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Dairy farming in northern Thailand is expanding, with dairy cattle populations increasing up to 8% per year. In addition, disease outbreaks frequently occur in this region, especially foot-and-mouth disease and bovine tuberculosis. Our goal was to quantify the underlying pattern of dairy cattle movements in the context of infectious disease surveillance and control as movements have been identified as risk factors for several infectious diseases. Movements at district levels within the northern region and between the northern and other regions from 2010 to 2017 were recorded by the Department of Livestock Development. Analyzed data included origin, destination, date and purpose of the movement, type of premise of origin and destination, and type and number of moved cattle. Social network analysis was performed to demonstrate patterns of dairy cattle movement within and between regions. The total numbers of movements and moved animals were 3,906 and 180,305, respectively. Decreasing trends in both the number of cattle moved and the number of movements were observed from 2010 to 2016, with increases in 2017. The majority (98%) of the animals moved were male dairy calves, followed by dairy cows (1.7%). The main purpose of the movements was for slaughter (96.3%). Most movements (67.4%) were shipments from central to northern regions, involving 87.1% of cattle moved. By contrast, 56% of the movements for growing and selling purposes occurred within the northern region, commonly involving dairy cows. Constructed movement networks showed heterogeneity of connections among districts. Of 110 districts, 28 were found to be influential to the movement networks, among which 11 districts showed high centrality measures in multiple networks stratified for movement purposes and regions, including eight districts in the northern and one district in each of the central, eastern, and lower northeastern regions of Thailand. These districts were more highly connected than others in the movement network, which may be important for disease transmission, surveillance, and control., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Boonyayatra, Wang, Singhla, Kongsila, VanderWaal and Wells.)

Identifying drivers of transmission-especially of emerging pathogens-is a formidable challenge for proactive disease management efforts. While close social interactions can be associated with microbial sharing between individuals, and thereby imply dynamics important for transmission, such associations can be obscured by the influences of factors such as shared diets or environments. Directly-transmitted viral agents, specifically those that are rapidly evolving such as many RNA viruses, can allow for high-resolution inference of transmission, and therefore hold promise for elucidating not only which individuals transmit to each other, but also drivers of those transmission events. Here, we tested a novel approach in the Florida panther, which is affected by several directly-transmitted feline retroviruses. We first inferred the transmission network for an apathogenic, directly-transmitted retrovirus, feline immunodeficiency virus (FIV), and then used exponential random graph models to determine drivers structuring this network. We then evaluated the utility of these drivers in predicting transmission of the analogously transmitted, pathogenic agent, feline leukemia virus (FeLV), and compared FIV-based predictions of outbreak dynamics against empirical FeLV outbreak data. FIV transmission was primarily driven by panther age class and distances between panther home range centroids. FIV-based modeling predicted FeLV dynamics similarly to common modeling approaches, but with evidence that FIV-based predictions captured the spatial structuring of the observed FeLV outbreak. While FIV-based predictions of FeLV transmission performed only marginally better than standard approaches, our results highlight the value of proactively identifying drivers of transmission-even based on analogously-transmitted, apathogenic agents-in order to predict transmission of emerging infectious agents. The identification of underlying drivers of transmission, such as through our workflow here, therefore holds promise for improving predictions of pathogen transmission in novel host populations, and could provide new strategies for proactive pathogen management in human and animal systems., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Gilbertson, Fountain-Jones, Malmberg, Gagne, Lee, Kraberger, Kechejian, Petch, Chiu, Onorato, Cunningham, Crooks, Funk, Carver, VandeWoude, VanderWaal and Craft.)

While the widespread and endemic circulation of porcine reproductive and respiratory syndrome virus type 2 (PRRSV-2) causes persistent economic losses to the U.S. swine industry, unusual increases of severe cases associated with the emergence of new genetic variants are a major source of concern for pork producers. Between 2020 and 2021, such an event occurred across pig production sites in the Midwestern U.S. The emerging viral clade is referred to as the novel sub-lineage 1C (L1C) 1-4-4 variant. This genetic classification is based on the open reading frame 5 (ORF5) gene. However, although whole genome sequence (WGS) suggested that this variant represented the emergence of a new strain, the true evolutionary history of this variant remains unclear. To better elucidate the variant's evolutionary history, we conducted a recombination detection analysis, time-scaled phylogenetic estimation, and discrete trait analysis on a set of L1C-1-4-4 WGSs ( n = 19) alongside other publicly published WGSs ( n = 232) collected over a 26-year period (1995-2021). Results from various methodologies consistently suggest that the novel L1C variant was a descendant of a recombinant ancestor characterized by recombination at the ORF1a gene between two segments that would be otherwise classified as L1C and L1A in the ORF5 gene. Based on analysis of different WGS fragments, the L1C-1-4-4 variant descended from an ancestor that existed around late 2018 to early 2019, with relatively high substitution rates in the proximal ORF1a as well as ORF5 regions. Two viruses from 2018 were found to be the closest relatives to the 2020-21 outbreak strain but had different recombination profiles, suggesting that these viruses were not direct ancestors. We also assessed the overall frequency of putative recombination amongst ORF5 and other parts of the genome and found that recombination events which leave detectable numbers of descendants are not common. However, the rapid spread and high virulence of the L1C-1-4-4 recombinant variant demonstrates that inter-sub-lineage recombination occasionally found amongst the U.S. PRRSV-2 might be an evolutionary mechanisms that contributed to this emergence. More generally, recombination amongst PRRSV-2 accelerates genetic change and increases the chance of the emergence of high fitness variants., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Pamornchainavakul, Kikuti, Paploski, Makau, Rovira, Corzo and VanderWaal.)

We report an ongoing regional outbreak of an emerging porcine reproductive and respiratory syndrome virus (PRRSV2) variant within Lineage 1C affecting 154 breeding and grow-finishing sites in the Midwestern U.S. Transmission seemed to have occurred in two waves, with the first peak of weekly cases occurring between October and December 2020 and the second starting in April 2021. Most of cases occurred within a 120 km radius. Both orf5 and whole genome sequencing results suggest that this represents the emergence of a new variant within Lineage 1C distinct from what has been previously circulating. A case-control study was conducted with 50 cases (sites affected with the newly emerged variant) and 58 controls (sites affected with other PRRSV variants) between October and December 2020. Sites that had a market vehicle that was not exclusive to the production system had 0.04 times the odds of being a case than a control. A spatial cluster (81.42 km radius) with 1.68 times higher the number of cases than controls was found. The average finishing mortality within the first 4 weeks after detection was higher amongst cases (4.50%) than controls (0.01%). The transmission of a highly similar virus between different farms carrying on trough spring rises concerns for the next high transmission season of PRRS., Competing Interests: MS is employed by Schwartz Farms Inc. PY, BL, and LB are employed by Swine Vet Center. DM is employed by New Fashion Pork. BR is employed by Fairmont Veterinary Clinic. PT is employed by Iowa Select Farms. LF is employed by Protein Sources Management. MA is employed by Holden Farms Inc. MH, TB, and BS are employed by The Maschhoffs LLC. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Kikuti, Paploski, Pamornchainavakul, Picasso-Risso, Schwartz, Yeske, Leuwerke, Bruner, Murray, Roggow, Thomas, Feldmann, Allerson, Hensch, Bauman, Sexton, Rovira, VanderWaal and Corzo.)

Contact network analysis has become a vital tool for conceptualizing the spread of pathogens in animal populations and is particularly useful for understanding the implications of heterogeneity in contact patterns for transmission. However, the transmission of most pathogens cannot be simplified to a single mode of transmission and, thus, a single definition of contact. In addition, host-pathogen interactions occur in a community context, with many pathogens infecting multiple host species and most hosts being infected by multiple pathogens. Multilayer networks provide a formal framework for researching host-pathogen systems in which multiple types of transmission-relevant interactions, defined as network layers, can be analyzed jointly. Here, we provide an overview of multilayer network analysis and review applications of this novel method to epidemiological research questions. We then demonstrate the use of this technique to analyze heterogeneity in direct and indirect contact patterns amongst swine farms in the United States. When contact among nodes can be defined in multiple ways, a multilayer approach can advance our ability to use networks in epidemiological research by providing an improved approach for defining epidemiologically relevant groups of interacting nodes and changing the way we identify epidemiologically important individuals such as superspreaders., (Copyright © 2020 Kinsley, Rossi, Silk and VanderWaal.)

In the event of a Food and Mouth Disease (FMD) outbreak in the United States, an infected livestock premises is likely to result in a high number of carcasses (swine and/or cattle) as a result of depopulation. If relocating infected carcasses to an off-site disposal site is allowed, the virus may have increased opportunity to spread to uninfected premises and result in exposure of susceptible livestock. A stochastic within-herd disease spread model was used to predict the time to detect the disease by observation of clinical signs within the herd, and the number of animals in different disease stages over time. Expert opinion was elicited to estimate depopulation parameters in various scenarios. Disease detection was assumed when 5% of the population showed clinical signs by direct observation. Time to detection (5 and 95th percentile values) was estimated for all swine farm sizes (500-10,000 head) ranged from 102 to 282 h, from 42 to 216 h for all dairy cattle premises sizes (100-2,000 head) and from 66 to 240 h for all beef cattle premises sizes (5,000-50,000 head). Total time from infection to beginning depopulation (including disease detection and confirmation) for the first FMD infected case was estimated between 8.5-14.3 days for swine, 6-12.8 days for dairy or beef cattle premises. Total time estimated for subsequent FMD cases was between 6.8-12.3 days for swine, 4.3-10.8 days for dairy and 4.5-10.5 days for beef cattle premises. On an average sized operation, a sizable proportion of animals in the herd (34-56% of swine, 48-60% of dairy cattle, and 47-60% of beef cattle for the first case and 49-60% of swine, 55-60% of dairy cattle, 56-59% of beef cattle for subsequent cases) would be viremic at the time of beginning depopulation. A very small fraction of body fluids from the carcasses (i.e., 1 mL) would contain virus that greatly exceeds the minimum infectious dose by oral (4-7x) or inhalation (7-13x) route for pigs and cattle., (Copyright © 2020 Walz, Middleton, Sampedro, VanderWaal, Malladi and Goldsmith.)

In the event of a foot-and-mouth disease (FMD) outbreak in the United States, "stamping out" FMD infected premises has been proposed as the method of choice for the control of outbreaks. However, if a widespread, catastrophic FMD outbreak in the U.S. were to occur, alternative solutions to stamping out may be required, particularly for large feedlots with over 10,000 cattle. Such strategies include moving cattle from infected or not known to be infected operations to slaughter facilities either with or without prior implementation of vaccination. To understand the risk of these strategies, it is important to estimate levels of herd viremia. Multiple factors must be considered when determining risk and feasibility of moving cattle from a feedlot to a slaughter facility during an FMD outbreak. In addition to modeling within-herd disease spread to estimate prevalence of viremic animals, we explore potential pathways for viral spread associated with the movement of asymptomatic beef cattle (either pre-clinical or recovered) from an infected feedlot premises to offsite harvest facilities. This analysis was proactive in nature, however evaluation of the likelihood of disease spread relative to disease (infection) phase, time of movement, and vaccination status are all factors which should be considered in managing and containing a large-scale FMD outbreak in the United States., (Copyright © 2020 Walz, Evanson, Sampedro, VanderWaal and Goldsmith.)

Livestock producers have traditionally been reluctant to share information related to their business, including data on health status of their animals, which, sometimes, has impaired the ability to implement surveillance programs. However, during the last decade, swine producers in the United States (US) and other countries have voluntarily begun to share data for the control and elimination of specific infectious diseases, such as the porcine reproductive and respiratory syndrome virus (PRRSv). Those surveillance programs have played a pivotal role in bringing producers and veterinarians together for the benefit of the industry. Examples of situations in which producers have decided to voluntarily share data for extended periods of time to support applied research and, ultimately, disease control in the absence of a regulatory framework have rarely been documented in the peer-reviewed literature. Here, we provide evidence of a national program for voluntary sharing of disease status data that has helped the implementation of surveillance activities that, ultimately, allowed the generation of critically important scientific information to better support disease control activities. Altogether, this effort has supported, and is supporting, the design and implementation of prevention and control approaches for the most economically devastating swine disease affecting the US. The program, which has been voluntarily sustained and supported over an extended period of time by the swine industry in the absence of any regulatory framework and that includes data on approximately 50% of the sow population in the US, represents a unique example of a livestock industry self-organized surveillance program to generate scientific-driven solutions for emerging swine health issues in North America.

Foot-and-mouth disease (FMD), caused by FMD virus (FMDV; Aphthovirus, Picornaviridae ), is a highly contagious and economically important disease of cloven-hoofed domestic livestock and wildlife species worldwide. Subsequent to the clinical phase of FMD, a large proportion of FMDV-infected ruminants become persistently infected carriers, defined by detection of FMDV in oropharyngeal fluid (OPF) samples 28 days or more post-infection. The goal of this prospective study was to characterize the FMD carrier state in cattle subsequent to natural infection under typical husbandry practices in Vietnam. Ten persistently infected cattle on eight farms in the Long An province in southern Vietnam were monitored by monthly screening of serum and oropharyngeal fluid samples for 12 months. To assess transmission from FMDV carriers, 16 naïve cattle were intentionally brought into direct contact with the persistently infected animals for 6 months, and were monitored by clinical and laboratory methods. The restricted mean duration of the FMD carrier state was 27.7 months, and the rate of decrease of the proportion of carrier animals was 0.03 per month. There was no evidence of transmission to naïve animals throughout the study period. Additionally, there was no detection of FMDV infection or seroconversion in three calves born to carrier animals during the study. The force of infection for carrier-to-contact transmission was 0 per month, with upper 95% confidence limit of 0.064 per month. Phylogenetic analysis of viral protein 1 (VP1) coding sequences obtained from carriers indicated that all viruses recovered in this study belonged to the O/ME-SA/PanAsia lineage, and grouped phylogenetically with temporally and geographically related viruses. Analysis of within-host evolution of FMDV, based upon full-length open reading frame sequences recovered from consecutive samples from one animal, indicated that most of the non-synonymous changes occurred in L pro , VP2, and VP3 protein coding regions. This study suggests that the duration of FMDV persistent infection in cattle may be longer than previously recognized, but the risk of transmission is low. Additional novel insights are provided into within-host viral evolution under natural conditions in an endemic setting.

The increasing availability and complexity of data has led to new opportunities and challenges in veterinary epidemiology around how to translate abundant, diverse, and rapidly growing "big" data into meaningful insights for animal health. Big data analytics are used to understand health risks and minimize the impact of adverse animal health issues through identifying high-risk populations, combining data or processes acting at multiple scales through epidemiological modeling approaches, and harnessing high velocity data to monitor animal health trends and detect emerging health threats. The advent of big data requires the incorporation of new skills into veterinary epidemiology training, including, for example, machine learning and coding, to prepare a new generation of scientists and practitioners to engage with big data. Establishing pipelines to analyze big data in near real-time is the next step for progressing from simply having "big data" to create "smart data," with the objective of improving understanding of health risks, effectiveness of management and policy decisions, and ultimately preventing or at least minimizing the impact of adverse animal health issues.

Porcine reproductive and respiratory syndrome (PRRS) is, arguably, the most impactful disease for the North American swine industry, due to its known considerable economic losses. The Swine Health Monitoring Project (SHMP) monitors and reports weekly new PRRS cases in 766 sow herds across the US. The time-dependent reproduction number (TD-R) is a measure of a pathogen's transmissibility. It may serve to capture and report PRRS virus (PRRSV) spread at the regional and system levels. The primary objective of the study here was to estimate the TD-R values for PRRSV using regional and system-level PRRS data, and to contrast it with commonly used metrics of disease, such as incidence estimates and space-time clusters. The second objective was to test whether the estimated TD-Rs were homogenous across four US regions. Retrospective monthly incidence data (2009-2016) were available from the SHMP. The dataset was divided into four regions based on location of participants, and demographic and environmental features, namely, South East (North Carolina), Upper Midwest East (UME, Minnesota/Iowa), Upper Midwest West (Nebraska/South Dakota), and South (Oklahoma panhandle). Generation time distributions were fit to incidence data for each region, and used to calculate the TD-Rs. The Kruskal-Wallis test was used to determine whether the median TD-Rs differed across the four areas. Furthermore, we used a space-time permutation model to assess spatial-temporal patterns for the four regions. Results showed TD-Rs were right skewed with median values close to "1" across all regions, confirming that PRRS has an overall endemic nature. Variation in the TD-R patterns was noted across regions and production systems. Statistically significant periods of PRRSV spread (TD-R > 1) were identified for all regions except UME. A minimum of three space-time clusters were detected for all regions considering the time period examined herein; and their overlap with "spreader events" identified by the TD-R method varied according to region. TD-Rs may help to measure PRRS spread to understand, in quantitative terms, disease spread, and, ultimately, support the design, implementation, and monitoring of interventions aimed at mitigating the impact of PRRSV spread 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.

Lumpy skin disease virus (LSDV) is an infectious disease of cattle that can have severe economic implications. New LSD outbreaks are currently circulating in the Middle East (ME). Since 2012, severe outbreaks were reported in cattle across the region. Characterizing the spatial and temporal dynamics of LSDV in cattle populations is prerequisite for guiding successful surveillance and control efforts at a regional level in the ME. Here, we aim to model the ecological niche of LSDV and identify epidemic progression patterns over the course of the epidemic. We analyzed publically available outbreak data from the ME for the period 2012-2015 using presence-only maximum entropy ecological niche modeling and the time-dependent method for the estimation of the effective reproductive number (R-TD). High-risk areas (probability >0.60) for LSDV identified by ecological niche modeling included parts of many northeastern ME countries, though Israel and Turkey were estimated to be the most suitable locations for occurrence of LSDV outbreaks. The most important environmental predictors that contributed to the ecological niche of LSDV included annual precipitation, land cover, mean diurnal range, type of livestock production system, and global livestock densities. Average monthly effective R-TD was equal to 2.2 (95% CI: 1.2, 3.5), whereas the largest R-TD was estimated in Israel (R-TD = 22.2, 95 CI: 15.2, 31.5) in September 2013, which indicated that the demographic and environmental conditions during this period were suitable to LSDV super-spreading events. The sharp drop of Isreal's inferred R-TD in the following month reflected the success of their 2013 vaccination campaign in controlling the disease. Our results identified areas in which underreporting of LSDV outbreaks may have occurred. More epidemiological information related to cattle populations are needed to further improve the inferred spatial and temporal characteristics of currently circulating LSDV. However, the methodology presented here may be useful in guiding the design of risk-based surveillance and control programs in the region as well as aid in the formulation of epidemic preparedness plans in neighboring LSDV-free countries.