Using Machine Learning to Predict Swine Movements within a Regional Program to Improve Control of Infectious Diseases in the US.

Between-farm animal movement is one of the most important factors influencing the spread of infectious diseases in food animals, including in the US swine industry. Understanding the structural network of contacts in a food animal industry is prerequisite to planning for efficient production strategies and for effective disease control measures. Unfortunately, data regarding between-farm animal movements in the US are not systematically collected and thus, such information is often unavailable. In this paper, we develop a procedure to replicate the structure of a network, making use of partial data available, and subsequently use the model developed to predict animal movements among sites in 34 Minnesota counties. First, we summarized two networks of swine producing facilities in Minnesota, then we used a machine learning technique referred to as random forest, an ensemble of independent classification trees, to estimate the probability of pig movements between farms and/or markets sites located in two counties in Minnesota. The model was calibrated and tested by comparing predicted data and observed data in those two counties for which data were available. Finally, the model was used to predict animal movements in sites located across 34 Minnesota counties. Variables that were important in predicting pig movements included between-site distance, ownership, and production type of the sending and receiving farms and/or markets. Using a weighted-kernel approach to describe spatial variation in the centrality measures of the predicted network, we showed that the south-central region of the study area exhibited high aggregation of predicted pig movements. Our results show an overlap with the distribution of outbreaks of porcine reproductive and respiratory syndrome, which is believed to be transmitted, at least in part, though animal movements. While the correspondence of movements and disease is not a causal test, it suggests that the predicted network may approximate actual movements. Accordingly, the predictions provided here might help to design and implement control strategies in the region. Additionally, the methodology here may be used to estimate contact networks for other livestock systems when only incomplete information regarding animal movements is available

Trojan hosts: the menace of invasive vertebrates as vectors of pathogens in the Southern Cone of South America

Invasive alien species (IAS) can act as vectors for the introduction of pathogens in ecosystems and their transmission to threatened native species (TNS), leading to biodiversity loss, population reductions and extinctions. We assessed pathogens potentially occurring in a set of IAS in the Southern Cone of South America and identified TNS potentially vulnerable to their effects. Also, we assessed how risk analysis systems proposed or adopted by national authorities in the study region value the importance of pathogens. We identified 324 pathogens in the selected IAS, which could potentially affect 202 TNS. Wild boar (Sus scrofa) was the IAS with the largest number of pathogens (91), followed by domestic dog (Canis familiaris) (62), red deer (Cervus elaphus) (58), rock dove (Columba livia) (37), American vison (Neovison vison) (18), European hare (Lepus europaeus) (17), common starling (Sturnus vulgaris) (12), common slider (Trachemys scripta) (6), and American bullfrog (Lithobates catesbeianus) (2). Most TNS were in the “vulnerable” IUCN category, followed by “endangered” and “critically endangered” species. Bacteria were the most frequently represented pathogens (112), followed by ectoparasites (78), viruses (69), protozoa and other (65). The direct effects of IAS on native wildlife are beginning to be addressed in South America, and their potential impact as pathogen spreaders to native wildlife has remained largely unexplored. Risk analysis systems associated with the introduction of IAS are scarce in this region. Although the existing systems contemplate hazard analyses for the co-introduction of pathogens, they underestimate the potential impact of diseases on TNS. Conservation efforts in the region would benefit from systems which give pathogen risk a relevant place, and from government agencies promoting targeted disease surveillance in IAS and wildlife.Fil: la Sala, Luciano Francisco. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias Biológicas y Biomédicas del Sur. Universidad Nacional del Sur. Departamento de Biología, Bioquímica y Farmacia. Instituto de Ciencias Biológicas y Biomédicas del Sur; ArgentinaFil: Burgos, Julian M.. Marine And Freshwater Research Institute; IslandiaFil: Scorolli, Alberto Luis. Universidad Nacional del Sur. Departamento de Biología, Bioquímica y Farmacia. Grupo de Estudios en Conservación y Manejo; ArgentinaFil: VanderWaal, Kimberly. University of Minnesota; Estados UnidosFil: Zalba, Sergio Martín. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Biología, Bioquímica y Farmacia. Grupo de Estudios en Conservación y Manejo; Argentin

Temporal dynamics of co-circulating lineages of porcine reproductive and respiratory syndrome virus

Porcine Reproductive and Respiratory Syndrome Virus (PRRSV) is the most important endemic pathogen in the U.S. swine industry. Despite control efforts involving improved biosecurity and different vaccination protocols, the virus continues to circulate and evolve. One of the foremost challenges in its control is high levels of genetic and antigenic diversity. Here, we quantify the co-circulation, emergence and sequential turnover of multiple PRRSV lineages in a single swine-producing region in the United States over a span of 9 years (2009–2017). By classifying over 4,000 PRRSV sequences (open-reading frame 5) into phylogenetic lineages and sub-lineages, we document the ongoing diversification and temporal dynamics of the PRRSV population, including the rapid emergence of a novel sub-lineage that appeared to be absent globally pre-2008. In addition, lineage 9 was the most prevalent lineage from 2009 to 2010, but its occurrence fell to 0.5% of all sequences identified per year after 2014, coinciding with the emergence or re-emergence of lineage 1 as the dominant lineage. The sequential dominance of different lineages, as well as three different sub-lineages within lineage 1, is consistent with the immune-mediated selection hypothesis for the sequential turnover in the dominant lineage. As host populations build immunity through natural infection or vaccination toward the most common variant, this dominant (sub-) lineage may be replaced by an emerging variant to which the population is more susceptible. An analysis of patterns of non- synonymous and synonymous mutations revealed evidence of positive selection on immunologically important regions of the genome, further supporting the potential that immune-mediated selection shapes the evolutionary and epidemiological dynamics for this virus. This has important implications for patterns of emergence and re-emergence of genetic variants of PRRSV that have negative impacts on the swine industry. Constant surveillance on PRRSV occurrence is crucial to a better understanding of the epidemiological and evolutionary dynamics of co-circulating viral lineages. Further studies utilizing whole genome sequencing and exploring the extent of cross-immunity between heterologous PRRS viruses could shed further light on PRRSV immunological response and aid in developing strategies that might be able to diminish disease impact

Potential novel N-glycosylation patterns associated with the emergence of new genetic variants of PRRSV-2 in the U.S.

Glycosylation of proteins is a post-translational process where oligosaccharides are attached to proteins, potentially altering their folding, epitope availability, and immune recognition. In Porcine reproductive and respiratory syndrome virus-type 2 (PRRSV-2), positive selection pressure acts on amino acid sites potentially associated with immune escape through glycan shielding. Here, we describe the patterns of potential N-glycosylation sites over time and across different phylogenetic lineages of PRRSV-2 to better understand how these may contribute to patterns of coexistence and emergence of different lineages. We screened 19,179 PRRSV GP5 sequences (2004-2021) in silico for potential N-glycosylated sites. The emergence of novel combinations of N-glycosylated sites coincided with past PRRSV epidemics in the U.S. For lineage L1A, glycosylation at residues 32, 33, 44, 51, and 57 first appeared in 2012, but represented >62% of all L1A sequences by 2015, coinciding with the emergence of the L1A 1-7-4 strain that increased in prevalence from 8 to 86% of all L1A sequences from 2012 to 2015. The L1C 1-4-4 strain that emerged in 2020 also had a distinct N-glycosylation pattern (residues 32, 33, 44, and 51). From 2020 to 2021, this pattern was responsible for 44-47% of the L1C sequences, contrasting to <5% in years prior. Our findings support the hypothesis that antigenic evolution contributes to the sequential dominance of different PRRSV strains and that N-glycosylation patterns may partially account for antigenic differences amongst strains. Further studies on glycosylation and its effect on PRRSV GP5 folding are needed to further understand how glycosylation patterns shape PRRSV occurrence