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The genetic diversity and frequent emergence of novel genetic variants of porcine reproductive and respiratory syndrome virus type-2 (PRRSV) hinders control efforts, yet drivers of macro-evolutionary patterns of PRRSV remain poorly documented. Utilizing a comprehensive database of >20,000 orf5 sequences, our objective was to classify variants according to the phylogenetic structure of PRRSV co-circulating in the U.S., quantify evolutionary dynamics of sub-lineage emergence, and describe potential antigenic differences among sub-lineages. We subdivided the most prevalent lineage (Lineage 1, accounting for approximately 60% of available sequences) into eight sub-lineages. Bayesian coalescent SkyGrid models were used to estimate each sub-lineage's effective population size over time. We show that a new sub-lineage emerged every 1 to 4 years and that the time between emergence and peak population size was 4.5 years on average (range: 2-8 years). A pattern of sequential dominance of different sub-lineages was identified, with a new dominant sub-lineage replacing its predecessor approximately every 3 years. Consensus amino acid sequences for each sub-lineage differed in key GP5 sites related to host immunity, suggesting that sub-lineage turnover may be linked to immune-mediated competition. This has important implications for understanding drivers of genetic diversity and emergence of new PRRSV variants in the U.S.

As a consequence of multi-site pig production practiced in North America, frequent and widespread animal movements create extensive networks of interaction between farms. Social network analysis (SNA) has been used to understand disease transmission risks within these complex and dynamic production ecosystems and is particularly relevant for designing risk-based surveillance and control strategies targeting highly connected farms. However, inferences from SNA and the effectiveness of targeted strategies may be influenced by temporal changes in network structure. Since farm movements represent a temporally dynamic network, it is also unclear how many months of data are required to gain an accurate picture of an individual farm's connectivity pattern and the overall network structure. The extent to which shipments between two specific farms are repeated (i.e., "loyalty" of farm contacts) can influence the rate at which the structure of a network changes over time, which may influence disease dynamics. In this study, we aimed to describe temporal stability and loyalty patterns of pig movement networks in the U.S. swine industry. We analyzed a total of 282,807 animal movements among 2724 farms belonging to two production systems between 2014 and 2017. Loyalty trends were largely driven by contacts between sow farms and nurseries and between nurseries and finisher farms; mean loyalty (percent of contacts that were repeated at least once within a 52-week interval) of farm contacts was 51-60 % for farm contacts involving weaned pigs, and 12-22% for contacts involving feeder pigs. A cyclic pattern was observed for both weaned and feeder pig movements, with episodes of increased loyalty observed at intervals of 8 and 17-20 weeks, respectively. Network stability was achieved when six months of data were aggregated, and only small shifts in node-level and global network metrics were observed when adding more data. This stability is relevant for designing targeted surveillance programs for disease management, given that movements summarized over too short a period may lead to stochastic swings in network metrics. A temporal resolution of six months would be reliable for the identification of potential super-spreaders in a network for targeted intervention and disease control. The temporal stability observed in these networks suggests that identifying highly connected farms in retrospective network data (up to 24 months) is reliable for future planning, albeit with reduced effectiveness., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Bovine tuberculosis (bTB) prevalence substantially increased over the past two decades with relatively high impact on large dairy herds, raising the concern of regulatory authorities and industry stakeholders, and threatening animal and public health. Lack of resources, together with the economic and social consequences of whole-herd stamping-out, makes depopulation an impractical disease control alternative in these herds. The increase in bTB prevalence was associated with demographic and management changes in the dairy industry in Uruguay, reducing the efficacy of the current control programme (i.e. status quo) based on intradermal serial testing with caudal fold- and comparative-cervical tuberculin test-and-slaughter of reactors (CFT-CCT). Here, we aimed to assess the epidemiological effectiveness of six alternative control scenarios based on test-and-slaughter of positive animals, using mathematical modelling to infer bTB-within-herd dynamics. We simulated six alternative control strategies consisting of testing adult cattle (>1 year) in the herd every 3 months using one test (in vivo or in vitro) or a combination in parallel of two tests (CFT, interferon-gamma release assay-IGRA- or enzyme-linked immunosorbent assay). Results showed no significant differences overall in the time needed to reach bTB eradication (median ranging between 61 and 82 months) or official bovine tuberculosis-free status (two consecutive negative herd tests) between any of the alternative strategies and the status quo (median ranging between 50 and 59 months). However, we demonstrate how alternative strategies can significantly reduce bTB prevalence when applied for restricted periods (6, 12 or 24 months), and in the case of IGRAc (IGRA using peptide-cocktail antigens), without incurring on higher unnecessary slaughter of animals (false positives) than the status quo in the first 6 months of the programme (p-value < .05). Enhanced understanding bTB-within-herd dynamics with the application of different control strategies help to identify optimal strategies to ultimately improve bTB control and bTB eradication from dairies in Uruguay and similar endemic settings., (© 2020 Wiley-VCH GmbH.)

Increased risk of pathogen transmission through proximity and contact is a well-documented cost of sociality. Affiliative social contact, however, is an integral part of primate group life and can benefit health. Despite its importance to the evolution and maintenance of sociality, the tradeoff between costs and benefits of social contact for group-living primate species remains poorly understood. To improve our understanding of this interplay, we used social network analysis to investigate whether contact via association in the same space and/or physical contact measured through grooming were associated with helminth parasite species richness in a community of wild chimpanzees ( Pan troglodytes schweinfurthii ). We identified parasite taxa in 381 fecal samples from 36 individuals from the Kasekela community of chimpanzees in Gombe National Park, Tanzania, from November 1, 2006 - October 31, 2012. Over the study period, eight environmentally transmitted helminth taxa were identified. We quantified three network metrics for association and grooming contact, including degree strength, betweenness, and closeness. Our findings suggest that more gregarious individuals - those who spent more time with more individuals in the same space - had higher parasite richness, while the connections in the grooming network were not related to parasite richness. The expected parasite richness in individuals increased by 1.13 taxa (CI: 1.04, 1.22; p = 0.02) per one standard deviation increase in degree strength of association contact. The results of this study add to the understanding of the role that different types of social contact plays in the parasite richness of group-living social primates., Competing Interests: Conflict of interest The authors declare that they have no conflicts of interests.

Network analysis is a powerful tool to describe, estimate, and predict the role of pig trade in the spread of pathogens and generate essential patterns that can be used to understand, prevent, and mitigate possible outbreaks. This study aimed to describe the network between premises such as production herds, slaughterhouses, and traders of pig movements and identify heterogeneities in the connectivity of premises in the state of Santa Catarina, Brazil, using social network analysis (SNA). We used static and temporal network approaches to describe pig trade in the state by quantifying network attributes using SNA parameters, such as causal fidelity, loyalty, the proportion of node-loyalty, resilience of outgoing contact chains, and communities. Two indexes were implemented, the first one is a normalized index based on SNA-farm level measures and other index-based SNA-farm level measures considering the swineherd population size from all premises, both indexes were summarized by a municipality to target and rank surveillance activities. Within Santa Catarina, the southwest region played a key role in that 80 % of trade was concentrated in this region, and thus acted as a hub in the network. Besides, nine communities were found. The results also showed that premises were highly connected in the static network, with the network exhibiting low levels of fragmentation and loyalty. Also, just 11 % of the paths in the static network existed in the temporal network which accounted for the order in which edges occurred. Therefore, the use of time-respecting-paths was essential to not overestimate potential transmission pathways and outbreak sizes. Compared to static networks, the application of temporal network approaches was more suitable to capture the dynamics of pig trade and should be used to inform the design of riskbased disease surveillance., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Livestock movements are important drivers for infectious disease transmission. However, paucity of such data in pastoralist communities in rangeland ecosystems limits our understanding of their dynamics and hampers disease surveillance and control. The aim of this study was to investigate animal movement networks in a pastoralist community in Kenya, and assess network-based strategies for disease control. We used network analysis to characterize five types of between-village animal movement networks. We then evaluated implications of these networks for disease spread and control by quantifying topological changes in the network associated with targeted and random removal of nodes. To construct these networks, data were collected using standardized questionnaires (N = 165 households) from communities living within the Maasai Mara Ecosystem in southwestern Kenya. Our analyses show that the Maasai Mara National Reserve (MMNR), a protected wildlife area, was critical for maintaining village connectivity in the agistment network (dry season grazing), with MMNR-adjacent villages being highly utilized during the dry season. In terms of disease dynamics, the network-based basic reproduction number, R 0 , was sufficient to allow disease invasion in all the five networks, and removal of villages based on degree or betweenness was not efficient in reducing R 0 . However, we show that villages with high degree or betweenness may play an important role in maintaining network connectivity, which may not be captured by assessment of R 0 alone. Such villages may function as potential "firebreaks." For example, targeted removal of highly connected village nodes was more effective at fragmenting each network than random removal of nodes, indicating that network-based targeting of interventions such as vaccination could potentially disrupt transmission pathways in the ecosystem. In conclusion, this work shows that animal movements have the potential to shape patterns of disease transmission in this ecosystem, with targeted interventions being a practical and efficient measure for disease control., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Mycoplasma hyopneumoniae (M. hyopneumoniae) infections continue to result in significant respiratory challenges in the swine industry worldwide. Vaccination for M. hyopneumoniae is commonly utilized, as reduction in bacterial loads and clinical severity in vaccinated pigs have been shown. However, the effect of M. hyopneumoniae vaccination on transmission across different pig populations has been minimally investigated. The aim of this pilot study was to evaluate the effect of multiple vaccinations on M. hyopneumoniae infection, transmission, and genetic variability in infected and susceptible gilt populations. Thirty-two naïve gilts were allocated to four treatment groups: (1) Vaccinated seeder (VS); (2) Non-vaccinated seeder (NVS); (3) Vaccinated contact (VC); and (4) Non-vaccinated contact (NVC). At 5, 7, and 9 weeks of age, all gilts selected to be vaccinated received a commercial M. hyopneumoniae bacterin for a total of 3 doses. At 11 weeks of age, VS and NVS gilts were inoculated with M. hyopneumoniae to become seeders. At 28 days post-inoculation (dpi), VS and NVS gilts were individually relocated to clean experimental rooms, where they were placed in contact with one age-matched VC or NVC gilt (1:1 ratio) for 14 days. Blood and tracheal samples, bronchial swabs, and lung lesions were collected and/or evaluated for M. hyopneumoniae infection. In this study, a three-dose vaccination strategy against M. hyopneumoniae significantly reduced bacterial load in seeder gilts. Furthermore, a numerical reduction in M. hyopneumoniae lung lesions at 28 dpi was observed in VS gilts. All VC gilts in the VS:VC treatment group pairing remained M. hyopneumoniae negative, compared to other groups in which 1-2 transmission events occurred per treatment group. Results from this investigation provide insight on the potential impact of multiple vaccinations on reducing M. hyopneumoniae transmission and infection. Further research encompassing vaccinations of gilt groups in field settings is necessary to validate findings., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: [Dr. Eduardo Fano is an employee of Boehringer Ingelheim, the company that provided funding and the commercial vaccine product for this study.]., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

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

Chronic wasting disease is a transmissible spongiform encephalopathy that affects cervids with a clinical picture of muscle wasting in infected animals. The objective of this study was to quantify movement patterns of farmed cervids in the state of Minnesota as a model for identifying potential disease mitigation points. Time aggregated network analysis was performed on data consisting of 1221 intra-state cervid movements from farms located within Minnesota (n = 432 farms). Intra-state movements accounted for 48.2 % of all documented movements (2578) in Minnesota from 2013 to 2018; the remaining movements were inter-state. Annual networks were sparse in nature with low graph densities (6.9 × 10 -4 - 1.4 × 10 -3 ) and transitivity (0.06-0.12). Frequency of movements increased significantly (p < 0.05) in the months of September and October before decreasing in November, which coincided with the breeding and hunting seasons. Some of these contacts were as far as 500 km apart. The median length of infection chains for CWD positive farms was estimated to be 5.0 and 6.0 farms in-and out-going infection chains, respectively. A k-test analysis demonstrated that the observed median number of infected farms directly connected to other infected farms was 2.0, which was significantly higher than a fortuitous event (p = 0.002). Movements of cervids between farms were largely unpredictable with very low edge overlap (mean 0.02 %) from year to year, suggesting that persistent commercial relationships among farms were rare. In conclusion, long distance trade movements present a risk for spread of chronic wasting disease in Minnesota. The sparse networks and unpredictable farm contacts could be because cervid production is not as vertically integrated as other species-differentiated and established industries, such as swine or poultry. Our analytical approach can be used to understand chronic wasting disease in other states in the U.S. and North America in general., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Analyses of livestock movement networks has become key to understanding an industry's vulnerability to infectious disease spread and for identifying farms that play disproportionate roles in pathogen dissemination. In addition to animal movements, many pathogens can spread between farms via mechanisms mediated by spatial proximity. Heterogeneities in contact patterns based on spatial proximity are less commonly considered in network studies, and studies that jointly consider spatial connectivity and animal movement are rare. The objective of this study was to determine the extent to which movement versus spatial proximity networks determine the distribution of an economically important endemic virus, porcine reproductive and respiratory syndrome virus (PRRSV), within a swine-dense region of the U.S. PRRSV can be classified into numerous phylogenetic lineages. Such data can be used to better resolve between-farm infection chains and elucidate types of contact most associated with transmission. Here, we construct movement and spatial proximity networks; farms within the networks were classified as cases if a given PRRSV lineage had been recovered at least once in a year for each of three years analyzed. We evaluated six lineages and sub-lineages across three years, and evaluated the epidemiological relevance of each network by applying network k-tests to statistically evaluate whether the pattern of case occurrence within the network was consistent with transmission via network linkages. Our results indicated that animal movements, not local area spread, play a dominant role in shaping transmission pathways, though there were differences amongst lineages. The median number of case farms inter-linked via animal movements was approximately 4.1x higher than random expectations (range: 1.7-13.7; p < 0.05, network k-test), whereas this measure was only 2.7x higher than random expectations for farms linked via spatial proximity (range: 1.3-5.4; p < 0.05, network k-test). For spatial proximity networks, contact based on proximities of <5 km appeared to have greater epidemiological relevance than longer distances, likely related to diminishing probabilities of local area spread at greater distances. However, the greater overall levels of connectivity of the spatial network compared to the movement network highlights the vulnerability of pig populations to widespread transmission via this route. By combining genetic data with network analysis, this research advances our understanding of dynamics of between-farm spread of PRRSV, helps establish the relative importance of transmission via animal movements versus local area spread, and highlights the potential for targeted control strategies based upon heterogeneities in network connectivity., (Copyright © 2020. Published by Elsevier B.V.)

Foot and mouth disease (FMD) virus serotype O is the predominant cause of FMD outbreaks in several regions of the world including India. Five independent neutralizing antigenic sites have been identified on the capsid surface of FMD virus serotype O. The relative importance of these neutralizing sites in eliciting antibody responses in the polyclonal sera collected from un-infected vaccinated (both primo and multiply-vaccinated) and naturally infected cattle populations were determined through a combination of reverse genetics and serology. The known critical amino acid residues present on the five antigenic sites of FMD virus serotype O Indian vaccine strain O IND R2/1975 were mutated through site-directed mutagenesis. The mutant viruses were rescued in cell-culture and analyzed through virus-neutralization assays along with parent virus using the polyclonal sera collected from three groups of cattle. In the polyclonal sera from primo-vaccinated cattle, significantly higher level of antibodies were directed towards antigenic site 2. In contrast, in polyclonal sera from multiply vaccinated animals, both antigenic sites 1 and 2 were equally important. In case of naturally infected animals, antibody responses were elicited against all the five antigenic sites. Although a drop in neutralization titres was observed for all the mutants, in one instance, increase in titre was noticed for a site 3 mutant. The findings from this study extend our knowledge on the antibody immunodominace following FMDV vaccination and infection, and may improve our strategies for vaccine strain selection and rational vaccine design., Competing Interests: Declaration of Competing Interest None., (Copyright © 2019 Elsevier B.V. All rights reserved.)

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.

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

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

Foot-and-mouth disease virus (FMDV) has a substantial impact on cattle populations in Uganda, causing short- and long-term production losses and hampering local and international trade. Although FMDV has persisted in Uganda for at least 60 years, its epidemiology there and in other endemic settings remains poorly understood. Here, we utilized a large-scale cross-sectional study of cattle to elucidate the dynamics of FMDV spread in Uganda. Sera samples (n = 14,439) from 211 herds were analyzed for non-structural protein reactivity, an indication of past FMDV exposure. Serological results were used to determine spatial patterns, and a Bayesian multivariable logistic regression mixed model was used to identify risk factors for FMDV infection. Spatial clustering of the disease was evident, with higher risk demonstrated near international borders. Additionally, high cattle density, low annual rainfall, and pastoralism were associated with increased likelihood of FMD seropositivity. These results provide insights into the complex epidemiology of FMDV in Uganda and will help inform refined control strategies in Uganda and other FMDV-endemic settings., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Porcine Reproductive and Respiratory Syndrome Virus (PRRSV) is the most important endemic pathogen in the U.S. swine industry. Despite control efforts involving improved biosecurity and different vaccination protocols, the virus continues to circulate and evolve. One of the foremost challenges in its control is high levels of genetic and antigenic diversity. Here, we quantify the co-circulation, emergence and sequential turnover of multiple PRRSV lineages in a single swine-producing region in the United States over a span of 9 years (2009-2017). By classifying over 4,000 PRRSV sequences (open-reading frame 5) into phylogenetic lineages and sub-lineages, we document the ongoing diversification and temporal dynamics of the PRRSV population, including the rapid emergence of a novel sub-lineage that appeared to be absent globally pre-2008. In addition, lineage 9 was the most prevalent lineage from 2009 to 2010, but its occurrence fell to 0.5% of all sequences identified per year after 2014, coinciding with the emergence or re-emergence of lineage 1 as the dominant lineage. The sequential dominance of different lineages, as well as three different sub-lineages within lineage 1, is consistent with the immune-mediated selection hypothesis for the sequential turnover in the dominant lineage. As host populations build immunity through natural infection or vaccination toward the most common variant, this dominant (sub-) lineage may be replaced by an emerging variant to which the population is more susceptible. An analysis of patterns of non- synonymous and synonymous mutations revealed evidence of positive selection on immunologically important regions of the genome, further supporting the potential that immune-mediated selection shapes the evolutionary and epidemiological dynamics for this virus. This has important implications for patterns of emergence and re-emergence of genetic variants of PRRSV that have negative impacts on the swine industry. Constant surveillance on PRRSV occurrence is crucial to a better understanding of the epidemiological and evolutionary dynamics of co-circulating viral lineages. Further studies utilizing whole genome sequencing and exploring the extent of cross-immunity between heterologous PRRS viruses could shed further light on PRRSV immunological response and aid in developing strategies that might be able to diminish disease impact., (Copyright © 2019 Paploski, Corzo, Rovira, Murtaugh, Sanhueza, Vilalta, Schroeder and VanderWaal.)

We report the genome sequence of a foot-and-mouth disease virus (FMDV) serotype A topotype Africa isolate collected from bovine vesicular epithelium from Kenya in 2016. This novel sequence updates the knowledge of FMDV diversity in eastern Africa and has important implications for FMDV epidemiology and molecular analyses.

Here, we report the near-complete genomes of three Southern African Territories 1 (SAT1) serotype strains and one SAT2 serotype strain of foot-and-mouth disease virus (FMDV) recently isolated from Kenya. Viral isolates were obtained from bovine epithelial tissues collected in 2014 and 2016 following outbreaks of foot-and-mouth disease (FMD). These near-complete genome sequences provide a critical update of Kenyan FMDV molecular epidemiology.

Here, we report the results of a cross-sectional study designed to monitor the circulation and genetic diversity of foot and mouth disease virus (FMDV) in Uganda between 2014 and 2017. In this study, 13,614 sera and 2,068 oral-pharyngeal fluid samples were collected from cattle and analysed to determine FMDV seroprevalence, circulating serotypes and their phylogenetic relationships. Circulation of FMDV was evidenced by the detection of antibodies against non-structural proteins of FMDV or viral isolations in all districts sampled in Uganda. Sequence analysis revealed the presence of FMDV serotypes A, O, SAT 1 and SAT 2. FMDVs belonging to serotype O, isolated from 21 districts, were the most prevalent and were classified into six lineages within two East African topotypes, namely EA-1 and EA-2. Serotype A viruses belonging to the Africa G-I topotype were isolated from two districts. SAT 1 viruses grouped within topotypes I and IV and SAT 2 viruses within topotypes VII, IV and X were isolated from six and four districts respectively. Phylogenetic analysis of SAT 1 and SAT 2 sequences from cattle clustered with historical sequences from African buffalo, indicating possible interspecies transmission at the wildlife-livestock interface. In some cases, Uganda viruses also shared similarities to viral strains recovered from other regions in East Africa. This 3-year study period provides knowledge about the geographical distribution of FMDV serotypes isolated in Uganda and insights into the genetic diversity of the multiple serotypes circulating in the country. Knowledge of circulating FMDV viruses will assist in antigenic matching studies to devise improved FMDV control strategies with vaccination and vaccine strain selection for Uganda., (© 2019 Blackwell Verlag GmbH.)

Lumpy skin disease virus (LSDV) is an infectious disease of cattle transmitted by arthropod vectors which results in substantial economic losses due to impact on production efficiency and profitability, and represents an emerging threat to international trade of livestock products and live animals. Since 2015, the disease has spread into the Northern Hemisphere including Azerbaijan, Kazakhstan, the Russian Federation and the Balkans. The rapid expansion of LSDV in those regions represented the emergence of the virus in more temperate regions than those in which LSDV traditionally occurred. The goal of this study was to assess the risk for further LSDV spread through the (a) analysis of environmental factors conducive for LSDV, and (b) estimate of the underlying LSDV risk, using a combination of ecological niche modelling and fine spatiotemporally explicit Bayesian hierarchical model on LSDV outbreak occurrence data. We used ecological niche modelling to estimate the potential distribution of LSDV outbreaks for 2014-2016. That analysis resulted in a spatial representation of environmental limits where, if introduced, LSDV is expected to efficiently spread. The Bayesian space-time model incorporated both environmental factors and the changing spatiotemporal distribution of the disease to capture the dynamics of disease spread and predict areas in which there is an increased risk for LSDV occurrence. Variables related to the average temperature, precipitation, wind speed, as well as land cover and host densities were important drivers explaining the observed distribution of LSDV in both modelling approaches. Areas of elevated LSDV risks were identified mainly in Russia, Turkey, Serbia and Bulgaria. The results suggest that, if current ecological and epidemiological conditions persist, further spread of LSDV in Eurasia may be expected. The results presented here advance our understanding of the ecological requirements of LSDV in temperate regions and may help in the design and implementation of prevention and surveillance strategies in the region., (© 2019 Blackwell Verlag GmbH.)

This is the first report of two near-complete genome sequences of foot-and-mouth disease virus (FMDV) serotype O from Kenya. The viruses were isolated from bovine epithelium collected in 2014 and 2016 from local FMD outbreaks. These full-genome sequences are critical for improving the understanding of regional FMDV molecular epidemiology.

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.

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

Understanding the dynamics of foot-and-mouth disease virus (FMDV), an endemic and economically constraining disease, is critical in designing control programmes in Africa. This study investigates the evolutionary epidemiology of SAT1 and SAT2 FMDV in Eastern Africa, as well as between cattle and wild African buffalo. Bayesian phylodynamic models were used to analyse SAT1 and SAT2 VP1 gene segments collected between 1975 and 2016, focusing on the SAT1 and SAT2 viruses currently circulating in Eastern Africa. The root state posterior probabilities inferred from our analyses suggest Zimbabwe as the ancestral location for SAT1 currently circulating in Eastern Africa (p = 0.67). For the SAT2 clade, Kenya is inferred to be the ancestral location for introduction of the virus into other countries in Eastern Africa (p = 0.72). Salient (Bayes factor >10) viral dispersal routes were inferred from Tanzania to Kenya, and from Kenya to Uganda for SAT1 and SAT2, respectively. Results suggest that cattle are the source of the SAT1 and SAT2 clades currently circulating in Eastern Africa. In addition, our results suggest that the majority of SAT1 and SAT2 in livestock come from other livestock rather than wildlife, with limited evidence that buffalo serve as reservoirs for cattle. Insights from the present study highlight the role of cattle movements and anthropogenic activities in shaping the evolutionary history of SAT1 and SAT2 in Eastern Africa. While the results may be affected by inherent limitations of imperfect surveillance, our analysis elucidates the dynamics between host species in this region, which is key to guiding disease intervention activities., (© 2019 John Wiley & Sons Ltd.)

Understanding between-farm movement patterns is an essential component in developing effective surveillance and control programs in livestock populations. Quantitative knowledge on movement patterns is particularly important for the commercial swine industry, in which large numbers of pigs are frequently moved between farms. Here, we described the annual movement patterns between swine farms in three production systems of the United States and identified farms that may be targeted to increase the efficacy of infectious disease control strategies. Research results revealed a high amount of variability in movement patterns across production systems, indicating that quantities captured from one production system and applied to another may lead to invalid estimations of disease spread. Furthermore, we showed that targeting farms based on their mean infection potential, a metric that captured the temporal sequence of movements, substantially reduced the potential for transmission of an infectious pathogen in the contact network and performed consistently well across production systems. Specifically, we found that by targeting farms based on their mean infection potential, we could reduce the potential spread of an infectious pathogen by 80% when removing approximately 25% of farms in each of the production systems. Whereas other metrics, such as degree, required 26-35% of farms to be removed in two of the production systems to reach the same outcome; this outcome was not achievable in one of the production systems. Our results demonstrate the importance of fine-scale temporal movement data and the need for in-depth understanding of the contact structure in developing more efficient disease surveillance and response strategies in swine production systems., (Copyright © 2019 Elsevier B.V. All rights reserved.)

The spread of pathogens in swine populations is in part determined by movements of animals between farms. However, understanding additional characteristics that predict disease outbreaks and uncovering landscape factors related to between-farm spread are crucial steps toward risk mitigation. This study integrates animal movements with environmental risk factors to identify the occurrence of porcine epidemic diarrhea virus (PEDV) outbreaks. Using weekly farm-level incidence data from 332 sow farms, we applied machine-learning algorithms to quantify associations between risk factors and PEDV outbreaks with the ultimate goal of training predictive models and to identify the most important factors associated with PEDV occurrence. Our best algorithm was able to correctly predict whether an outbreak occurred during one-week periods with >80% accuracy. The most important predictors included pig movements into neighboring farms. Other important neighborhood attributes included hog density, environmental and weather factors such as vegetation, wind speed, temperature, and precipitation, and topographical features such as slope. Our neighborhood-based approach allowed us to simultaneously capture disease risks associated with long-distance animal movement as well as local spatial dynamics. The model presented here forms the foundation for near real-time disease mapping and will advance disease surveillance and control for endemic swine pathogens in the United States.

Between-farm transmission of bovine tuberculosis (bTB) occurs mostly through fence-to-fence contact between neighbouring farms, endemic infected wildlife or movement of infected animals. Unfortunately, bTB detection is frequently delayed and identification of the source of introduction is often difficult, particularly in endemic regions. Here, we characterized the cattle movement network of Castilla y Leon, a high bTB-prevalence (1.9% at the farm level in 2015) region in Spain, over six years and analysed the distribution of bTB to ultimately assess the likelihood of spatial and movement-mediated transmission. We analysed movement and bTB data from 27,633 units located in the region, of which 87% were involved in ~1.4 million movements of ~8.8 million animals. Network-level connectivity was low, although a few highly connected units were identified. Up to 15% of the herds became bTB-positive at some point during the study, with the highest percentage found in bullfighting and beef herds. Although bTB-positive herds had a significantly higher degree and moved more cattle than negative herds. Results of the k-test, a permutation-based procedure, suggested that positive farms were not significantly clustered in the movement network. Location was a likely risk factor as bTB-positive farms tended to be located within 5 km from each other. Results suggested that movements may be a source of bTB in cattle in Castilla y Leon, although local factors may be more influential in determining risk of disease at the farm level. The description of the movement network in Castilla y Leon may be valuable for bTB surveillance in Spain. Moreover, results are useful to assess the movement-associated risk for multiple diseases., (© 2018 Blackwell Verlag GmbH.)

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., (Copyright © 2018 American Society for Microbiology.)

Background: Anthrax is a zoonotic disease primarily of herbivores, caused by Bacillus anthracis, a bacterium with diverse geographical and global distribution. Globally, livestock outbreaks have declined but in Africa significant outbreaks continue to occur with most countries still categorized as enzootic, hyper endemic or sporadic. Uganda experiences sporadic human and livestock cases. Severe large-scale outbreaks occur periodically in hippos (Hippopotamus amphibious) at Queen Elizabeth Protected Area, where in 2004/2005 and 2010 anthrax killed 437 hippos. Ecological drivers of these outbreaks and potential of hippos to maintain anthrax in the ecosystem remain unknown. This study aimed to describe spatio-temporal patterns of anthrax among hippos; examine significant trends associated with case distributions; and generate hypotheses for investigation of ecological drivers of anthrax., Methods: Spatio-temporal patterns of 317 hippo cases in 2004/5 and 137 in 2010 were analyzed. QGIS was used to examine case distributions; Spearman's nonparametric tests to determine correlations between cases and at-risk hippo populations; permutation models of the spatial scan statistics to examine spatio-temporal clustering of cases; directional tests to determine directionality in epidemic movements; and standard epidemic curves to determine patterns of epidemic propagation., Key Findings: Results showed hippopotamus cases extensively distributed along water shorelines with strong positive correlations (p<0.01) between cases and at-risk populations. Significant (p<0.001) spatio-temporal clustering of cases occurred throughout the epidemics, pointing towards a defined source. Significant directional epidemic spread was detected along water flow gradient (206.6°) in 2004/5 and against flow gradient (20.4°) in 2010. Temporal distributions showed clustered pulsed epidemic waves., Conclusion: These findings suggest mixed point-source propagated pattern of epidemic spread amongst hippos and points to likelihood of indirect spread of anthrax spores between hippos mediated by their social behaviour, forces of water flow, and persistent presence of infectious carcasses amidst schools. This information sheds light on the epidemiology of anthrax in highly social wildlife, can help drive insight into disease control, wildlife conservation, and tourism management, but highlights the need for analytical and longitudinal studies aimed at clarifying the hypotheses., Competing Interests: The authors have declared that no competing interests exist.

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., Competing Interests: The authors declare no conflict of interest.

In the United States, slaughter surveillance combined with other measures has effectively maintained a very low prevalence of bovine tuberculosis (bTB). However, bTB continues to be sporadically detected, causing substantial economic burden to the government and cattle producers. To detect the infection earlier and reduce sudden economic losses, additional risk-based surveillance of live animals might be more cost-effective than slaughter surveillance alone to detect and prevent bTB infection. The objective of this study was to evaluate alternative risk-based surveillance strategies targeting high-risk herds to complement slaughter surveillance in a region with very low bTB prevalence. We developed an integrated within- and between-herd bTB transmission model with simulated premises-level cattle movements among beef and dairy herds in Minnesota for 10 years. We constructed ten risk-based surveillance strategies for beef herds and dairy herds, and predicted the epidemiological outcomes and costs for each strategy in combination with slaughter surveillance. Our models showed that slaughter surveillance alone resulted in low risk of between-herd transmission with typically small outbreak sizes, and also cost less compared to alternative risk-based surveillance measures. However, risk-based surveillance strategies could reduce the time to detect infection and the time to reach disease freedom by up to 9 months. At a higher initial prevalence, alternative risk-based surveillance could reduce the number of infected herds and shorten the time to disease freedom by almost 3 years (34-35 months). Our findings suggest that risk-based surveillance could detect infection more quickly and allow affected regions to reach disease freedom faster. If the bTB status of the affected regions changes after an outbreak happens, the reduced time to disease freedom could reduce the economic impact on the affected region., (Published by Elsevier B.V.)

Epidemiological models of the spread of pathogens in livestock populations primarily focus on direct contact between farms based on animal movement data, and in some cases, local spatial spread based on proximity between premises. The roles of other types of indirect contact among farms is rarely accounted for. In addition, data on animal movements is seldom available in the United States. However, the spread of porcine epidemic diarrhea virus (PEDv) in U.S. swine represents one of the best documented emergences of a highly infectious pathogen in the U.S. livestock industry, providing an opportunity to parameterize models of pathogen spread via direct and indirect transmission mechanisms in swine. Using observed data on pig movements during the initial phase of the PEDv epidemic, we developed a network-based and spatially explicit epidemiological model that simulates the spread of PEDv via both indirect and direct movement-related contact in order to answer unresolved questions concerning factors facilitating between-farm transmission. By modifying the likelihood of each transmission mechanism and fitting this model to observed epidemiological dynamics, our results suggest that between-farm transmission was primarily driven by direct mechanisms related to animal movement and indirect mechanisms related to local spatial spread based on geographic proximity. However, other forms of indirect transmission among farms, including contact via contaminated vehicles and feed, were responsible for high consequence transmission events resulting in the introduction of the virus into new geographic areas. This research is among the first reports of farm-level animal movements in the U.S. swine industry and, to our knowledge, represents the first epidemiological model of commercial U.S. swine using actual data on farm-level animal movement., (Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.)

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

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.

Capturing heterogeneity in contact patterns in animal populations is essential for understanding the spread of infectious diseases. In contrast to other regions of the world in which livestock movement networks are integral to pathogen prevention and control policies, contact networks are understudied in pastoral regions of Africa due to the challenge of measuring contact among mobile herds of cattle whose movements are driven by access to resources. Furthermore, the extent to which seasonal changes in the distribution of water and resources impacts the structure of contact networks in cattle is uncertain. Contact networks may be more conducive to pathogen spread in the dry season due to congregation at limited water sources. Alternatively, less abundant forage may result in decreased pathogen transmission due to competitive avoidance among herds, as measured by reduced contact rates. Here, we use GPS technology to concurrently track 49 free-roaming cattle herds within a semi-arid region of Kenya, and use these data to characterize seasonal contact networks and model the spread of a highly infectious pathogen. This work provides the first empirical data on the local contact network structure of mobile herds based on quantifiable contact events. The contact network demonstrated high levels of interconnectivity. An increase in contacts near to water resources in the dry season resulted in networks with both higher contact rates and higher potential for pathogen spread than in the wet season. Simulated disease outbreaks were also larger in the dry season. Results support the hypothesis that limited water resources enhance connectivity and transmission within contact networks, as opposed to reducing connectivity as a result of competitive avoidance. These results cast light on the impact of seasonal heterogeneity in resource availability on predicting pathogen transmission dynamics, which has implications for other free-ranging wild and domestic populations., Competing Interests: We have no competing interests.

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 (FIV Ple ) in African lions (Panthera leo) at multiple scales in the Serengeti National Park, Tanzania. We used FIV Ple 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 FIV Ple co-occurrence networks and assessed what combination of social networks, spatial networks or co-infection best structured the FIV Ple network. While social organization (i.e., pride) was an important component of FIV Ple transmission pathways at all scales, we find that FIV Ple 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 FIV Ple 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., (© 2017 The Authors. Journal of Animal Ecology © 2017 British Ecological Society.)

Several foot-and-mouth disease virus (FMDV) carrier cattle were identified in Vietnam by the recovery of infectious virus from oropharyngeal fluid. This report contains the first near-complete genome sequences of seven viruses from sequential samples from one carrier animal collected over the course of 1 year. The characterization of within-host viral evolution has implications for FMDV control strategies.

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

Bovine tuberculosis (bTB) is a chronic disease of cattle that is difficult to control and eradicate in part due to the costly nature of surveillance and poor sensitivity of diagnostic tests. Like many countries, bTB prevalence in Uruguay has gradually declined to low levels due to intensive surveillance and control efforts over the past decades. In low prevalence settings, broad-based surveillance strategies based on routine testing may not be the most cost-effective way for controlling between-farm bTB transmission, while targeted surveillance aimed at high-risk farms may be more efficient for this purpose. To investigate the efficacy of targeted surveillance, we developed an integrated within- and between-farm bTB transmission model utilizing data from Uruguay's comprehensive animal movement database. A genetic algorithm was used to fit uncertain parameter values, such as the animal-level sensitivity of skin testing and slaughter inspection, to observed bTB epidemiological data. Of ten alternative surveillance strategies evaluated, a strategy based on eliminating testing in low-risk farms resulted in a 40% reduction in sampling effort without increasing bTB incidence. These results can inform the design of more cost-effective surveillance programs to detect and control bTB in Uruguay and other countries with low bTB prevalence.

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

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

Networks are often used to incorporate heterogeneity in contact patterns in mathematical models of pathogen spread. However, few tools exist to evaluate whether potential transmission pathways in a population are adequately represented by an observed contact network. Here, we describe a novel permutation-based approach, the network k-test, to determine whether the pattern of cases within the observed contact network are likely to have resulted from transmission processes in the network, indicating that the network represents potential transmission pathways between nodes. Using simulated data of pathogen spread, we compare the power of this approach to other commonly used analytical methods. We test the robustness of this technique across common sampling constraints, including undetected cases, unobserved individuals and missing interaction data. We also demonstrate the application of this technique in two case studies of livestock and wildlife networks. We show that the power of the k-test to correctly identify the epidemiologic relevance of contact networks is substantially greater than other methods, even when 50% of contact or case data are missing. We further demonstrate that the impact of missing data on network analysis depends on the structure of the network and the type of missing data., (© 2016 The Author(s).)

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

Parasitic infections transmitted between livestock and wildlife pose a significant risk to wildlife conservation efforts and constrain livestock productivity in tropical regions of the world. Gastrointestinal helminths are among the most ubiquitous parasites, and many parasites within this taxon can readily infect a wide range of host species. Factors shaping bidirectional transmission of parasites in wildlife-livestock systems are understudied. In this study, we investigate the prevalence and diversity of helminth infections in an East African community of wild and domestic ungulates. We also identify pairs of host species between which transmission may be possible based on shared parasite taxa, and explore the role of multi-host aggregations in shaping patterns of parasite sharing. Helminth taxa detected included Trichostrongylus, Trichuris, Paramphistomum, Skrjabinema, Strongyloides, Strongylus spp., and other strongyle-type nematodes. We found that nearly 50% of individuals harbored at least one species of helminth, but certain species, such as zebra and impala, exhibited higher prevalence than others. High canopy feeders, like giraffe, had lower prevalence than hosts feeding at medium and low foraging heights. For helminths, patterns of parasite sharing likely emerge from shared space use, which is mediated in part by mixed-species aggregations. The frequency with which host species associated together in mixed-species aggregations was positively correlated with the number of parasite taxa shared. We suggest that variation among species in their tendency to form mixed-species aggregations creates heterogeneity in transmission opportunities, and consequently, parasite sharing across ungulate species. These results enhance our understanding of the role of spatiotemporal relationships among host species in shaping parasite communities in mixed wildlife-livestock grazing systems., (Copyright © 2014 Elsevier B.V. All rights reserved.)

The nexin-dynein regulatory complex (N-DRC) is proposed to coordinate dynein arm activity and interconnect doublet microtubules. Here we identify a conserved region in DRC4 critical for assembly of the N-DRC into the axoneme. At least 10 subunits associate with DRC4 to form a discrete complex distinct from other axonemal substructures. Transformation of drc4 mutants with epitope-tagged DRC4 rescues the motility defects and restores assembly of missing DRC subunits and associated inner-arm dyneins. Four new DRC subunits contain calcium-signaling motifs and/or AAA domains and are nearly ubiquitous in species with motile cilia. However, drc mutants are motile and maintain the 9 + 2 organization of the axoneme. To evaluate the function of the N-DRC, we analyzed ATP-induced reactivation of isolated axonemes. Rather than the reactivated bending observed with wild-type axonemes, ATP addition to drc-mutant axonemes resulted in splaying of doublets in the distal region, followed by oscillatory bending between pairs of doublets. Thus the N-DRC provides some but not all of the resistance to microtubule sliding and helps to maintain optimal alignment of doublets for productive flagellar motility. These findings provide new insights into the mechanisms that regulate motility and further highlight the importance of the proximal region of the axoneme in generating flagellar bending.

The advent of high-throughput technologies and the concurrent advances in information sciences have led to an explosion in size and complexity of the data sets collected in biological sciences. The biggest challenge today is to assimilate this wealth of information into a conceptual framework that will help us decipher biological functions. A large and complex collection of data, usually called a data cloud, naturally embeds multi-scale characteristics and features, generically termed geometry. Understanding this geometry is the foundation for extracting knowledge from data. We have developed a new methodology, called data cloud geometry-tree (DCG-tree), to resolve this challenge. This new procedure has two main features that are keys to its success. Firstly, it derives from the empirical similarity measurements a hierarchy of clustering configurations that captures the geometric structure of the data. This hierarchy is then transformed into an ultrametric space, which is then represented via an ultrametric tree or a Parisi matrix. Secondly, it has a built-in mechanism for self-correcting clustering membership across different tree levels. We have compared the trees generated with this new algorithm to equivalent trees derived with the standard Hierarchical Clustering method on simulated as well as real data clouds from fMRI brain connectivity studies, cancer genomics, giraffe social networks, and Lewis Carroll's Doublets network. In each of these cases, we have shown that the DCG trees are more robust and less sensitive to measurement errors, and that they provide a better quantification of the multi-scale geometric structures of the data. As such, DCG-tree is an effective tool for analyzing complex biological data sets.

To understand the mechanisms that regulate the assembly and activity of flagellar dyneins, we focused on the I1 inner arm dynein (dynein f) and a null allele, bop5-2, defective in the gene encoding the IC138 phosphoprotein subunit. I1 dynein assembles in bop5-2 axonemes but lacks at least four subunits: IC138, IC97, LC7b, and flagellar-associated protein (FAP) 120--defining a new I1 subcomplex. Electron microscopy and image averaging revealed a defect at the base of the I1 dynein, in between radial spoke 1 and the outer dynein arms. Microtubule sliding velocities also are reduced. Transformation with wild-type IC138 restores assembly of the IC138 subcomplex and rescues microtubule sliding. These observations suggest that the IC138 subcomplex is required to coordinate I1 motor activity. To further test this hypothesis, we analyzed microtubule sliding in radial spoke and double mutant strains. The results reveal an essential role for the IC138 subcomplex in the regulation of I1 activity by the radial spoke/phosphorylation pathway.

The transport of materials to and from the cell body and tips of eukaryotic flagella and cilia is carried out by a process called intraflagellar transport, or IFT. This process is essential for the assembly and maintenance of cilia and flagella: in the absence of IFT, cilia cannot assemble and, if IFT is arrested in ciliated cells, the cilia disassemble. The major IFT complex proteins and the major motor proteins, kinesin-2 and osm-3 (which transport particles from the cell body to ciliary tips) and cytoplasmic dynein 1b (which transports particles from ciliary tips to the cell body) have been identified. However, we have little understanding of the structure of the IFT particles, the cargo that these particles carry, how cargo is loaded and unloaded from the particles, or how the motor proteins are regulated. The focus of this chapter is to provide methods to observe and quantify the movements of IFT particles in Chlamydomonas flagella. IFT movements can be visualized in paralyzed or partially arrested flagella using either differential interference contrast (IFT) microscopy or, in cells with fluorescently tagged IFT components, with fluorescence microscopy. Methods for recording IFT movements and analyzing movements using kymograms are described., (2009 Elsevier Inc. All rights reserved.)