but there were no systematic differences between the two regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The basic reproduction number (\u003cem\u003eR\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e) for cattle and sheep was calculated from the force of infection by multiplying it by the mean lifespan of the species. This indicated that the basic reproduction number was higher for cattle (median: 1.75; range: 1.00-2.31) than for sheep (median: 0.68; range: 0.39\u0026ndash;0.89) across all ten households (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). For all ten households the median \u003cem\u003eR\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e for cattle was \u0026ge;\u0026thinsp;1 and for four households it was significantly (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u0026thinsp;\u0026ge;\u0026thinsp;1 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). By contrast, the median \u003cem\u003eR\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e for sheep was \u0026lt;\u0026thinsp;1 for all ten households and for four households it was significantly (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u0026thinsp;\u0026lt;\u0026thinsp;1 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\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\u003eEstimated parameters for the force of infection for foot-and-mouth disease virus in households in Northern Nigeria.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eparameter*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eposterior median\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e95% credible limit\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003elower\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eupper\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebaseline (\u003cem\u003eα\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003especies (\u003cem\u003eβ\u003c/em\u003e)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecattle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (baseline)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \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\u003e-0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egoats\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-8.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-23.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehousehold random effect (\u003cem\u003eσ\u003c/em\u003e\u003csub\u003e\u003cem\u003eγ\u003c/em\u003e\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e* note that these parameters and on the natural logarithm scale\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\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\u003eBasic reproduction number (\u003cem\u003eR\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e) for foot-and-mouth disease virus in households in Northern Nigeria.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eregion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ehousehold\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eoutbreak\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003ecattle\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003esheep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003ecattle and sheep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eestimate*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e95% credible limit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eestimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e95% credible limit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eestimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e95% credible limit\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003elower\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eupper\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003elower\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eupper\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003elower\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eupper\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"12\" nameend=\"c12\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePCR results for individual outbreaks\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBassa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eH1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eJuly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.20\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\u003eH2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eJuly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.18\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\u003eH5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eApril\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJos South\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eH1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eApril\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.19\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSep-Oct\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.19\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\u003eH2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSep-Oct\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.26\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\u003eH3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSep-Oct\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.11\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\u003eH4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.19\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSep-Oct\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.10\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\u003eH5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSep-Oct\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKanke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDec\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"12\" nameend=\"c12\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eage-dependent seroprevalence (NSP ELISA)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBassa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eH1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\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\u003eH2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\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\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\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\u003eH3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\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\u003eH4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\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\u003eH5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJos South\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eH1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\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\u003eH2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\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\u003eH3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\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\u003eH4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\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\u003eH5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTen outbreaks were identified during the study period, where an outbreak was defined as any one or two consecutive monthly samplings when at least one sample was positive by rRT-PCR. Three outbreaks occurred in households in Bassa and five in households in Jos South (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Six of the households had a single outbreak, while two (both in Jos South) had two outbreaks each with gaps of three or four months between outbreaks (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The proportion of PCR positive animals was assumed to be a measure of the final size of the outbreak and used to estimate \u003cem\u003eR\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e for each outbreak (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; see Additional file 9 for posterior densities for \u003cem\u003eR\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e for each outbreak). Posterior predictive checking showed that this model provided an acceptable fit to the data, with all observations lying in the 95% prediction intervals for each outbreak (see Additional file 10 for model checking). Across the outbreaks the median estimate for the basic reproduction number was higher for cattle (median: 0.90; range: 0.71\u0026ndash;1.07) than for sheep (median: 0.71; range: 0.61\u0026ndash;1.06) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). However, there was substantial overlap of the posterior distributions, indicating the estimates did not differ significantly between cattle and sheep. For all outbreaks, the 95% credible intervals for \u003cem\u003eR\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e included the threshold at \u003cem\u003eR\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;1. When the results for cattle and sheep were combined, the estimates for \u003cem\u003eR\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e were lower than those for the individual species (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn addition to \u003cem\u003eR\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e, the initial proportion of the household that was susceptible (\u003cem\u003es\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e) was also estimated for each outbreak (see Additional file 11 for posterior densities for \u003cem\u003es\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e for each outbreak). Across the outbreaks the median initial proportion susceptible was the same for cattle (median: 0.82; range: 0.77\u0026ndash;0.88) and sheep (median: 0.88; range: 0.76\u0026ndash;0.93).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eSampling at households with reported outbreaks\u003c/h2\u003e \u003cp\u003eAdditional environmental sampling was also carried out at three households with reported outbreaks in neighbouring LGAs, two in Wase LGA and one in Kanke LGA, both of which are in Plateau State (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). Environmental swabs taken at all three households were positive for FMDV RNA (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). All cattle from the three households had FMD-like lesions in their oral cavities (one bull from household P1, five cattle from household P2, and three cattle from household P7). Tissue samples were collected from five and two cattle from households P2 and P7 respectively, all of which tested positive by rRT-PCR and virus isolation. In addition, serum samples collected from sheep at all three households were positive by NSP ELISA or rRT-PCR (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\u003eSampling carried out at households with reported outbreaks.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eoutbreak\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003emonth sampled\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eenvironmental samples\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003epositive locations\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eanimal samples\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecollected\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003epositive\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWase P1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeptember\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003efence (\u0026times;2), feeding trough, rope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3/3 sheep NSP ELISA positive\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWase P2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeptember\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003efence, rope (\u0026times;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2/3 sheep NSP ELISA positive\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKanke P7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDecember\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003epegs, ropes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5/6 sheep rRT-PCR positive; 3/6 sheep NSP ELISA positive\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u0026dagger;rRT- PCR results are not available for these animals\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cb\u003eSupporting information\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAssuming the 5 (out of 6) samples from sheep positive by rRT-PCR (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) are a measure of the final size for the outbreak in Kanke LGA, the basic reproduction number was estimated to be 2.05 (95% credible interval: 0.51\u0026ndash;4.40) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eSequencing\u003c/h2\u003e \u003cp\u003eEight samples were selected for sequencing using the probe enrichment technique; samples were selected to cover a range of C\u003csub\u003eT\u003c/sub\u003e values, locations and sample types (animal and environmental) (see Additional file 1 for sample details). Sequences for the VP1 region of the FMDV genome were obtained for five of these samples which had C\u003csub\u003eT\u003c/sub\u003e values that ranged between 20.9 and 26.6.\u003c/p\u003e \u003cp\u003eAll five samples for which VP1 sequence data were generated were serotype O, EA-3 topotype. There was a total of 633 positions in the final dataset. VP1 sequences were closely related to other samples collected in Nigeria and submitted to World Reference Laboratory for FMD (WRLFMD) over the same time period as the study (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Samples H5-V6-R2-J/NIG/2021, H2-V7-HFS-J/NIG/2021, H5-V6-C3-J/NIG/2021 and H5-V6-HFS-J/NIG/2021 were all 99.7\u0026ndash;100% identical to NIG/9/2021 (accession no. PP101561), whereas sample P7-21/KK-R2/NIG/2021 was 100% identical to NIG/15/2021 (accession no. PP101563) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe VP1 sequences for the three environmental samples from the longitudinal study (two taken from household H5 in Jos South in September and one taken from household H2 in Jos South in October) were identical. The VP1 sequence for the serum sample taken from a bovine at household H5 in Jos South in September differed from the environmental samples taken at the same location. Finally, the VP1 sequence for the environmental sample collected at the outbreak in Kanke in December (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) was more distantly related to those collected during the longitudinal study.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe conducted a longitudinal study to investigate the role that small ruminants and contaminated environments have on the endemicity of FMD, and the implications for disease transmission, surveillance and control.\u003c/p\u003e \u003cp\u003e We found that seropositivity varied amongst households and animal species, with a higher proportion of NSP ELISA positive samples in cattle, followed by sheep, and lowest in goats (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e), which is aligned with findings in previous studies [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. However, the seroprevalence did not change significantly over the study period (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). When the age-seroprevalence data were used to estimate the force of infection, we found that it was significantly lower in goats compared to sheep and cattle. By contrast, the force of infection was similar in sheep and cattle, suggesting sheep might play an important role in FMD transmission. The model used to estimate the force of infection assumes that FMDV is at equilibrium, which is reasonable given that the virus is endemic in the region. Although the strains and serotypes circulating have changed over time, one or more strains have been present in the study region [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Furthermore, because detection of antibodies against FMDV NSP reflects historic infection with any strain or serotype, it also means the force of infection is essentially related to an animal's first exposure to FMDV, which may not be recent.\u003c/p\u003e \u003cp\u003eThe force of infection inferred from age-seroprevalence data can be used to estimate the basic reproduction number (\u003cem\u003eR\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e) by multiplying it by the mean lifespan of an animal [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. This yield estimates for \u003cem\u003eR\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e for each household of between 1.00 and 2.31 for cattle and 0.39 and 0.89 for sheep in each household (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), with the differences in \u003cem\u003eR\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e between species mostly a result of differences in longevity (mean of five and two years for cattle and sheep, respectively). The estimates of \u003cem\u003eR\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e for cattle are similar to those reported based on age-seroprevalence data from other endemic areas, for example, Ethiopia [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], but are much lower than is often reported for outbreaks in disease-free settings [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn addition to using age-seroprevalence data we also estimated \u003cem\u003eR\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e for individual outbreaks based on rRT-PCR data, which measures only recent infection with any strain or serotype (for the target of the PCR used in this study). To do this we assumed the proportion of PCR positive cattle and/or sheep gave an estimate for the final size of the outbreak. For the four outbreaks in the earlier part of the study, this is reasonable as there was at least a three-month gap before other PCR positive animals were detected. For the six outbreaks that occurred later in the study, this may not be the case as PCR positive animals were detected when the last samples were taken in October. If the outbreaks had not finished, we would underestimate the final size and, hence, \u003cem\u003eR\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e. Bearing this caveat in mind, the estimates for the basic reproduction number were mostly below one for both cattle and sheep, though the 95% credible intervals included the threshold at \u003cem\u003eR\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;1 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). An exception to this was the outbreak in Kanke for which \u003cem\u003eR\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e in sheep was estimated to be 2.05 (95% CI: 0.51\u0026ndash;4.40) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The estimates for \u003cem\u003eR\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e are lower those obtained using outbreak data from Ethiopia [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], but are similar to those obtained using outbreak data from Thailand [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur results have found little difference in the transmission of FMDV by sheep compared with by cattle, at least in our study setting. Only one study has quantified transmission from infected sheep to cattle under controlled conditions [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], which found the basic reproduction number of transmission from sheep to cattle (1.4) was similar to that for transmission from sheep to sheep (1.1), but was lower than that for transmission cattle to cattle. Further studies are needed to quantify transmission from small ruminants to other susceptible species to understand differences at host level.\u003c/p\u003e \u003cp\u003eThe sheep and goats sampled in households were kept in the same premises and potentially had a similar level of exposure to FMDV, although anecdotal observations suggest that sheep and goats are kept separately, with sheep maintained in closer contact with cattle. A limitation of this study is that we did not collect detailed information regarding management practices. Further studies, with more detailed information about the management practices used in different geographic areas and production systems, are needed to better understand the extent to which this plays a role on the differences observed between seroconversion in sheep and goats and force of infection.\u003c/p\u003e \u003cp\u003eWe also evaluated different sampling and testing methods in order to identify those which are reliable and convenient for surveillance in endemic settings. All oral swabs positive for FMDV RNA were collected during a month in which FMD outbreaks were reported (either in nearby locations or in one of the households that was part of the study). In contrast, no positive oral swabs were obtained during the months in which FMD outbreaks were not reported. These data suggest that FMDV positive oral swabs, as tested by rRT-PCR, are a good indicator of ongoing infection in a geographic region. Detecting FMDV RNA in serum was also evaluated as an alternative method. However, FMDV RNA in blood was found a few days after clinical signs were present and therefore it may be that animals recently infected were missed. However, FMDV RNA was detected in blood samples after the clinical window, demonstrating animals can still be infected after this time and clinical evaluation alone is a poor indicator of the presence of FMDV as viraemia is transient.\u003c/p\u003e \u003cp\u003eIn addition, this study showed that environmental swabs are a good herd level indicator of FMDV circulating in the area. FMDV RNA was detected in environmental samples in all households that reported an outbreak. In addition, premises where FMDV RNA was detected in environmental samples as part of the longitudinal study had at least one animal in which FMDV RNA was detected in serum samples and/or oral swabs. This is aligned with previous studies that showed that environmental sampling can detect FMDV in a herd more quickly than clinical inspection if sufficient samples are collected often enough [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. To increase the likelihood of detection of FMDV in the environment, it is important to sample at areas likely to have come into contact with secretions or excretions from infected animals. Our results suggest that good targets are boots, ropes, pegs, transport vehicles or hard floor surfaces.\u003c/p\u003e \u003cp\u003eFragments of FMDV RNA may survive in the environment for prolonged periods of time [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], although persistence in the environment is likely to exhibit some seasonality based on changes in relative humidity and temperature [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. As survival and persistence times for FMDV RNA in this environment are unknown, it is difficult to attribute positive samples to a particular outbreak in an endemic setting. However, our results suggest that the number of positive environmental swabs does match well with the number of positive oral swabs from animals. This suggests that an increase in the number of positive samples would be indicative of virus circulating in a region.\u003c/p\u003e \u003cp\u003eOur study showed that VP1 sequences could be obtained from environmental samples (including hard floor and ropes) with low C\u003csub\u003eT\u003c/sub\u003e values (between 20.9 and 25.3). However, the RNA derived from environmental samples is likely to be fragmented and difficult to sequence by conventional methods. The incorporation of a probe enrichment step in a next generation sequencing library workflow enabled the recovery of viral genomic data from poor quality samples, as described previously [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. When comparing the sequences obtained from environmental samples with sequences from other samples collected during outbreaks in nearby areas over the same time period as the study, we found that sequences obtained from environmental samples were closely related to those submitted from clinical samples. These findings confirm environmental sampling can be used as a surveillance method in endemic settings, especially when combined with sequencing.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study sheds new light on the role of small ruminants on the epidemiology and endemicity of FMD. In addition, this study demonstrates the utility of oral and environmental swabs as suitable sampling methods for the early detection of FMDV infection at the animal and herd level, respectively. Furthermore, when combined with sequencing they allow for outbreak tracing.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSG, FR, ABL, CC, GL and DOE conceptualised and planned the study. YW, OO, AJA, SII, RBA, MOO, MS and BBD implemented field study, collected and processed samples. EB, HH, BAW, JW, NK, CC and AES tested the samples collected. SG, NK AES and GL conducted data analysis. SG, DPK, ABL, CC, AES, GL, DOE contributed to the interpretation of results. SG and GL drafted the first version of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis project was funded by the European Commission for the Control of Foot-and-Mouth Disease (EuFMD) through their Fund for Applied Research 8th and 9th Calls. The authors acknowledge additional support from the Department for Environment, Food and Rural Affairs (grant codes SE2722, SE2723 and SE2945) and UKRI Biotechnology and Biological Sciences Research Council (grant codes BBS/E/PI/230002C and BBS/E/PI/23NB0004).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data from the current study are available in Additional file 12.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval for the study was obtained from the Animal Welfare Ethical Review Board (AWERB) of The Pirbright Institute.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are grateful to the livestock keepers for allowing us to sample their animals and households. We thank Etienne Chevanne for providing helpful comments during the internal review process of the European Commission for the Control of Foot-and-Mouth Disease.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKnowles NJ, Samuel AR (2003) Molecular epidemiology of foot-and-mouth disease virus. Virus Res 91(1):65\u0026ndash;80\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarnett PV, Cox SJ (1999) The role of small ruminants in the epidemiology and transmission of foot-and-mouth disease. Vet J 158(1):6\u0026ndash;13\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKitching RP, Hughes GJ (2002) Clinical variation in foot and mouth disease: sheep and goats. Rev sci tech Off int Epiz 21(3):505\u0026ndash;512\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFAOSTAT (2021) FAOSTAT Crop and livestock products. Food and Agricultural Organization of the United Nations. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.fao.org/faostat/en/#data/QCL\u003c/span\u003e\u003cspan address=\"https://www.fao.org/faostat/en/#data/QCL\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBegovoeva M, Ehizibolo DO, Adedeji AJ, Oguche MO, Oyekan O, Ijoma SI, Atai RB, Wungak Y, Dogonyaro BB, Lazarus DD et al (2023) Factors associated with foot-and-mouth disease seroprevalence in small ruminants and identification of hot-spot areas in northern Nigeria. Prev Vet Med 212:105842\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEhizibolo DO, De Vleeschauwer AR, Haegeman A, Lefebvre D, Nwosuh CI, Umoh JU, Okolocha EC, Kazeem HM, Van Borm S, De Clercq K (2019) Serological and molecular epidemiology of foot-and-mouth disease viruses in agro-pastoralist livestock herds in the kachia grazing reserve, Nigeria. Transbound Emerg Dis 66(4):1575\u0026ndash;1586\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEhizibolo DO, Haegeman A, De Vleeschauwer AR, Umoh JU, Kazeem HM, Okolocha EC, Van Borm S, De Clercq K (2017) Detection and Molecular Characterization of Foot and Mouth Disease Viruses from Outbreaks in Some States of Northern Nigeria 2013\u0026ndash;2015. Transbound Emerg Dis 64(6):1979\u0026ndash;1990\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEhizibolo DO, Perez AM, Carrillo C, Pauszek S, AlKhamis M, Ajogi I, Umoh JU, Kazeem HM, Ehizibolo PO, Fabian A et al (2014) Epidemiological analysis, serological prevalence and genotypic analysis of foot-and-mouth disease in Nigeria 2008\u0026ndash;2009. Transbound Emerg Dis 61(6):500\u0026ndash;510\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLazarus DD, Schielen WJG, Wungak Y, Kwange D, Fasina FO (2012) Sero-epidemiology of foot-and-mouth disease in some Border States of Nigeria. Afr J Microbiol Res 6(8):1756\u0026ndash;1761\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEhizibolo DO, Haegeman A, De Vleeschauwer AR, Umoh JU, Kazeem HM, Okolocha EC, Van Borm S, De Clercq K (2017) Foot-and-mouth disease virus serotype SAT1 in cattle, Nigeria. Transbound Emerg Dis 64(3):683\u0026ndash;690\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFomenky B, Hole K, Ularamu H, Wungak Y, Ehizibolo D, Nebroski M, Kruczkiewicz P, Buchanan C, Lung O, Nfon C (2021) Molecular Characterization of Southern African Territories 2 (SAT2) Serotype of Foot-and-Mouth Disease Virus from Nigeria in 2017 to 2018. Microbiol Resour Announc 10(27):e0036221\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUlaramu HG, Ibu JO, Wood BA, Abenga JN, Lazarus DD, Wungak YS, Knowles NJ, Wadsworth J, Mioulet V, King DP et al (2017) Characterization of Foot-and-Mouth Disease Viruses Collected in Nigeria Between 2007 and 2014: Evidence for Epidemiological Links Between West and East Africa. Transbound Emerg Dis 64(6):1867\u0026ndash;1876\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEhizibolo DO, Fish IH, Brito B, Bertram MR, Ardo A, Ularamu HG, Lazarus DD, Wungak YS, Nwosuh CI, Smoliga GR et al (2020) Characterization of transboundary foot-and-mouth disease viruses in Nigeria and Cameroon during 2016. Transbound Emerg Dis 67(3):1257\u0026ndash;1270\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUlaramu HG, Lefebvre DJ, Haegeman A, Wungak YS, Ehizibolo DO, Lazarus DD, De Vleeschauwer AR, De Clercq K (2020) Complex Circulation of Foot-and-Mouth Disease Virus in Cattle in Nigeria. Front Vet Sci 7:466\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBuckle K, Bueno R, McFadden A, van Andel M, Spence R, Hamill C, Roe W, Vallee E, Castillo-Alcala F, Abila R et al (2021) Detection of Foot-and-Mouth Disease Virus in the Absence of Clinical Disease in Cattle and Buffalo in South East Asia. Front Vet Sci 8:691308\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStenfeldt C, Lohse L, Belsham GJ (2013) The comparative utility of oral swabs and probang samples for detection of foot-and-mouth disease virus infection in cattle and pigs. Vet Microbiol 162(2\u0026ndash;4):330\u0026ndash;337\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eColenutt C, Brown E, Nelson N, Wadsworth J, Maud J, Adhikari B, Chapagain Kafle S, Upadhyaya M, Kafle Pandey S, Paton DJ et al (2018) Environmental Sampling as a Low-Technology Method for Surveillance of Foot-and-Mouth Disease Virus in an Area of Endemicity. Appl Environ Microbiol 84(16)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eColenutt C, Brown E, Paton DJ, Mahapatra M, Parida S, Nelson N, Maud J, Motta P, Sumption K, Adhikari B et al (2022) Environmental sampling for the detection of foot-and-mouth disease virus and peste des petits ruminants virus in a live goat market, Nepal. Transbound Emerg Dis 69(5):3041\u0026ndash;3046\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMielke SR, Lendzele S, Delgado AH, Abdoulmoumini M, Dickmu S, Garabed R (2023) Patterns of foot-and-mouth disease virus detection in environmental samples in an endemic setting. Front Vet Sci 10:1157538\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEllis J, Brown E, Colenutt C, Gubbins S (2023) Assessing the effectiveness of environmental sampling for surveillance of foot-and-mouth disease virus in a cattle herd. Front Vet Sci 10:1074264\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEuFMD WRL Foot-and-Mouth disease October-December 2020 quartely report. In: \u003cem\u003eFAST reports - Foot-and-mouth And Similar Transboundary (FAST) animal diseases\u003c/em\u003e 2020\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCallahan JD, Brown F, Osorio FA, Sur JH, Kramer E, Long GW, Lubroth J, Ellis SJ, Shoulars KS, Gaffney KL et al (2002) Use of a portable real-time reverse transcriptase-polymerase chain reaction assay for rapid detection of foot-and-mouth disease virus. J Am Vet Med Assoc 220(11):1636\u0026ndash;1642\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLogan G, Freimanis GL, King DJ, Valdazo-Gonzalez B, Bachanek-Bankowska K, Sanderson ND, Knowles NJ, King DP, Cottam EM (2014) A universal protocol to generate consensus level genome sequences for foot-and-mouth disease virus and other positive-sense polyadenylated RNA viruses using the Illumina MiSeq. BMC Genomics 15(1):828\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eColenutt C, Shaw A, Esemu S, Kfusib A, Besong W, Brown E, Wadsworth J, Knowles N, King D, Ndip L et al Characterising foot-and-mouth disease viruses circulating in Cameroon using environmental sampling at livestock markets and abattoirs. submitted\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTamura K (1992) Estimation of the number of nucleotide substitutions when there are strong transition-transversion and G\u0026thinsp;+\u0026thinsp;C-content biases. Mol Biol Evol 9:678\u0026ndash;687\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKumar S, Stecher G, Tamura K (2016) MEGA7: Molecular Evolutionary Genetics Analysis version 7.0 for bigger datasets. Mol Biol Evol 33:1870\u0026ndash;1874\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBodenham RF, Mazeri S, Cleaveland S, Crump JA, Fasina FO, de Glanville WA, Haydon DT, Kazwala RR, Kibona TJ, Maro VP et al (2021) Latent class evaluation of the performance of serological tests for exposure to Brucella spp. in cattle, sheep, and goats in Tanzania. PLoS Negl Trop Dis 15(8):e0009630\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePlummer M (2003) A program for analysis of Bayesian graphical models using Gibbs sampling. In: Proc 3rd Int Work Distrib Stat Comput\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePlummer M, Stukalov A, Denwood M (2019) rjags: Bayesian grafical models using MCMC. In, 4\u0026ndash;9 edn\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR Core Team (2024) R: A language and Environment for Statistical Computing In., vol. Version 4.4.0\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDelignette-Muller ML, Dutang C (2015) fitdistrplus: An R Package for Fitting Distributions. J Stat Softw 64(4)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStevenson M, Sergeant E epiR: Tools for the Analysis of Epidemiological Data. \u003cem\u003eR package version 2062\u003c/em\u003e 2023\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSpiegelhalter DJ, Best NG, Carlin BP, van der Linde A (2002) Bayesian measures of model complexity and fit (with discussion). J Royal Stat Soc Ser B 64:583\u0026ndash;639\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHens N, Aerts M, Faes C, Shkedy Z, Lejeune O, Van Damme P, Beutels P (2010) Seventy-five years of estimating the force of infection from current status data. Epidemiol Infect 138(6):802\u0026ndash;812\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnderson RM, May RM (1991) Infectious diseases of humans. Oxford University Press, Oxford UK\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGelman A, Carlin JB, Stern HS, Rubin DB (2004) Bayesian data analysis, 2nd edn. Chapman Hall/CRC, Boca Raton, FL, U.S.A.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKeeling M, Rohani P (2008) Modeling infectious diseases in humans and animals. Princeton University Press, Princeton, NJ\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHaario H, Saksman E, Tamminen J (2001) An adaptive Metropolis algorithm. Bernoulli 7:223\u0026ndash;242\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAndrieu C, Thoms J (2008) A tutorial on adaptive MCMC. Stats Comput 18:343\u0026ndash;373\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePlummer M, Best N, Cowles K, Vines K (2006) Diagnosis and Output Analysis for MCMC. R News 6:7\u0026ndash;11\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBeyene B, Tolosa T, Rufael T, Hailu B, Teklue T (2015) Foot and mouth disease in selected districts of western Ethiopia: seroprevalence and associated risk factors. Rev Sci Tech 34(3):939\u0026ndash;952\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMesfine M, Nigatu S, Belayneh N, Jemberu WT (2019) Sero-Epidemiology of Foot and Mouth Disease in Domestic Ruminants in Amhara Region, Ethiopia. Front Vet Sci 6:130\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBelayneh N, Molla W, Mesfine M, Jemberu WT (2020) Modeling the transmission dynamics of foot and mouth disease in Amhara region, Ethiopia. Prev Vet Med 181:104673\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWoolhouse ME, Haydon DT, Pearson A, Kitching RP (1996) Failure of vaccination to prevent outbreaks of foot-and-mouth disease. Epidemiol Infect 116(3):363\u0026ndash;371\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChis Ster I, Dodd PJ, Ferguson NM (2012) Within-farm transmission dynamics of foot and mouth disease as revealed by the 2001 epidemic in Great Britain. Epidemics 4(3):158\u0026ndash;169\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTadesse B, Molla W, Mengsitu A, Jemberu WT (2019) Transmission dynamics of foot and mouth disease in selected outbreak areas of northwest Ethiopia. Epidemiol Infect 147:e189\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArjkumpa O, Picasso-Risso C, Perez A, Punyapornwithaya V (2021) Subdistrict-Level Reproductive Number for Foot and Mouth Disease in Cattle in Northern Thailand. Front Vet Sci 8:757132\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Bravo C, de Jong MC, Eble PL, Dekker A (2014) Estimation of the transmission of foot-and-mouth disease virus from infected sheep to cattle. Vet Res 45(1):58\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBartley LM, Donnelly CA, Anderson RM (2002) Review of foot-and-mouth disease virus survival in animal excretions and on fomites. Vet Rec 151(22):667\u0026ndash;669\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"cattle, sheep, goats, surveillance, foot-and-mouth disease virus, endemic disease environmental sampling","lastPublishedDoi":"10.21203/rs.3.rs-4312238/v2","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4312238/v2","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFoot-and-mouth disease (FMD) is a highly contagious disease affecting cloven-hoofed ungulates. This study aimed to enhance our understanding of the role of small ruminants and environmental contamination in the epidemiology and endemicity of FMD. A longitudinal study was conducted between March 2021 and October 2021 in northern Nigeria, where monthly samples were collected from five households, one livestock market and one transhumance location in two local government areas (LGA) identified as being at high risk of FMD. Serum samples (n\u0026thinsp;=\u0026thinsp;783), oral swabs (n\u0026thinsp;=\u0026thinsp;424) and environmental swabs (n\u0026thinsp;=\u0026thinsp;458) were collected and tested for the presence of foot-and-mouth disease virus (FMDV) RNA by rRT-PCR. Serum samples (n\u0026thinsp;=\u0026thinsp;780) were also tested for the presence of antibodies against FMDV non-structural proteins. The proportion of FMDV RNA positive samples increased in all sample types collected in one LGA during the period when an FMD outbreak was reported in the same LGA. In contrast, sero-positive samples did not differ by month but differed between LGAs and amongst species. The force of infection estimated from age-seroprevalence data for each household was significantly lower in goats compared with both cattle or sheep. Five O/EA-3 topotype sequences were obtained from selected FMDV RNA positive samples; findings which support the use of environmental swabs to detect circulating FMDV strains in endemic settings. These results show oral and environmental swabs are suitable sampling methods for early detection at animal and herd level, respectively and provide insights on the role of small ruminants on FMD epidemiology.\u003c/p\u003e","manuscriptTitle":"Longitudinal study of foot-and-mouth disease virus in Northern Nigeria: implications for the roles of small ruminants and environmental contamination in endemic settings","msid":"","msnumber":"","nonDraftVersions":[{"code":2,"date":"2025-01-23 20:55:31","doi":"10.21203/rs.3.rs-4312238/v2","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}},{"code":1,"date":"2024-04-24 09:11:06","doi":"10.21203/rs.3.rs-4312238/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6ceb6d3b-8722-4eec-8151-e5aaa1efe2f4","owner":[],"postedDate":"January 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":31091638,"name":"Veterinary Epidemiology"}],"tags":[],"updatedAt":"2024-04-24T09:11:06+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-23 20:55:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v2","identity":"rs-4312238","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4312238","identity":"rs-4312238","version":["v2"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}