Serologic evidence of West Nile virus and Saint Louis encephalitis virus in horses from Southern Brazil.

Flaviviruses as West Nile virus (WNV), Saint Louis encephalitis virus (SLEV), Ilhéus virus (ILHV), and Rocio virus (ROCV) are previously reported in different Brazilian regions, but studies in Southern Brazil are still scarce. To improve the information regarding flaviviruses in Southern Brazil, horse serum samples were analyzed using RT-qPCR and a commercial ELISA-Ab against WNV followed by PRNT75. All 1000 samples analyzed by real-time RT-PCR resulted negative. The 465 subsampled samples were analyzed by a commercial ELISA-Ab against WNV, and the 18.5% (86/465) positive samples were further analyzed by PRNT75. In the PRNT75, 13/86 and 2/86 horses were positive for SLEV and WNV, respectively. It was observed that 5.8% (13/226) of the farms presented at least one positive animal for SLEV in PRNT75, whereas 0.9% (2/226) for WNV. Apart from the lower seroprevalences identified when compared to data previously reported in other Brazilian regions, our results suggest that public health professionals must be aware of the presence of these potential zoonotic pathogens.

Burkholderia mallei: The dynamics of networks and disease transmission.

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.