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...

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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

Accounting for multiple modes of livestock disease dissemination in epidemiological models remains a challenge. We developed and calibrated a mathematical model for transmission of porcine reproductive and respiratory syndrome virus (PRRSV), tailored to fit nine modes of between-farm transmission pathways including: farm-to-farm proximity (local transmission), contact network of batches of pigs transferred between farms (pig movements), re-break probabilities for farms with previous PRRSV outbreaks, with the addition of four different contact networks of transportation vehicles (vehicles to transport pigs to farms, pigs to markets, feed and crew) and the amount of animal by-products within feed ingredients (e.g., animal fat or meat and bone meal).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 (i.e., sow, nursery). Although our results estimated that the networks formed by transportation vehicles were more densely connected than the network of pigs transported between farms, pig movements and farm proximity were the main PRRSV transmission routes regardless of farm types. Among the four vehicle networks, vehicles transporting pigs to farms explained a large proportion of infections, sow = 20.9%; nursery = 15%; and finisher = 20.6%. The animal by-products showed a limited association with PRRSV outbreaks through descriptive analysis, and our model results showed that the contribution of animal fat contributed only 2.5% and meat and bone meal only .03% of the infected sow farms. Our work demonstrated the contribution of multiple routes of PRRSV dissemination, which has not been deeply explored before. It also provides strong evidence to support the need for cautious, measured PRRSV control strategies for transportation vehicles and further research for feed by-products modelling.Finally, this study provides valuable information and opportunities for the swine industry to focus effort on the most relevant modes of PRRSV between-farm transmission.