Search

Middle East respiratory syndrome coronavirus (MERS-CoV) remains a notable disease and poses a significant threat to global public health. The Arabian Peninsula is considered a major global epicentre for the disease and the virus has crossed regional and continental boundaries since 2012. In this study, we focused on exploring the temporal dynamics of MERS-CoV in human populations in the Arabian Peninsula between 2012 and 2017, using publicly available data on case counts and combining two analytical methods. Disease progression was assessed by quantifying the time-dependent reproductive number (TD-Rs), while case series temporal pattern was modelled using the AutoRegressive Integrated Moving Average (ARIMA). We accounted for geographical variability between three major affected regions in Saudi Arabia including Eastern Province, Riyadh and Makkah. In Saudi Arabia, the epidemic size was large with TD-Rs >1, indicating significant spread until 2017. In both Makkah and Riyadh regions, the epidemic progression reached its peak in April 2014 (TD-Rs > 7), during the highest incidence period of MERS-CoV cases. In Eastern Province, one unique super-spreading event (TD-R > 10) was identified in May 2013, which comprised of the most notable cases of human-to-human transmission. Best-fitting ARIMA model inferred statistically significant biannual seasonality in Riyadh region, a region characterised by heavy seasonal camel-related activities. However, no statistical evidence of seasonality was identified in Eastern Province and Makkah. Instead, both areas were marked by an endemic pattern of cases with sporadic outbreaks. Our study suggested new insights into the epidemiology of the virus, including inferences about epidemic progression and evidence for seasonality. Despite the inherent limitations of the available data, our conclusions provide further guidance to currently implement risk-based surveillance in high-risk populations and, subsequently, improve related interventions strategies against the epidemic at country and regional levels. [ABSTRACT FROM AUTHOR]

Summary: Foot‐and‐mouth disease (FMD) is an important transboundary disease with substantial economic impacts. Although between‐herd transmission of the disease has been well studied, studies focusing on within‐herd transmission using farm‐level outbreak data are rare. The aim of this study was to estimate parameters associated with within‐herd transmission, host physiological factors and FMD virus (FMDV) persistence using data collected from an outbreak that occurred at a large, organized dairy farm in India. Of 1,836 regularly vaccinated, adult dairy cattle, 222 had clinical signs of FMD over a 39‐day period. Assuming homogenous mixing, a frequency‐dependent compartmental model of disease transmission was built. The transmission coefficient and basic reproductive number were estimated to be between 16.2–18.4 and 67–88, respectively. Non‐pregnant animals were more likely to manifest clinical signs of FMD as compared to pregnant cattle. Based on oropharyngeal fluid (probang) sampling and FMDV‐specific RT‐PCR, four of 36 longitudinally sampled animals (14%) were persistently infected carriers 10.5 months post‐outbreak. There was no statistical difference between subclinical and clinically infected animals in the duration of the carrier state. However, prevalence of NSP‐ELISA antibodies differed significantly between subclinical and clinically infected animals 12 months after the outbreak with 83% seroprevalence amongst clinically infected cattle compared to 69% of subclinical animals. This study further elucidates within‐herd FMD transmission dynamics during the acute‐phase and characterizes duration of FMDV persistence and seroprevalence of FMD under natural conditions in an endemic setting. [ABSTRACT FROM AUTHOR]

Importance: In this study, comprehensive analysis of 82,237 global porcine reproductive and respiratory syndrome virus type 2 (PRRSV-2) open reading frame 5 sequences spanning from 1989 to 2021 refined PRRSV-2 genetic classification system, which defines 11 lineages and 21 sublineages and provides flexibility for growth if additional lineages, sublineages, or more granular classifications are needed in the future. Geographic distribution and temporal changes of PRRSV-2 were investigated in detail. This is a thorough study describing the molecular epidemiology of global PRRSV-2. In addition, the reference sequences based on the refined genetic classification system are made available to the public for future epidemiological and diagnostic applications worldwide. The data from this study will facilitate global standardization and application of PRRSV-2 genetic classification., Competing Interests: The authors declare no conflict of interest.

Seasonal variation in habitat use and animal behavior can alter host contact patterns with potential consequences for pathogen transmission dynamics. The endangered Florida panther (Puma concolor coryi) has experienced significant pathogen-induced mortality and continues to be at risk of future epidemics. Prior research has found increased panther movement in Florida's dry versus wet seasons, which may affect panther population connectivity and seasonally increase potential pathogen transmission. Our objective was to determine if Florida panthers are more spatially connected in dry seasons relative to wet seasons, and test if identified connectivity differences resulted in divergent predicted epidemic dynamics. We leveraged extensive panther telemetry data to construct seasonal panther home range overlap networks over an 11 year period. We tested for differences in network connectivity, and used observed network characteristics to simulate transmission of a broad range of pathogens through dry and wet season networks. We found that panthers were more spatially connected in dry seasons than wet seasons. Further, these differences resulted in a trend toward larger and longer pathogen outbreaks when epidemics were initiated in the dry season. Our results demonstrate that seasonal variation in behavioral patterns-even among largely solitary species-can have substantial impacts on epidemic dynamics., (© 2023. Springer Nature Limited.)

Predicting what factors promote or protect populations from infectious disease is a fundamental epidemiological challenge. Social networks, where nodes represent hosts and edges represent direct or indirect contacts between them, are important in quantifying these aspects of infectious disease dynamics. However, how network structure and epidemic parameters interact in empirical networks to promote or protect animal populations from infectious disease remains a challenge. Here we draw on advances in spectral graph theory and machine learning to build predictive models of pathogen spread on a large collection of empirical networks from across the animal kingdom. We show that the spectral features of an animal network are powerful predictors of pathogen spread for a variety of hosts and pathogens and can be a valuable proxy for the vulnerability of animal networks to pathogen spread. We validate our findings using interpretable machine learning techniques and provide a flexible web application for animal health practitioners to assess the vulnerability of a particular network to pathogen spread.

The United States (U.S.) swine industry has struggled to control porcine reproductive and respiratory syndrome (PRRS) for decades, yet the causative virus, PRRSV-2, continues to circulate and rapidly diverges into new variants. In the swine industry, the farm is typically the epidemiological unit for monitoring, prevention, and control; breaking transmission among farms is a critical step in containing disease spread. Despite this, our understanding of farm transmission still is inadequate, precluding the development of tailored control strategies. Therefore, our objective was to infer farm-to-farm transmission links, estimate farm-level transmissibility as defined by reproduction numbers ( R ), and identify associated risk factors for transmission using PRRSV-2 open reading frame 5 (ORF5) gene sequences, animal movement records, and other data from farms in a swine-dense region of the U.S. from 2014 to 2017. Timed phylogenetic and transmission tree analyses were performed on three sets of sequences ( n  = 206) from 144 farms that represented the three largest genetic variants of the virus in the study area. The length of inferred pig-to-pig infection chains that corresponded to pairs of farms connected via direct animal movement was used as a threshold value for identifying other feasible transmission links between farms; these links were then transformed into farm-to-farm transmission networks and calculated farm-level R -values. The median farm-level R was one (IQR = 1-2), whereas the R value of 28% of farms was more than one. Exponential random graph models were then used to evaluate the influence of farm attributes and/or farm relationships on the occurrence of farm-to-farm transmission links. These models showed that, even though most transmission events cannot be directly explained by animal movement, movement was strongly associated with transmission. This study demonstrates how integrative techniques may improve disease traceability in a data-rich era by providing a clearer picture of regional disease transmission., Competing Interests: The authors declare no conflict of interest., (© 2023 The Authors. Evolutionary Applications published by John Wiley & Sons Ltd.)

Describing PRRSV whole-genome viral diversity data over time within the host and within-farm is crucial for a better understanding of viral evolution and its implications. A cohort study was conducted at one naïve farrow-to-wean farm reporting a PRRSV outbreak. All piglets 3-5 days of age (DOA) born to mass-exposed sows through live virus inoculation with the recently introduced wild-type virus two weeks prior were sampled and followed up at 17-19 DOA. Samples from 127 piglets were individually tested for PRRSV by RT-PCR and 100 sequences were generated using Oxford Nanopore Technologies chemistry. Female piglets had significantly higher median Ct values than males (15.5 vs. 13.7, Kruskal-Wallis p < 0.001) at 3-5 DOA. A 52.8% mortality between sampling points was found, and the odds of dying by 17-19 DOA decreased with every one unit increase in Ct values at 3-5 DOA (OR = 0.76, 95% CI 0.61-0.94, p = 0.01). Although the within-pig percent nucleotide identity was overall high (99.7%) between 3-5 DOA and 17-19 DOA samples, ORFs 4 and 5a showed much lower identities (97.26% and 98.53%, respectively). When looking solely at ORF5, 62% of the sequences were identical to the 3-5 DOA consensus. Ten and eight regions showed increased nucleotide and amino acid genetic diversity, respectively, all found throughout ORFs 2a/2b, 4, 5a/5, 6, and 7.

The repeated emergence of new genetic variants of PRRSV-2, the virus that causes porcine reproductive and respiratory syndrome (PRRS), reflects its rapid evolution and the failure of previous control efforts. Understanding spatiotemporal heterogeneity in variant emergence and spread is critical for future outbreak prevention. Here, we investigate how the pace of evolution varies across time and space, identify the origins of sub-lineage emergence, and map the patterns of the inter-regional spread of PRRSV-2 Lineage 1 (L1)-the current dominant lineage in the U.S. We performed comparative phylogeographic analyses on subsets of 19,395 viral ORF5 sequences collected across the U.S. and Canada between 1991 and 2021. The discrete trait analysis of multiple spatiotemporally stratified sampled sets ( n = 500 each) was used to infer the ancestral geographic region and dispersion of each sub-lineage. The robustness of the results was compared to that of other modeling methods and subsampling strategies. Generally, the spatial spread and population dynamics varied across sub-lineages, time, and space. The Upper Midwest was a main spreading hotspot for multiple sub-lineages, e.g., L1C and L1F, though one of the most recent emergence events (L1A(2)) spread outwards from the east. An understanding of historical patterns of emergence and spread can be used to strategize disease control and the containment of emerging variants.

The control of porcine reproductive and respiratory syndrome (PRRS) remains a significant challenge due to the genetic and antigenic variability of the causative virus (PRRSV). Predominantly, PRRSV management includes using vaccines and live virus inoculations to confer immunity against PRRSV on farms. While understanding cross-protection among strains is crucial for the continued success of these interventions, understanding how genetic diversity translates to antigenic diversity remains elusive. We developed machine learning algorithms to estimate antigenic distance in silico , based on genetic sequence data, and identify differences in specific amino acid sites associated with antigenic differences between viruses. First, we obtained antigenic distance estimates derived from serum neutralization assays cross-reacting PRRSV monospecific antisera with virus isolates from 27 PRRSV1 viruses circulating in Europe. Antigenic distances were weakly to moderately associated with ectodomain amino acid distance for open reading frames (ORFs) 2 to 4 (ρ < 0.2) and ORF5 (ρ = 0.3), respectively. Dividing the antigenic distance values at the median, we then categorized the sera-virus pairs into two levels: low and high antigenic distance (dissimilarity). In the machine learning models, we used amino acid distances in the ectodomains of ORFs 2 to 5 and site-wise amino acid differences between the viruses as potential predictors of antigenic dissimilarity. Using mixed-effect gradient boosting models, we estimated the antigenic distance (high versus low) between serum-virus pairs with an accuracy of 81% (95% confidence interval, 76 to 85%); sensitivity and specificity were 86% and 75%, respectively. We demonstrate that using sequence data we can estimate antigenic distance and potential cross-protection between PRRSV1 strains. IMPORTANCE Understanding cross-protection between cocirculating PRRSV1 strains is crucial to reducing losses associated with PRRS outbreaks on farms. While experimental studies to determine cross-protection are instrumental, these in vivo studies are not always practical or timely for the many cocirculating and emerging PRRSV strains. In this study, we demonstrate the ability to rapidly estimate potential immunologic cross-reaction between different PRRSV1 strains in silico using sequence data routinely collected by production systems. These models can provide fast turn-around information crucial for improving PRRS management decisions such as selecting vaccines/live virus inoculation to be used on farms and assessing the risk of outbreaks by emerging strains on farms previously exposed to certain PRRSV strains and vaccine development among others.

Bayesian space-time regression models are helpful tools to describe and predict the distribution of infectious disease outbreaks and to delineate high-risk areas for disease control. In these models, structured and unstructured spatial and temporal effects account for various forms of non-independence amongst case counts across spatial units. Structured spatial effects capture correlations in case counts amongst neighboring provinces arising from shared risk factors or population connectivity. For highly mobile populations, spatial adjacency is an imperfect measure of connectivity due to long-distance movement, but we often lack data on host movements. Phylogeographic models inferring routes of viral dissemination across a region could serve as a proxy for patterns of population connectivity. The objective of this study was to investigate whether the effects of population connectivity in space-time regressions of case counts were better captured by spatial adjacency or by inferences from phylogeographic analyses. To compare these two approaches, we used foot-and-mouth disease virus (FMDV) outbreak data from across Vietnam as an example. We identified that accounting for virus movement through phylogeographic analysis serves as a better proxy for population connectivity than spatial adjacency in spatial-temporal risk models. This approach may contribute to design surveillance activities in countries lacking movement data.

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.

Foot-and-mouth disease virus (FMDV) SAT2 sequences were acquired from Cape buffalo in Kenya in 2016, from either primary passage ( n = 38) or plaque purification of dually SAT1/SAT2-infected samples ( n = 61). All samples were derived from asymptomatic animals. These sequences contribute to our understanding of FMDV diversity in reservoirs and during subclinical FMDV infections.

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.)

Potential interactions among co-circulating viral strains in host populations are often overlooked in the study of virus transmission. However, these interactions probably shape transmission dynamics by influencing host immune responses or altering the relative fitness among co-circulating strains. In this Review, we describe multi-strain dynamics from ecological and evolutionary perspectives, outline scales in which multi-strain dynamics occur and summarize important immunological, phylogenetic and mathematical modelling approaches used to quantify interactions among strains. We also discuss how host-pathogen interactions influence the co-circulation of pathogens. Finally, we highlight outstanding questions and knowledge gaps in the current theory and study of ecological and evolutionary dynamics of multi-strain viruses., (© 2022. Springer Nature Limited.)

Agricultural use of antimicrobials in food animal production may contribute to the global emergence of antimicrobial resistance (AMR). However, considerable gaps exist in research on the use of antimicrobial drugs (AMDs) in food animals in small-scale production systems in low- and middle-income countries, despite the minimal regulation of antimicrobials in such regions. The aim of this study was to identify factors that may influence AMD use in livestock among pastoral communities in Kenya. We collected data related to household and herd demographics, herd health, and herd management from 55 households in the Maasai Mara ecosystem, Kenya, between 2018 and 2019. We used multi-model logistic regression inference (supervised machine learning) to ascertain trends in AMD use within these households. AMD use in cattle was significantly associated with AMD use in sheep and goats (p = 0.05), implying that decisions regarding AMD use in cattle or sheep and goats were interdependent. AMD use in sheep and goats was negatively associated with vaccination against the foot and mouth disease (FMD) virus in cattle (OR = 0.06, 95% CI 0.01-0.67, p = 0.02). Less AMD use was observed for vaccine-preventable diseases like contagious ecthyma when households had access to state veterinarians (OR = 0.06, p = 0.05, 95% CI 0.004-0.96). Overall, decisions to use AMDs were associated with vaccine usage, occurrence of respiratory diseases, and access to animal health advice. This hypothesis-generating study suggests that applying community-centric methods may be necessary to understand the use of AMDs in pastoral communities., (© 2022. The Author(s), under exclusive licence to Springer Nature B.V.)

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.)

Foot-and-mouth disease (FMD) is endemic in India, where circulation of serotypes O, A and Asia1 is frequent. Here, we provide an epidemiological assessment of the ongoing mass vaccination programs in regard to post-vaccination monitoring and outbreak occurrence. The objective of this study was assessing the contribution of mass vaccination campaigns in reducing the risk of FMD in India from 2008 to 2016 by evaluating sero-monitoring data and modelling the spatiotemporal dynamics of reported outbreaks. Through analyzing antibody titre data from >1 million animals sampled as part of pre- and post-vaccination monitoring, we show that the percent of animals with inferred immunological protection (based on ELISA) was highly variable across states but generally increased through time. In addition, the number of outbreaks in a state was negatively correlated with the percent of animals with inferred protection. We then analyzed the distribution of reported FMD outbreaks across states using a Bayesian space-time model. This approach provides better acuity to disentangle the effect of mass vaccination programs on outbreak occurrence, while accounting for other factors that contribute to spatiotemporal variability in outbreak counts, notably proximity to international borders and inherent spatiotemporal correlations in incidence. This model demonstrated a ∼50% reduction in the risk of outbreaks in states that were part of the vaccination program. In addition, after controlling for spatial autocorrelation in the data, states that had international borders experienced heightened risk of FMD outbreaks. These findings help inform risk-based control strategies for India as the country progresses towards reducing reported clinical disease., (© 2022 The Authors. Transboundary and Emerging Diseases published by Wiley-VCH GmbH.)

To evaluate the use of asymptomatic surveillance, we implemented a surveillance program for asymptomatic SARS-CoV-2 infection in a voluntary sample of individuals at the College of Veterinary Medicine at the University of Minnesota. Self-collected anterior nasal samples were tested using real time reverse transcription-polymerase chain reaction (RT-PCR), in a 5:1 pooled testing strategy, twice weekly for 18 weeks. Positive pools were deconvoluted into individual tests, revealing an observed prevalence of 0.07% (3/4,525). Pooled testing allowed for large scale testing with an estimated cost savings of 79.3% and modeling demonstrated this testing strategy prevented up to 2 workplace transmission events, averting up to 4 clinical cases. At the study endpoint, antibody testing revealed 80.7% of participants had detectable vaccine antibody levels while 9.6% of participants had detectable antibodies to natural infection., 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 Mladonicky, Bedada, Yoder, VanderWaal, Torrison and Wells.)

Modeling the windborne transmission of aerosolized pathogens is challenging. We adapted an atmospheric dispersion model (ADM) to simulate the windborne dispersion of porcine reproductive and respiratory syndrome virus (PRRSv) between swine farms. This work focuses on determining ADM applicable parameter values for PRRSv through a literature and expert opinion-based approach. The parameters included epidemiological features of PRRSv, characteristics of the aerosolized particles, and survival of aerosolized virus in relation to key meteorological features. A case study was undertaken to perform a sensitivity analysis on key parameters. Farms experiencing ongoing PRRSv outbreaks were assigned as particle emitting sources. The wind data from the North American Mesoscale Forecast System was used to simulate dispersion. The risk was estimated semi-quantitatively based on the median daily deposition of particles and the distance to the closest emitting farm. Among the parameters tested, the ADM was most sensitive to the number of particles emitted, followed by the model runtime, and the release height was the least sensitive. Farms within 25 km from an emitting farm were at the highest risk; with 53.66% being within 10 km. An ADM-based risk estimation of windborne transmission of PRRSv may inform optimum time intervals for air sampling, plan preventive measures, and aid in ruling out the windborne dispersion in outbreak investigations.

Understanding how the movement of individuals affects disease dynamics is critical to accurately predicting and responding to the spread of disease in an increasingly interconnected world. In particular, it is not yet known how movement between patches affects local disease dynamics (e.g., whether pathogen prevalence remains steady or oscillates through time). Considering a set of small, archetypal metapopulations, we find three surprisingly simple patterns emerge in local disease dynamics following the introduction of movement between patches: (1) movement between identical patches with cyclical pathogen prevalence dampens oscillations in the destination while increasing synchrony between patches; (2) when patches differ from one another in the absence of movement, adding movement allows dynamics to propagate between patches, alternatively stabilizing or destabilizing dynamics in the destination based on the dynamics at the origin; and (3) it is easier for movement to induce cyclical dynamics than to induce a steady-state. Considering these archetypal networks (and the patterns they exemplify) as building blocks of larger, more realistically complex metapopulations provides an avenue for novel insights into the role of host movement on disease dynamics. Moreover, this work demonstrates a framework for future predictive modelling of disease spread in real populations., (© 2022. The Author(s).)

Using georeferenced phylogenetic trees, phylogeography allows researchers to elucidate interactions between environmental heterogeneities and patterns of infectious disease spread. Concordant with the increasing availability of pathogen genetic sequence data, there is a growing need for tools to test epidemiological hypotheses in this field. In this study, we apply tools traditionally used in ecology to elucidate the epidemiology of foot-and-mouth disease virus (FMDV) in Uganda. We analyze FMDV serotype O genetic sequences and their corresponding spatiotemporal metadata from a cross-sectional study of cattle. We apply step selection function (SSF) models, typically used to study wildlife habitat selection, to viral phylogenies to show that FMDV is more likely to be found in areas of low rainfall. Next, we use a novel approach, a resource gradient function (RGF) model, to elucidate characteristics of viral source and sink areas. An RGF model applied to our data reveals that areas of high cattle density and areas near livestock markets may serve as sources of FMDV dissemination in Uganda, and areas of low rainfall serve as viral sinks that experience frequent reintroductions. Our results may help to inform risk-based FMDV control strategies in Uganda. More broadly, these tools advance the phylogenetic toolkit, as they may help to uncover patterns of spread of other organisms for which genetic sequences and corresponding spatiotemporal metadata exist.

African buffalo are the natural reservoirs of the SAT serotypes of foot-and-mouth disease virus (FMDV) in sub-Saharan Africa. Most buffalo are exposed to multiple FMDV serotypes early in life, and a proportion of them become persistently infected carriers. Understanding the genetic diversity and evolution of FMDV in carrier animals is critical to elucidate how FMDV persists in buffalo populations. In this study, we obtained oropharyngeal (OPF) fluid from naturally infected African buffalo, and characterized the genetic diversity of FMDV. Out of 54 FMDV-positive OPF, 5 were co-infected with SAT1 and SAT2 serotypes. From the five co-infected buffalo, we obtained eighty-nine plaque-purified isolates. Isolates obtained directly from OPF and plaque purification were sequenced using next-generation sequencing (NGS). Phylogenetic analyses of the sequences obtained from recombination-free protein-coding regions revealed a discrepancy in the topology of capsid proteins and non-structural proteins. Despite the high divergence in the capsid phylogeny between SAT1 and SAT2 serotypes, viruses from different serotypes that were collected from the same host had a high genetic similarity in non-structural protein-coding regions P2 and P3, suggesting interserotypic recombination. In two of the SAT1 and SAT2 co-infected buffalo identified at the first passage of viral isolation, the plaque-derived SAT2 genomes were distinctly grouped in two different genotypes. These genotypes were not initially detected with the NGS from the first passage (non-purified) virus isolation sample. In one animal with two SAT2 haplotypes, one plaque-derived chimeric sequence was found. These findings demonstrate within-host evolution through recombination and point mutation contributing to broad viral diversity in the wildlife reservoir. These mechanisms may be critical to FMDV persistence at the individual animal and population levels, and may contribute to the emergence of new viruses that have the ability to spill-over to livestock and other wildlife species.

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.)

Swine production in the United States is characterized by dynamic farm contacts through animal movements; such movements shape the risk of disease occurrence on farms. Pig movements have been linked to the spread of a virulent porcine reproductive and respiratory syndrome virus (PRRSV), RFLP type 1-7-4, herein denoted as phylogenetic sub-lineage 1A [L1A]. This study aimed to quantify the contribution of pig movements to the risk of L1A occurrence on farms in the United States. Farms were defined as L1A-positive in a given 6-month period if at least one L1A sequence was recovered from the farm. Temporal network autocorrelation modelling was performed using data on animal movements and 1,761 PRRSV ORF5 sequences linked to 494 farms from a dense pig production area in the United States between 2014 and 2017. A farm's current and past exposure to L1A and other PRRSV variants was assessed through its primary and secondary contacts in the animal movement network. Primary and secondary contacts with an L1A-positive farm increased the likelihood of L1A occurrence on a farm by 19% (p = .04) and 23% (p = .03), respectively. While the risk posed by primary contacts with PRRS-positive farms is unsurprising, the observation that secondary contacts also increase the likelihood of infection is novel. Risk of L1A occurrence on a farm also increased by 3.0% (p = .01) for every additional outgoing shipment, possibly due to biosecurity breaches during loading and transporting pigs from the farm. Finally, use of vaccines or field virus inoculation on sow farms one year prior reduced the risk of L1A occurrence in downstream farms by 36% (p = .04), suggesting that control measures that reduce viral circulation and enhance immunological protection in sow farms have a carry-over effect on L1A occurrence in downstream farms. Therefore, coordinated disease management interventions between farms connected via animal movements may be more effective than individual farm-based interventions., (© 2021 Wiley-VCH GmbH.)

Porcine reproductive and respiratory syndrome (PRRS) is an economically devastating disease of swine in many parts of the world. Porcine reproductive and respiratory syndrome virus (PRRSV) type 1 is endemic in Europe, and prevalence of the subtypes differ spatially. In this study, we investigated a severe PRRS outbreak reported in 30 farms located in eastern Russia that belong to a large swine production company in the region that was also experiencing a pseudorabies outbreak in the system. Data included 28 ORF5 sequences from samples across 18 of the 25 infected sites, reverse transcriptase real-time polymerase chain reaction (RT-qPCR) results from diagnostic testing, reports of clinical signs, and animal movement records. We observed that the outbreak was due to two distinct variants of wildtype PRRSV type 1 subtype 1 with an average genetic distance of 15%. Results suggest that the wildtype PRRSV variants were introduced into the region around 2019, before affecting this production system (i.e., sow farms, nurseries, and finisher farms). Clinical signs did not differ between the variants, but they did differ by stage of pig production. Biosecurity lapses, including movement of animals from infected farms contributed to disease spread.

Porcine reproductive and respiratory syndrome virus genotype 2 (PRRSV-2) genetic diversity in the U.S. was assessed using a database comprising 10 years' worth of sequence data obtained from swine production systems routine monitoring and outbreak investigations. A total of 26,831 ORF5 PRRSV-2 sequences from 34 production systems were included in this analysis. Within group mean genetic distance (i.e. mean proportion of nucleotide differences within ORF5) per year according to herd type was calculated for all PRRSV-2 sequences. The percent nucleotide difference between each sequence and the ORF5 sequences from four commercially available PRRSV-2 vaccines (Ingelvac PRRS MLV, Ingelvac PRRS ATP, Fostera PRRS, and Prevacent PRRS) within the same lineage over time was used to classify sequences in wild-type or vaccine-like. The mean ORF5 genetic distance fluctuated from 0.09 to 0.13, being generally smaller in years in which there was a relative higher frequency of dominant lineage. Vaccine-like sequences comprised about one fourth of sequences obtained through routine monitoring of PRRS. We found that lineage 5 sequences were mostly Ingelvac PRRS MLV-like. Lineage 8 sequences up to 2011 were 62.9% Ingelvac PRRS ATP-like while the remaining were wild-type viruses. From 2012 onwards, 51.9% of lineage 8 sequences were Ingelvac PRRS ATP-like, 45.0% were Fostera PRRS-like, and only 3.2% were wild-type. For lineage 1 sequences, 0.1% and 1.7% of the sequences were Prevacent PRRS-like in 2009-2018 and 2019, respectively. These results suggest that repeated introductions of vaccine-like viruses through use of modified live vaccines might decrease within-lineage viral diversity as vaccine-like strains become more prevalent. Overall, this compilation of private data from routine monitoring provides valuable information on PRRSV viral diversity., Competing Interests: The authors have declared that no competing interests exist. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

The genetic diversity of foot-and-mouth disease virus (FMDV) poses a challenge to the successful control of the disease, and it is important to identify the emergence of different strains in endemic settings. The objective of this study was to evaluate the sampling of clinically healthy livestock at slaughterhouses as a strategy for genomic FMDV surveillance. Serum samples ( n = 11,875) and oropharyngeal fluid (OPF) samples ( n = 5045) were collected from clinically healthy cattle and buffalo on farms in eight provinces in southern and northern Vietnam (2015-2019) to characterize viral diversity. Outbreak sequences were collected between 2009 and 2019. In two slaughterhouses in southern Vietnam, 1200 serum and OPF samples were collected from clinically healthy cattle and buffalo (2017 to 2019) as a pilot study on the use of slaughterhouses as sentinel points in surveillance. FMDV VP1 sequences were analyzed using discriminant principal component analysis and time-scaled phylodynamic trees. Six of seven serotype-O and -A clusters circulating in southern Vietnam between 2017-2019 were detected at least once in slaughterhouses, sometimes pre-dating outbreak sequences associated with the same cluster by 4-6 months. Routine sampling at slaughterhouses may provide a timely and efficient strategy for genomic surveillance to identify circulating and emerging FMDV strains.

Porcine epidemic diarrhea virus (PEDv) was introduced to the U.S. in 2013 and is now considered to be endemic. Like many endemic diseases, it is challenging for producers to estimate and respond to spatial and temporal variation in risk. Utilizing a regional spatio-temporal dataset containing weekly PEDv infection status for ∼15 % of the U.S. sow herd, we present a machine learning platform developed to forecast the probability of PEDv infection in sow farms in the U.S. Participating stakeholders (swine production companies) in a swine-dense region of the U.S. shared weekly information on a) PEDv status of farms and b) animal movements for the past week and scheduled movements for the upcoming week. Environmental (average temperature, humidity, among others) and land use characteristics (hog density, proportion of area with different land uses) in a 5 km radius around each farm were summarized. Using the Extreme Gradient Boosting (XGBoost) machine learning model with Synthetic Minority Over-sampling Technique (SMOTE), we developed a near real-time tool that generates weekly PEDv predictions (pertaining to two-weeks in advance) to farms of participating stakeholders. Based on retrospective data collected between 2014 and 2017, the sensitivity, specificity, positive and negative predictive values of our model were 19.9, 99.9, 70.5 and 99.4 %, respectively. Overall accuracy was 99.3 %, although this metric is heavily biased by imbalance in the data (less than 0.7 % of farms had an outbreak each week). This platform has been used to deliver weekly real-time forecasts since December 2019. The forecast platform has a built-in feature to re-train the predictive model in order to remain as relevant as possible to current epidemiological situations, or to expand to a different disease. These dynamic forecasts, which account for recent animal movements, present disease distribution, and environmental factors, will promote data-informed and targeted disease management and prevention within the U.S. swine industry., (Copyright © 2021 Elsevier B.V. All rights reserved.)

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.)

Pork processing plants were apparent hotspots for SARS-CoV2 in the spring of 2020. As a result, the swine industry was confronted with a major occupational health, financial, and animal welfare crisis. The objective of this work was to describe the epidemiological situation within processing plants, develop mathematical models to simulate transmission in these plants, and test the effectiveness of routine PCR screening at minimizing SARS-CoV2 circulation. Cumulative incidence of clinical (PCR-confirmed) disease plateaued at ~2.5% to 25% across the three plants studied here. For larger outbreaks, antibody prevalence was approximately 30% to 40%. Secondly, we developed a mathematical model that accounts for asymptomatic, pre-symptomatic, and background "community" transmission. By calibrating this model to observed epidemiological data, we estimated the initial reproduction number (R) of the virus. Across plants, R generally ranged between 2 and 4 during the initial phase, but subsequently declined to ~1 after two to three weeks, most likely as a result of implementation/compliance with biosecurity measures in combination with population immunity. Using the calibrated model to simulate a range of possible scenarios, we show that the effectiveness of routine PCR-screening at minimizing disease spread was far more influenced by testing frequency than by delays in results, R, or background community transmission rates. Testing every three days generally averted about 25% to 40% of clinical cases across a range of assumptions, while testing every 14 days typically averted 7 to 13% of clinical cases. However, the absolute number of additional clinical cases expected and averted was influenced by whether there was residual immunity from a previous peak (i.e., routine testing is implemented after the workforce had experienced an initial outbreak). In contrast, when using PCR-screening to prevent outbreaks or in the early stages of an outbreak, even frequent testing may not prevent a large outbreak within the workforce. This research helps to identify protocols that minimize risk to occupational safety and health and support continuity of business for U.S. processing plants. While the model was calibrated to meat processing plants, the structure of the model and insights about testing are generalizable to other settings where large number of people work in close proximity., Competing Interests: SD and JH are affiliated with commercial companies. We also disclose that JH is affiliated with one of the slaughtering facilities included in this manuscript. These commercial affiliations did not play a role in the study design, data analysis, or preparation of the manuscript and only provided financial support in the form of authors’ salaries. This does not alter our adherence to PLOS ONE policies on sharing data and materials, and data availability is outlined in the Data Availability Statement. Data utilized in this manuscript are confidential and were collected by commercial companies. These companies provided data and approved the manuscript for publication, but did not play a role in study design, data analysis, or preparation of the manuscript.

Porcine reproductive and respiratory syndrome virus (PRRSV) continues to mutate, causing disruptive PRRS outbreaks in farms that lead to reproductive failure and respiratory disease-associated mortality. We present four new PRRSV type 2 variants in the United States belonging to four distinct orf5 sublineages within lineage 1.

The continued endemicity of foot and mouth disease virus (FMDV) in East Africa has significant implications for livestock production and poverty reduction, yet its complex epidemiology in endemic settings remains poorly understood. Identifying FMDV dispersal routes and drivers of transmission is key to improved control strategies. Environmental heterogeneity and anthropogenic drivers (e.g., demand for animal products) can impact viral spread by influencing host movements. Here, we utilized FMDV serotype O VP1 genetic sequences and corresponding spatiotemporal data in order to (i) infer the recent dispersal history, and (II) investigate the impact of external factors (cattle density, human population density, proximity to livestock markets, and drought) on dispersal velocity, location, and direction of FMDV serotype O in East Africa. We identified statistical evidence of long-distance transmission events, and we found that FMDV serotype O tends to remain circulating in areas of high cattle density, high human population density, and in close proximity to livestock markets. The latter two findings highlight the influence of anthropogenic factors on FMDV serotype O spread in this region. These findings contribute to the understanding of FMDV epidemiology in East Africa and can help guide improved control measures., (© 2021 John Wiley & Sons Ltd.)

Viral sequence data coupled with phylodynamic models have become instrumental in investigating the outbreaks of human and animal diseases, and the incorporation of the hypothesized drivers of pathogen spread can enhance the interpretation from phylodynamic inference. Integrating animal movement data with phylodynamics allows us to quantify the extent to which the spatial diffusion of a pathogen is influenced by animal movements and contrast the relative importance of different types of movements in shaping pathogen distribution. We combine animal movement, spatial, and environmental data in a Bayesian phylodynamic framework to explain the spatial diffusion and evolutionary trends of a rapidly spreading sub-lineage (denoted L1A) of porcine reproductive and respiratory syndrome virus (PRRSV) Type 2 from 2014 to 2017. PRRSV is the most important endemic pathogen affecting pigs in the USA, and this particular virulent sub-lineage emerged in 2014 and continues to be the dominant lineage in the US swine industry to date. Data included 984 open reading frame 5 (ORF5) PRRSV L1A sequences obtained from two production systems in a swine-dense production region (∼85,000 mi 2 ) in the USA between 2014 and 2017. The study area was divided into sectors for which model covariates were summarized, and animal movement data between each sector were summarized by age class (wean: 3-4 weeks; feeder: 8-25 weeks; breeding: ≥21 weeks). We implemented a discrete-space phylogeographic generalized linear model using Bayesian evolutionary analysis by sampling trees (BEAST) to infer factors associated with variability in between-sector diffusion rates of PRRSV L1A. We found that between-sector spread was enhanced by the movement of feeder pigs, spatial adjacency of sectors, and farm density in the destination sector. The PRRSV L1A strain was introduced in the study area in early 2013, and genetic diversity and effective population size peaked in 2015 before fluctuating seasonally (peaking during the summer months). Our study underscores the importance of animal movements and shows, for the first time, that the movement of feeder pigs (8-25 weeks old) shaped the spatial patterns of PRRSV spread much more strongly than the movements of other age classes of pigs. The inclusion of movement data into phylodynamic models as done in this analysis may enhance our ability to identify crucial pathways of disease spread that can be targeted to mitigate the spatial spread of infectious human and animal pathogens., (© The Author(s) 2021. Published by Oxford University Press.)