Epidemiological connectivity between humans and animals across an urban landscape.

Urbanization is predicted to be a key driver of disease emergence through human exposure to novel, animal-borne pathogens. However, while we suspect that urban landscapes are primed to expose people to novel animal-borne diseases, evidence for the mechanisms by which this occurs is lacking. To address this, we studied how bacterial genes are shared between wild animals, livestock, and humans (n = 1,428) across Nairobi, Kenya-one of the world's most rapidly developing cities. Applying a multilayer network framework, we show that low biodiversity (of both natural habitat and vertebrate wildlife communities), coupled with livestock management practices and more densely populated urban environments, promotes sharing of Escherichia coli-borne bacterial mobile genetic elements between animals and humans. These results provide empirical support for hypotheses linking resource provision, the biological simplification of urban landscapes, and human and livestock demography to urban dynamics of cross-species pathogen transmission at a landscape scale. Urban areas where high densities of people and livestock live in close association with synanthropes (species such as rodents that are more competent reservoirs for zoonotic pathogens) should be prioritized for disease surveillance and control.

The spectral underpinnings of pathogen spread on animal networks.

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.

Factors influencing usage of antimicrobial drugs among pastoralists in Kenya.

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.

Phylogeographic analysis of foot-and-mouth disease virus serotype O dispersal and associated drivers in East Africa.

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.

Global trends in infectious diseases of swine.

Pork accounts for more than one-third of meat produced worldwide and is an important component of global food security, agricultural economies, and trade. Infectious diseases are among the primary constraints to swine production, and the globalization of the swine industry has contributed to the emergence and spread of pathogens. Despite the importance of infectious diseases to animal health and the stability and productivity of the global swine industry, pathogens of swine have never been reviewed at a global scale. Here, we build a holistic global picture of research on swine pathogens to enhance preparedness and understand patterns of emergence and spread. By conducting a scoping review of more than 57,000 publications across 50 years, we identify priority pathogens globally and regionally, and characterize geographic and temporal trends in research priorities. Of the 40 identified pathogens, publication rates for eight pathogens increased faster than overall trends, suggesting that these pathogens may be emerging or constitute an increasing threat. We also compared regional patterns of pathogen prioritization in the context of policy differences, history of outbreaks, and differing swine health challenges faced in regions where swine production has become more industrialized. We documented a general increasing trend in importance of zoonotic pathogens and show that structural changes in the industry related to intensive swine production shift pathogen prioritization. Multinational collaboration networks were strongly shaped by region, colonial ties, and pig trade networks. This review represents the most comprehensive overview of research on swine infectious diseases to date.

Merging Metagenomics and Spatial Epidemiology To Understand the Distribution of Antimicrobial Resistance Genes from Enterobacteriaceae in Wild Owls.

Antimicrobial resistance (AMR) is a well-documented phenomenon in bacteria from many natural ecosystems, including wild animals. However, the specific determinants and spatial distribution of resistant bacteria and antimicrobial resistance genes (ARGs) in the environment remain incompletely understood. In particular, information regarding the importance of anthropogenic sources of AMR relative to that of other biological and ecological influences is lacking. We conducted a cross-sectional study of AMR in great horned owls (Bubo virginianus) and barred owls (Strix varia) admitted to a rehabilitation center in the midwestern United States. A combination of selective culture enrichment and shotgun metagenomic sequencing was used to identify ARGs from Enterobacteriaceae Overall, the prevalence of AMR was comparable to that in past studies of resistant Enterobacteriaceae in raptors, with acquired ARGs being identified in 23% of samples. Multimodel regression analyses identified seasonality and owl age to be important predictors of the likelihood of the presence of ARGs, with birds sampled during warmer months being more likely to harbor ARGs than those sampled during cooler months and with birds in their hatch year being more likely to harbor ß-lactam ARGs than adults. Beyond host-specific determinants, ARG-positive owls were also more likely to be recovered from areas of high agricultural land cover. Spatial clustering analyses identified a significant high-risk cluster of tetracycline resistance gene-positive owls in the southern sampling range, but this could not be explained by any predictor variables. Taken together, these results highlight the complex distribution of AMR in natural environments and suggest that both biological and anthropogenic factors play important roles in determining the emergence and persistence of AMR in wildlife.IMPORTANCE Antimicrobial resistance (AMR) is a multifaceted problem that poses a worldwide threat to human and animal health. Recent reports suggest that wildlife may play an important role in the emergence, dissemination, and persistence of AMR. As such, there have been calls for better integration of wildlife into current research on AMR, including the use of wild animals as biosentinels of AMR contamination in the environment. A One Health approach can be used to gain a better understanding of all AMR sources and pathways, particularly those at the human-animal-environment interface. Our study focuses on this interface in order to assess the effect of human-impacted landscapes on AMR in a wild animal. This work highlights the value of wildlife rehabilitation centers for environmental AMR surveillance and demonstrates how metagenomic sequencing within a spatial epidemiology framework can be used to address questions surrounding AMR complexity in natural ecosystems.

Molecular detection and phylogenetic analysis of lumpy skin disease virus from outbreaks in Uganda 2017-2018.

BACKGROUND: Lumpy skin disease (LSD) is an infectious viral disease of cattle caused by a Capripoxvirus. LSD has substantial economic implications, with infection resulting in permanent damage to the skin of affected animals which lowers their commercial value. In Uganda, LSD is endemic and cases of the disease are frequently reported to government authorities. This study was undertaken to molecularly characterize lumpy skin disease virus (LSDV) strains that have been circulating in Uganda between 2017 and 2018. Secondly, the study aimed to determine the phylogenetic relatedness of Ugandan LSDV sequences with published sequences, available in GenBank. RESULTS: A total of 7 blood samples and 16 skin nodule biopsies were screened for LSDV using PCR to confirm presence of LSDV nucleic acids. PCR positive samples were then characterised by amplifying the GPCR gene. These amplified genes were sequenced and phylogenetic trees were constructed. Out of the 23 samples analysed, 15 were positive for LSDV by PCR (65.2%). The LSDV GPCR sequences analysed contained the unique signatures of LSDV (A11, T12, T34, S99, and P199) which further confirmed their identity. Sequence comparison with vaccine strains revealed a 12 bp deletion unique to Ugandan outbreak strains. Phylogenetic analysis indicated that the LSDV sequences from this study clustered closely with sequences from neighboring East African countries and with LSDV strains from recent outbreaks in Europe. It was noted that the sequence diversity amongst LSDV strains from Africa was higher than diversity from Eurasia. CONCLUSION: The LSDV strains circulating in Uganda were closely related with sequences from neighboring African countries and from Eurasia. Comparison of the GPCR gene showed that outbreak strains differed from vaccine strains. This information is necessary to understand LSDV molecular epidemiology and to contribute knowledge towards the development of control strategies by the Government of Uganda.

Assessing Transmission of Antimicrobial-Resistant Escherichia coli in Wild Giraffe Contact Networks.

There is growing evidence that anthropogenic sources of antibiotics and antimicrobial-resistant bacteria can spill over into natural ecosystems, raising questions about the role wild animals play in the emergence, maintenance, and dispersal of antibiotic resistance genes. In particular, we lack an understanding of how resistance genes circulate within wild animal populations, including whether specific host characteristics, such as social associations, promote interhost transmission of these genes. In this study, we used social network analysis to explore the forces shaping population-level patterns of resistant Escherichia coli in wild giraffe (Giraffa camelopardalis) and assess the relative importance of social contact for the dissemination of resistant E. coli between giraffe. Of 195 giraffe sampled, only 5.1% harbored E. coli isolates resistant to one or more tested antibiotics. Whole-genome sequencing on a subset of resistant isolates revealed a number of acquired resistance genes with linkages to mobile genetic elements. However, we found no evidence that the spread of resistance genes among giraffe was facilitated by interhost associations. Giraffe with lower social degree were more likely to harbor resistant E. coli, but this relationship was likely driven by a correlation between an individual's social connectedness and age. Indeed, resistant E. coli was most frequently detected in socially isolated neonates, indicating that resistant E. coli may have a selective advantage in the gastrointestinal tracts of neonates compared to other age classes. Taken together, these results suggest that the maintenance of antimicrobial-resistant bacteria in wild populations may, in part, be determined by host traits and microbial competition dynamics within the host.IMPORTANCE Antimicrobial resistance represents a significant threat to human health, food security, and the global economy. To fully understand the evolution and dissemination of resistance genes, a complete picture of antimicrobial resistance in all biological compartments, including natural ecosystems, is required. The environment and wild animals may act as reservoirs for anthropogenically derived resistance genes that could be transferrable to clinically relevant bacteria of humans and domestic animals. Our study investigated the possible transmission mechanisms for antimicrobial-resistant bacteria within a wild animal population and, more broadly, contributes to our understanding of how resistance genes are spread and maintained in natural ecosystems.

Seroprevalence and risk factors for lumpy skin disease virus seropositivity in cattle in Uganda.

BACKGROUND: Lumpy skin disease (LSD) is a transboundary cattle disease caused by a Capripoxvirus of the family Poxviridae. In Uganda, documented information on the epidemiology of the disease is rare and there is no nationwide control plan, yet LSD is endemic. This study set out to investigate the seroprevalence of lumpy skin disease and determine the risk factors for LSD seropositivity, by carrying out a cross-sectional study in 21 districts of Uganda. RESULTS: A total of 2,263 sera samples were collected from 65 cattle herds and an indirect ELISA was used to screen for lumpy skin disease virus (LSDV) antibodies. We used univariable and multivariable mixed effect logistic regression models to identify risk factors for LSD seropositivity. The overall animal and herd-level seroprevalences were 8.7% (95% CI: 7.0-9.3) and 72.3% (95% CI: 70.0-80.3), respectively. Animal-level seroprevalence in Central region (OR = 2.13, p = 0.05, 95% CI: 1.10-4.64) was significantly different from the Northern region (Reference) and Western region (OR = 0.84, p = 0.66, 95% CI: 0.39-1.81). Management type, sex, age, mean annual precipitation > 1000 mm, and drinking from communal water sources were statistically significant risk factors for occurrence of anti-LSDV antibodies in cattle. Breed, region, herd size, contact with buffalo and other wildlife and introduction of new cattle did not have a statistically significant association with being positive for LSDV. CONCLUSION: We report a high herd-level LSDV seroprevalence in Uganda with a moderate animal-level seroprevalence. Cattle with the highest risk of LSD infection in Uganda are those in fenced farms, females > 25 months old, in an area with a mean annual rainfall > 1000 mm, and drinking from a communal water source.

Spatial and temporal distribution of lumpy skin disease outbreaks in Uganda (2002-2016).

BACKGROUND: Lumpy skin disease (LSD) is a devastating transboundary viral disease of cattle which causes significant loss in production. Although this disease has been reported in Uganda and throughout East Africa, there is almost no information about its epidemiology, spatial or spatio-temporal distribution. We carried out a retrospective study on the epidemiology of LSD in Uganda between the years 2002 and 2016, using data on reported outbreaks collected monthly by the central government veterinary administration. Descriptive statistics were computed on frequency of outbreaks, number of cases, vaccinations and deaths. We evaluated differences in the number of reported outbreaks across different regions (agro-ecological zones), districts, months and years. Spatial, temporal and space-time scan statistics were used to identify possible epidemiological clusters of LSD outbreaks. RESULTS: A total of 1161 outbreaks and 319,355 cases of LSD were reported from 55 out of 56 districts of Uganda. There was a significant difference in incidence between years (P = 0.007) and across different regions. However, there was no significant difference in the number of outbreaks per month (P = 0.443). The Central region reported the highest number of outbreaks (n = 418, 36%) followed by Eastern (n = 372, 32%), Southwestern (n = 140, 12%), Northern (n = 131, 11%), Northeastern (n = 37, 3%), Western (n = 41, 4%) and Northwestern (n = 22, 2%) regions. Several endemic hotspots for the circulation of LSD were identified in the Central and Eastern regions using spatial cluster analyses. Outbreaks in endemic hotspots were less seasonal and had strikingly lower mortality and case-fatality rates than the other regions, suggesting an underlying difference in the epidemiology and impact of LSD in these different zones. CONCLUSION: Lumpy Skin disease is endemic in Uganda, with outbreaks occurring annually in all regions of the country. We identified potential spatial hotspots for LSD outbreaks, underlining the need for risk-based surveillance to establish the actual disease prevalence and risk factors for disease maintenance. Space-time analysis revealed that sporadic LSD outbreaks tend to occur both within and outside of endemic areas. The findings from this study will be used as a baseline for further epidemiological studies for the development of sustainable programmes towards the control of LSD in Uganda.

Linking social and spatial networks to viral community phylogenetics reveals subtype-specific transmission dynamics in African lions.

Heterogeneity within pathogen species can have important consequences for how pathogens transmit across landscapes; however, discerning different transmission routes is challenging. Here, we apply both phylodynamic and phylogenetic community ecology techniques to examine the consequences of pathogen heterogeneity on transmission by assessing subtype-specific transmission pathways in a social carnivore. We use comprehensive social and spatial network data to examine transmission pathways for three subtypes of feline immunodeficiency virus (FIVPle ) in African lions (Panthera leo) at multiple scales in the Serengeti National Park, Tanzania. We used FIVPle molecular data to examine the role of social organization and lion density in shaping transmission pathways and tested to what extent vertical (i.e., father- and/or mother-offspring relationships) or horizontal (between unrelated individuals) transmission underpinned these patterns for each subtype. Using the same data, we constructed subtype-specific FIVPle co-occurrence networks and assessed what combination of social networks, spatial networks or co-infection best structured the FIVPle network. While social organization (i.e., pride) was an important component of FIVPle transmission pathways at all scales, we find that FIVPle subtypes exhibited different transmission pathways at within- and between-pride scales. A combination of social and spatial networks, coupled with consideration of subtype co-infection, was likely to be important for FIVPle transmission for the two major subtypes, but the relative contribution of each factor was strongly subtype-specific. Our study provides evidence that pathogen heterogeneity is important in understanding pathogen transmission, which could have consequences for how endemic pathogens are managed. Furthermore, we demonstrate that community phylogenetic ecology coupled with phylodynamic techniques can reveal insights into the differential evolutionary pressures acting on virus subtypes, which can manifest into landscape-level effects.

Landscape influence on spatial patterns of meningeal worm and liver fluke infection in white-tailed deer.

Parasites that primarily infect white-tailed deer (Odocoileus virginianus), such as liver flukes (Fascioloides magna) and meningeal worm (Parelaphostrongylus tenuis), can cause morbidity and mortality when incidentally infecting moose (Alces alces). Ecological factors are expected to influence spatial variation in infection risk by affecting the survival of free-living life stages outside the host and the abundance of intermediate gastropod hosts. Here, we investigate how ecology influenced the fine-scale distribution of these parasites in deer in Voyageurs National Park, Minnesota. Deer pellet groups (N = 295) were sampled for the presence of P. tenuis larvae and F. magna eggs. We found that deer were significantly more likely to be infected with P. tenuis in habitats with less upland deciduous forest and more upland mixed conifer forest and shrub, a pattern that mirrored microhabitat differences in gastropod abundances. Deer were also more likely to be infected with F. magna in areas with more marshland, specifically rooted-floating aquatic marshes (RFAMs). The environment played a larger role than deer density in determining spatial patterns of infection for both parasites, highlighting the importance of considering ecological factors on all stages of a parasite's life cycle in order to understand its occurrence within the definitive host.

Linking social and pathogen transmission networks using microbial genetics in giraffe (Giraffa camelopardalis).

Although network analysis has drawn considerable attention as a promising tool for disease ecology, empirical research has been hindered by limitations in detecting the occurrence of pathogen transmission (who transmitted to whom) within social networks. Using a novel approach, we utilize the genetics of a diverse microbe, Escherichia coli, to infer where direct or indirect transmission has occurred and use these data to construct transmission networks for a wild giraffe population (Giraffe camelopardalis). Individuals were considered to be a part of the same transmission chain and were interlinked in the transmission network if they shared genetic subtypes of E. coli. By using microbial genetics to quantify who transmits to whom independently from the behavioural data on who is in contact with whom, we were able to directly investigate how the structure of contact networks influences the structure of the transmission network. To distinguish between the effects of social and environmental contact on transmission dynamics, the transmission network was compared with two separate contact networks defined from the behavioural data: a social network based on association patterns, and a spatial network based on patterns of home-range overlap among individuals. We found that links in the transmission network were more likely to occur between individuals that were strongly linked in the social network. Furthermore, individuals that had more numerous connections or that occupied 'bottleneck' positions in the social network tended to occupy similar positions in the transmission network. No similar correlations were observed between the spatial and transmission networks. This indicates that an individual's social network position is predictive of transmission network position, which has implications for identifying individuals that function as super-spreaders or transmission bottlenecks in the population. These results emphasize the importance of association patterns in understanding transmission dynamics, even for environmentally transmitted microbes like E. coli. This study is the first to use microbial genetics to construct and analyse transmission networks in a wildlife population and highlights the potential utility of an approach integrating microbial genetics with network analysis.

Predicting Antigenic Distance from Genetic Data for PRRSV-Type 1: Applications of Machine Learning.

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.

Unveiling invisible farm-to-farm PRRSV-2 transmission links and routes through transmission tree and network analysis.

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.

Phylogeography as a Proxy for Population Connectivity for Spatial Modeling of Foot-and-Mouth Disease Outbreaks in Vietnam.

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.

Mapping the Dynamics of Contemporary PRRSV-2 Evolution and Its Emergence and Spreading Hotspots in the U.S. Using Phylogeography.

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.

Refining PRRSV-2 genetic classification based on global ORF5 sequences and investigation of their geographic distributions and temporal changes.

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.

Seasonal changes in network connectivity and consequences for pathogen transmission in a solitary carnivore.

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.

Porcine Reproductive and Respiratory Syndrome (PRRSV2) Viral Diversity within a Farrow-to-Wean Farm Cohort Study.

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.