Modeling foot-and-mouth disease dissemination in Rio Grande do Sul, Brazil and evaluating the effectiveness of control measures.

Introduction: Foot-and-mouth disease (FMD) affects multiple food-animal species and spreads rapidly among ungulate populations, posing significant challenges for disease control. Understanding the dynamics of FMD transmission and evaluating the effectiveness of control measures are critical for mitigating its impact. This study introduces a multiscale compartmental stochastic model to simulate FMD spread and assess countermeasures.

Analyzing the intrastate and interstate swine movement network in the United States.

Identifying and restricting animal movements is a common approach used to mitigate the spread of diseases between premises in livestock systems. Therefore, it is essential to uncover between-premises movement dynamics, including shipment distances and network-based control strategies. Here, we analyzed three years of between-premises pig movements, which include 197,022 unique animal shipments, 3973 premises, and 391,625,374 pigs shipped across 20 U.

Spatiotemporal relative risk distribution of porcine reproductive and respiratory syndrome virus in the United States.

Porcine reproductive and respiratory syndrome virus (PRRSV) remains widely distributed across the U.S. swine industry.

Network analysis of pig movements in Ecuador: Strengthening surveillance of classical swine fever.

The analysis of domestic pig movements has become useful to understand the disease spread patterns and epidemiology, which facilitates the development of more effective animal diseases control strategies. The aim of this work was to analyse the static and spatial characteristics of the pig network, to identify its trading communities and to study the contribution of the network to the transmission of classical swine fever. In this regard, we used the pig movement records from the National Veterinary Service of Ecuador (2017-2019), using social network analysis and spatial analysis to construct a network with registered premises as nodes and their movements as edges.

Coupling spatial statistics with social network analysis to estimate distinct risk areas of disease circulation to improve risk-based surveillance.

Most animal disease surveillance systems concentrate efforts in blocking transmission pathways and tracing back infected contacts while not considering the risk of transporting animals into areas with elevated disease risk. Here, we use a suite of spatial statistics and social network analysis to characterize animal movement among areas with an estimated distinct risk of disease circulation to ultimately enhance surveillance activities. Our model utilized equine infectious anemia virus (EIAV) outbreaks, between-farm horse movements, and spatial landscape data from 2015 through 2017.

Modelling control strategies against classical swine fever: Influence of traders and markets using static and temporal networks in Ecuador.

Pig farming in Ecuador represents an important economic and cultural sector, challenged by classical swine fever (CSF). Recently, the National Veterinary Service (NVS), has dedicated its efforts to control the disease by implementing pig identification, mandatory vaccination against CSF and movement control. Our objective was to characterise pig premises according to risk criteria, to model the effect of movement restriction strategies and to consider the temporal evolution of the network.

Multiple species animal movements: network properties, disease dynamics and the impact of targeted control actions.

Infectious diseases in livestock are well-known to infect multiple hosts and persist through a combination of within- and between-host transmission pathways. Uncertainty remains about the epidemic dynamics of diseases being introduced on farms with more than one susceptible host species. Here, we describe multi-host contact networks and elucidate the potential of disease spread through farms with multiple hosts.

Spatio-temporal network analysis of pig trade to inform the design of risk-based disease surveillance.

Network analysis is a powerful tool to describe, estimate, and predict the role of pig trade in the spread of pathogens and generate essential patterns that can be used to understand, prevent, and mitigate possible outbreaks. This study aimed to describe the network between premises such as production herds, slaughterhouses, and traders of pig movements and identify heterogeneities in the connectivity of premises in the state of Santa Catarina, Brazil, using social network analysis (SNA). We used static and temporal network approaches to describe pig trade in the state by quantifying network attributes using SNA parameters, such as causal fidelity, loyalty, the proportion of node-loyalty, resilience of outgoing contact chains, and communities.

Evaluation of the performance of slaughterhouse surveillance for bovine tuberculosis detection in Castilla y Leon, Spain.

Post-mortem inspection (PMI) of routinely slaughtered cattle in abattoirs is an extremely valuable tool for detecting bovine tuberculosis (bTB) infected herds that can supplement active surveillance activities. However, its true performance is difficult to assess due to the multiple factors that may affect it. Here, we determined relative efficiencies in the detection of bTB-compatible lesions and probabilities of subsequent laboratory confirmation of abattoirs located in Castilla y Leon, one of the regions with the largest cattle population in Spain, between 2010 and 2017.

Use of Network Analysis and Spread Models to Target Control Actions for Bovine Tuberculosis in a State from Brazil.

Livestock movements create complex dynamic interactions among premises that can be represented, interpreted, and used for epidemiological purposes. These movements are a very important part of the production chain but may also contribute to the spread of infectious diseases through the transfer of infected animals over large distances. Social network analysis (SNA) can be used to characterize cattle trade patterns and to identify highly connected premises that may act as hubs in the movement network, which could be subjected to targeted control measures in order to reduce the transmission of communicable diseases such as bovine tuberculosis (TB).

Quantifying the dynamics of pig movements improves targeted disease surveillance and control plans.

Tracking animal movements over time may fundamentally determine the success of disease control interventions. In commercial pig production growth stages determine animal transportation schedule, thus it generates time-varying contact networks showed to influence the dynamics of disease spread. In this study, we reconstructed pig networks of one Brazilian state from 2017 to 2018, comprising 351,519 movements and 48 million transported pigs.

Risk factors associated with Leishmania exposure among dogs in a rural area of Ilha Solteira, SP, Brazil.

Introduction: We sought to determine risk factors (RFs) associated with the presence of antibodies against Leishmania in dogs from a rural area of Ilha Solteira, SP, Brazil.

Methods: Serum samples were collected from 250 dogs and tested using indirect enzyme-linked immunosorbent assay (ELISA) and indirect immunofluorescence antibody tests (IFATs). Data concerning dogs, their environment, and their owners' knowledge of leishmaniasis were collected using a questionnaire.

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.

Seroprevalence of spp infection and its risk factors among domestic dogs in Bogotá, Colombia.

Different analytical tools were used to determine the seroprevalence of and risk factors associated with spp infection in 192 domestic dogs () in Bogotá, Colombia. Using the microscopic agglutination test (MAT), a battery of 16 serovars were tested. The seroprevalence of spp was calculated as 36.