Simulation modeling of influenza transmission through backyard pig trade networks in a wildlife/livestock interface area

Mathematical modeling at the livestock-wildlife interface: scoping review of drivers of disease transmission between species

Modeling of infectious diseases at the livestock-wildlife interface is a unique subset of mathematical modeling with many innate challenges. To ascertain the characteristics of the models used in these scenarios, a scoping review of the scientific literature was conducted. Fifty-six studies qualified for inclusion. Only 14 diseases at this interface have benefited from the utility of mathematical modeling, despite a far greater number of shared diseases. The most represented species combinations were cattle and badgers (for bovine tuberculosis, 14), and pigs and wild boar [for African (8) and classical (3) swine fever, and foot-and-mouth and disease (1)]. Assessing control strategies was the overwhelming primary research objective (27), with most studies examining control strategies applied to wildlife hosts and the effect on domestic hosts (10) or both wild and domestic hosts (5). In spatially-explicit models, while livestock species can often be represented through explicit and identifiable location data (such as farm, herd, or pasture locations), wildlife locations are often inferred using habitat suitability as a proxy. Though there are innate assumptions that may not be fully accurate when using habitat suitability to represent wildlife presence, especially for wildlife the parsimony principle plays a large role in modeling diseases at this interface, where parameters are difficult to document or require a high level of data for inference. Explaining observed transmission dynamics was another common model objective, though the relative contribution of involved species to epizootic propagation was only ascertained in a few models. More direct evidence of disease spill-over, as can be obtained through genomic approaches based on pathogen sequences, could be a useful complement to further inform such modeling. As computational and programmatic capabilities advance, the resolution of the models and data used in these models will likely be able to increase as well, with a potential goal being the linking of modern complex ecological models with the depth of dynamics responsible for pathogen transmission. Controlling diseases at this interface is a critical step toward improving both livestock and wildlife health, and mechanistic models are becoming increasingly used to explore the strategies needed to confront these diseases.

A stochastic network-based model to simulate farm-level transmission of African swine fever virus in Vietnam

African swine fever virus is highly contagious, and mortality rates reach up to 100% depending on the host, virus dose, and the transmission routes. The main objective of this study was to develop a network-based simulation model for the farm-level transmission of ASF virus to evaluate the impact of changes in farm connectivity on ASF spread in Vietnam. A hypothetical population of 1,000 pig farms was created and used for the network-based simulation, where each farm represented a node, and the connection between farms represented an edge. The three scenarios modelled in this way (baseline, low, and high) evaluated the impact of connectivity on disease transmission. The median number of infected farms was higher as the connectivity increased (low: 659, baseline: 968 and high: 993). In addition, we evaluated the impact of the culling strategy on the number of infected farms. A total of four scenarios were simulated depending on the timing of culling after a farm was infected. We found that the timing of culling at 16, 12, 8, and 6 weeks had resulted in a reduction of the number of median infected farms by 81.92%, 91.63%, 100%, and 100%, respectively. Finally, our evaluation of the implication of stability of ties between farms indicated that if the farms were to have the same trading partners for at least six months could significantly reduce the median number of infected farms to two (95th percentile: 413) than in the basic model. Our study showed that pig movements among farms had a significant influence on the transmission dynamics of ASF virus. In addition, we found that the either timing of culling, reduction in the number of trading partners each farm had, or decreased mean contact rate during the outbreaks were essential to prevent or stop further outbreaks.

Molecular identification and phylogenetic characterization of influenza A virus at a wildlife-livestock interface in Mexico

Influenza A virus (IAV) outbreaks constitute a constant threat to public health and pose a remarkable impact on socio-economic systems worldwide. Interactions between wild and domestic birds, humans and swine can lead to spillover events. Backyard livestock systems in proximity to wetlands represent high-risk areas for viral spread. However, some gaps remain in our knowledge of IAV transmission at the wildlife-livestock interface in Mexico. Hence, the study aimed at molecular identification and phylogenetic characterization of IAV in the wild duck-backyard livestock interface at a wetland of Mexico. A total of 875 animals were tested by real-time RT-PCR (qRT-PCR). We detected IAV in 3.68% of the wild ducks sampled during the winter season 2016-2017. Nonetheless, the samples obtained from backyard poultry and swine tested negative. The highest IAV frequency (11.10%) was found in the Mexican duck (Anas diazi). Subtypes H1N1, H3N2 and H5N2 were detected. Phylogenetic analyses revealed that IAV detected in wild birds from the Lerma wetlands was mostly related to swine and poultry IAV strains previously isolated in the United States and Mexico. Except, the UIFMVZ377/H5N2 related to North American waterbirds. In conclusion, the co-circulation of three IAV subtypes in wild ducks close to backyard farms in Mexico, as well as the local identification of influenza viruses genetically related to Mexican and North American IAV strains, highlights the importance of the Lerma marshes for influenza surveillance given the close interaction among wild birds, poultry, pigs and humans.