Many aspects of the porcine reproductive and respiratory syndrome virus (PRRSV) between‐farm transmission dynamics have been investigated, but uncertainty remains about the significance of farm type and different transmission routes on PRRSV spread. We developed a stochastic epidemiological model calibrated on weekly PRRSV outbreaks accounting for the population dynamics in different pig production phases, breeding herds, gilt development units, nurseries and finisher farms, of three hog producer companies. Our model accounted for indirect contacts by the close distance between farms (local transmission), between‐farm animal movements (pig flow) and reinfection of sow farms (re‐break). The fitted model was used to examine the effectiveness of vaccination strategies and complementary interventions such as enhanced PRRSV detection and vaccination delays and forecast the spatial distribution of PRRSV outbreak. The results of our analysis indicated that for sow farms, 59% of the simulated infections were related to local transmission (e.g. airborne, feed deliveries, shared equipment) whereas 36% and 5% were related to animal movements and re‐break, respectively. For nursery farms, 80% of infections were related to animal movements and 20% to local transmission; while at finisher farms, it was split between local transmission and animal movements. Assuming that the current vaccines are 1% effective in mitigating between‐farm PRRSV transmission, weaned pigs vaccination would reduce the incidence of PRRSV outbreaks by 3%, indeed under any scenario vaccination alone was insufficient for completely controlling PRRSV spread. Our results also showed that intensifying PRRSV detection and/or vaccination pigs at placement increased the effectiveness of all simulated vaccination strategies. Our model reproduced the incidence and PRRSV spatial distribution; therefore, this model could also be used to map current and future farms at‐risk. Finally, this model could be a useful tool for veterinarians, allowing them to identify the effect of transmission routes and different vaccination interventions to control PRRSV spread.

A cross-sectional study was carried out between January 2012 and May 2014 to investigate the status of bovine tuberculosis in the state of Espírito Santo. The state was divided into two regions, and in each of them, 300 farms with reproductive activity were randomly selected and considered as the primary sampling units. In the selected farms, a fixed number of female bovines aged over 2 years were randomly selected to undergo a comparative cervical tuberculin test; an epidemiologic questionnaire was also applied. In the state of Espírito Santo, the apparent prevalence of tuberculosis-positive farms was 7.6% (95% confidence interval [CI] = 5.7-9.9). Prevalence at the herd level varied from 4.6% (95% CI = 2.6-7.3) in region 1 to 11.1% (95% CI = 7.7-15.3) in region 2. The apparent prevalence of tuberculosis-positive animals was 0.7% (95% CI = 0.3-1.1) in the state, and the prevalence varied from 0.3% (95% CI = 0.2-0.6) in region 1 to 1.2% (95% CI = 0.3-2.9) in region 2. The risk factors associated with tuberculosis prevalence in Espírito Santo were the number of adult females ≥ 10 (odds ratio [OR] = 2.40; 95% CI = 1.17-5.31) and milking type (milking machine/milking parlor) (OR = 2.88; 95% CI = 1.36-5.86]). The state of Espírito Santo should set up a surveillance system to detect and control bovine tuberculosis, taking into account the importance of dairy farms and animal trade in the state. Key words: Bovine. Brazil. Espírito Santo. Prevalence. Risk fator. Tuberculosis. ResumoPara estimar a prevalência e os fatores de risco da tuberculose bovina no Estado do Espírito Santo (Brasil), foi realizado um estudo transversal entre janeiro de 2012 e maio de 2014. O estado foi dividido em duas regiões, e em cada uma foram amostradas aleatoriamente 300 fazendas com atividade reprodutiva, consideradas unidades primárias de amostragem. Nas propriedades selecionadas um número fixo de fêmeas bovinas acima de 2 anos de idade foram aleatoriamente selecionadas para realização do teste cervical comparativo; também foi aplicado um questionário epidemiológico. No Estado do Espírito Santo a prevalência aparente de fazendas positivas para tuberculose foi de 7,6% (intervalo de confiança 95% [IC 95%] = 5,7-9,9). A prevalência de rebanhos positivos variou de 4,6% (IC 95% = 2,6-7,3) na região 1 a 11,1% (IC 95% = 7,7-15,3) na região 2. A prevalência aparente de animais positivos para tuberculose foi de 0,7% (IC 95% = 0,3-1,1) no estado, variando de 0,3% (IC 95% = 0,2-0,6) na região 1 a 1,2% (IC 95% = 0,3-2,9) na região 2. Os fatores de risco associados com a infecção por tuberculose no Espírito Santo foram: número de fêmeas adultas ≥ 10 (odds ratio [OR] = 2,40; IC 95% = 1,17-5,31) e tipo de ordenha (ordenhadeira mecânica/sala de ordenha) (OR = 2,88; IC 95% = 1,36-5,86). O estado do Espírito Santo deve implementar um sistema de vigilância para detectar e controlar a tuberculose bovina, levando em consideração a importância de propriedades leiteiras e comércio animal na epidemiologia da doença no estado. Palavras-chave: Bovina. Brasi...

14 15 In the monitoring of porcine reproductive and respiratory syndrome virus (PRRSv), 16 knowledge about between-farm transmission dynamics is still lacking. Our objective was 17 to assess the relative contribution of between-farm PRRSv transmission routes through a 18 mechanistic epidemiological model calibrated with PRRSv occurrence, identify risk 19 areas, and estimate the impact of immunization strategies in the disease spread. We 20 developed a mathematical model of PRRSv transmission accounting for spatial 21 proximity, pig movements, and re-breaks in sow farms, parametrized on data collected 22 routinely by commercial pig farms. We then used the model to simulate the weekly 23 frequency of cases and built risk maps, and compared with the observed cases. We 24 simulated the implementation of two immunization strategies (preventive and reactive) to 25 mitigate the between-farm transmission. Our results indicated for sow and GDU farms' 26 local spread on average was above 60%, while for nurseries between-farm movements 27 represented 83% of transmissions and in finisher farms it was distributed almost 50% 28 local and 50% between-farm movement, the model allowed reproduce the weekly 29 frequency of observed cases and the risk maps built allowed the identification of 30 observed cases in the space. The increase in vaccine efficacy was the most important 31 parameter to mitigate between-farm transmission. Also, the implementation of 32 immunization by a preventive and reactive strategy combined had the better result to 33 mitigate between-farm transmission than implement these strategies individually. These 34 immunization strategies had a better performance with the use of rigorous protocols, such 35

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Tracking animal movements over time can fundamentally determine the success of disease control interventions throughout targeting farms that are tightly connected. In commercial pig production, animals are transported between farms based on growth stages, thus it generates time-varying contact networks that will influence the dynamics of disease spread. Still, risk-based surveillance strategies are mostly based on a static network. In this study, we reconstructed the static and temporal pig networks of one Brazilian state from 2017 to 2018, comprising 351,519 movements and 48 million transported pigs. The static networks failed to capture time-respecting movement pathways. Therefore, we propose a time-dependent network susceptible-infected (SI) model to simulate the temporal spread of an epidemic over the pig network globally through the temporal movement of animals among farms, and locally with a stochastic compartmental model in each farm, configured to calculate the minimum number of target farms needed to achieve effective disease control. In addition, we propagated disease on the pig temporal network to calculate the cumulative contacts as a proxy of epidemic sizes and evaluated the impact of network-based disease control strategies. The results show that targeting the first 1,000 farms ranked by degree would be sufficient and feasible to diminish disease spread considerably. Our finding also suggested that assuming a worst-case scenario in which every movement transmit disease, pursuing farms by degree would limit the transmission to up to 29 farms over the two years period, which is lower than the number of infected farms for random surveillance, with epidemic sizes of 2,593 farms. The top 1,000 farms could benefit from enhanced biosecurity plans and improved surveillance, which constitute important next steps in strategizing targeted disease control .

A cross-sectional study was carried out between January 2012 and May 2014 to estimate the prevalence of bovine brucellosis in the state of Espírito Santo, Brazil. This study was carried out 11 years after the implementation of the immunization program for heifers with a vaccine derived from the S19 strain of Brucella abortus. The state was divided into two regions. Three hundred farms from each region, with reproductive activity, were randomly chosen and included as primary sample units. A fixed number of cows were randomly selected and tested for antibodies against Brucella spp. A farm was considered to be infected when at least one female tested positive for Brucella spp. In the selected farms, an epidemiological questionnaire based survey, focused on herd traits as well as husbandry and sanitary practices, was conducted, to evaluate the factors associated with the risk of infection. The overall prevalence of infected herds was 9.3% (95% confidence interval, 95% CI = 7.1-11.8%), varying from 8.7% (95% CI = 5.7-12.6%) in region 2 to 9.7% (95% CI = 6.8-13.4%) in region 1. There was no significant difference in the prevalence between the regions. The apparent prevalence of Brucella spp. positive farms across the regions and the state was similar to the prevalence observed 11 years earlier.The prevalence of positive animals was 3.8% (95% CI = 0.9-10.1%), varying from 1.5% (95% CI = 0.8-2.4%) in region 1 to 7.9% (95% CI = 1.9-20.3%) in region 2, without a significant difference between the regions. There was no difference in the number of Brucella spp. positive animals after 11 years of the immunization program. The risk factors associated with brucellosis were (i) more than 10 cows per herd (OR = 5.0; 95% CI =2.5-11.1) and (ii) equipment, feedstock, or personnel sharing (OR = 2.2; 95% CI = 1.1-4.2). The state of Espírito Santo should seek systematic vaccination coverage targeting more than 80% of the eligible heifers. An efficient animal health program, which educates the farmers to test replacement animals for brucellosis before introducing them to their herds, to avoid equipment, personnel or feedstock sharing with farms of unknown sanitary conditions, and to increase awareness of the importance of good sanitary procedures during artificial insemination, should be implemented. Anzai, E. K. et al. ResumoPara estimar a prevalência e os fatores de risco da brucelose bovina no Estado do Espírito Santo (Brasil), foi realizado um estudo transversal entre janeiro de 2012 e maio de 2014. Esse estudo foi realizado 11 anos após a implementação de um programa de imunização em novilhas utilizando uma vacina derivada da estirpe S19 de Brucella abortus. O estado foi dividido em duas regiões. Trezentas propriedades com atividade reprodutiva foram aleatoriamente selecionadas em cada região e incluídas como unidades primárias de amostragem. Um número fixo de fêmeas adultas foi aleatoriamente selecionado em cada propriedade. Os animais foram testados para anticorpos contra Brucella spp. Um questionário epidemiológico foi ...

Glanders is a highly infectious zoonotic disease caused by Burkholderia mallei. The transmission of B. mallei occurs mainly by direct contact, and horses are the natural reservoir. Therefore, the identification of infection sources within horse populations and animal movements is critical to enhance disease control. Here, we analysed the dynamics of horse movements from 2014 to 2016 using network analysis in order to understand the flow of animals in two hierarchical levels, municipalities and farms. The municipality‐level network was used to investigate both community clustering and the balance between the municipality's trades and the farm‐level network associations between B. mallei outbreaks and the network centrality measurements, analysed by spatio‐temporal generalized additive model (GAM). Causal paths were established for the dispersion of B. mallei outbreaks through the network. Our approach captured and established a direct relationship between movement of infected equines and predicted B. mallei outbreaks. The GAM model revealed that the parameters in degree and closeness centrality out were positively associated with B. mallei. In addition, we also detected 10 communities with high commerce among municipalities. The role of each municipality within the network was detailed, and significant changes in the structures of the network were detected over the course of 3 years. The results suggested the necessity to focus on structural changes of the networks over time to better control glanders disease. The identification of farms with a putative risk of B. mallei infection using the horse movement network provided a direct opportunity for disease control through active surveillance, thus minimizing economic losses and risks for human cases of B. mallei.

Porcine reproductive and respiratory syndrome virus (PRRSV) continues to cause substantial economic losses for the North American pork industry. Here we developed and parameterized a mathematical model for transmission of PRRSV amongst the swine farms of one U.S. state. The model is tailored by eight modes of between-farm transmission pathways including farm-to-farm proximity (local transmission), networks comprised of different layers contacts here considered the number of batches of pigs transferred between-farm (pig movements), transportation vehicles used for -- feed delivery, transferring live pigs to farms and to markets, and personnel (crew), in addition to the quantity of feed with animal by-products within feed ingredients, and finally we also accounted for re-break probabilities for farms with previous PRRSV outbreaks. The model was calibrated on weekly PRRSV outbreaks data. We assessed the role of each transmission pathway considering the dynamics of specific types of production. Our results estimated that the networks formed by transportation vehicles were more densely connected than the actual network of pigs moved between-farms. The model estimated that pig movements and farm proximity were the main route of transmission in the spread of PRRSV regardless of production types, but vehicles transporting pigs to farms explained a large proportion of infections (sow = 17.2%; nursery = 11.7%; and finisher = 29.5%). Animal by-products delivered via feed contributed principally to finisher farms, with a significant impact on PRRSV outbreaks on sow farms. Thus, our results support the consideration of transport vehicles and feed meals in order better to understand the transmission dynamic of PRRSV and establish more robust control strategies.