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

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.)

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.)

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 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.)

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.)

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.)