@article{cardenas_valencio_sanchez_kathleen c. o'hara_machado_2024, title={Analyzing the intrastate and interstate swine movement network in the United States}, volume={230}, ISSN={["1873-1716"]}, DOI={10.1016/j.prevetmed.2024.106264}, abstractNote={Identifying and restricting animal movements is a common approach used to mitigate the spread of diseases between premises in livestock systems. Therefore, it is essential to uncover between-premises movement dynamics, including shipment distances and network-based control strategies. Here, we analyzed three years of between-premises pig movements, which include 197,022 unique animal shipments, 3973 premises, and 391,625,374 pigs shipped across 20 U.S. states. We constructed unweighted, directed, temporal networks at 180-day intervals to calculate premises-to-premises movement distances, the size of connected components, network loyalty, and degree distributions, and, based on the out-going contact chains, identified network-based control actions. Our results show that the median distance between premises pig movements was 74.37 km, with median intrastate and interstate movements of 52.71 km and 328.76 km, respectively. On average, 2842 premises were connected via 6705 edges, resulting in a weak giant connected component that included 91 % of the premises. The premises-level network exhibited loyalty, with a median of 0.65 (IQR: 0.45 - 0.77). Results highlight the effectiveness of node targeting to reduce the risk of disease spread; we demonstrated that targeting 25 % of farms with the highest degree or betweenness limited spread to 1.23 % and 1.7 % of premises, respectively. While there is no complete shipment data for the entire U.S., our multi-state movement analysis demonstrated the value and the needs of such data for enhancing the design and implementation of proactive- disease control tactics.}, journal={PREVENTIVE VETERINARY MEDICINE}, author={Cardenas, Nicolas C. and Valencio, Arthur and Sanchez, Felipe and Kathleen C. O'Hara and Machado, Gustavo}, year={2024}, month={Sep} }
 @article{sanchez_galvis_cardenas_corzo_jones_machado_2023, title={Spatiotemporal relative risk distribution of porcine reproductive and respiratory syndrome virus in the United States}, volume={10}, ISSN={["2297-1769"]}, url={http://dx.doi.org/10.3389/fvets.2023.1158306}, DOI={10.3389/fvets.2023.1158306}, abstractNote={Porcine reproductive and respiratory syndrome virus (PRRSV) remains widely distributed across the U.S. swine industry. Between-farm movements of animals and transportation vehicles, along with local transmission are the primary routes by which PRRSV is spread. Given the farm-to-farm proximity in high pig production areas, local transmission is an important pathway in the spread of PRRSV; however, there is limited understanding of the role local transmission plays in the dissemination of PRRSV, specifically, the distance at which there is increased risk for transmission from infected to susceptible farms. We used a spatial and spatiotemporal kernel density approach to estimate PRRSV relative risk and utilized a Bayesian spatiotemporal hierarchical model to assess the effects of environmental variables, between-farm movement data and on-farm biosecurity features on PRRSV outbreaks. The maximum spatial distance calculated through the kernel density approach was 15.3 km in 2018, 17.6 km in 2019, and 18 km in 2020. Spatiotemporal analysis revealed greater variability throughout the study period, with significant differences between the different farm types. We found that downstream farms (i.e., finisher and nursery farms) were located in areas of significant-high relative risk of PRRSV. Factors associated with PRRSV outbreaks were farms with higher number of access points to barns, higher numbers of outgoing movements of pigs, and higher number of days where temperatures were between 4°C and 10°C. Results obtained from this study may be used to guide the reinforcement of biosecurity and surveillance strategies to farms and areas within the distance threshold of PRRSV positive farms.}, journal={FRONTIERS IN VETERINARY SCIENCE}, publisher={Frontiers Media SA}, author={Sanchez, Felipe and Galvis, Jason A. and Cardenas, Nicolas C. and Corzo, Cesar and Jones, Christopher and Machado, Gustavo}, year={2023}, month={Jun} }
 @article{cardenas_sanchez_lopes_machado_2022, title={Coupling spatial statistics with social network analysis to estimate distinct risk areas of disease circulation to improve risk-based surveillance}, volume={6}, ISSN={["1865-1682"]}, url={https://doi.org/10.1111/tbed.14627}, DOI={10.1111/tbed.14627}, abstractNote={Abstract Most animal disease surveillance systems concentrate efforts in blocking transmission pathways and tracing back infected contacts while not considering the risk of transporting animals into areas with elevated disease risk. Here, we use a suite of spatial statistics and social network analysis to characterize animal movement among areas with an estimated distinct risk of disease circulation to ultimately enhance surveillance activities. Our model utilized equine infectious anemia virus (EIAV) outbreaks, between‐farm horse movements, and spatial landscape data from 2015 through 2017. We related EIAV occurrence and the movement of horses between farms with climate variables that foster conditions for local disease propagation. We then constructed a spatially explicit model that allows the effect of the climate variables on EIAV occurrence to vary through space (i.e., non‐stationary). Our results identified important areas in which in‐going movements were more likely to result in EIAV infections and disease propagation. Municipalities were then classified as having high 56 (11.3%), medium 48 (9.66%), and low 393 (79.1%) spatial risk. The majority of the movements were between low‐risk areas, altogether representing 68.68% of all animal movements. Meanwhile, 9.48% were within high‐risk areas, and 6.20% were within medium‐risk areas. Only 5.37% of the animals entering low‐risk areas came from high‐risk areas. On the other hand, 4.91% of the animals in the high‐risk areas came from low‐ and medium‐risk areas. Our results demonstrate that animal movements and spatial risk mapping could be used to make informed decisions before issuing animal movement permits, thus potentially reducing the chances of reintroducing infection into areas of low risk.}, journal={TRANSBOUNDARY AND EMERGING DISEASES}, publisher={Wiley}, author={Cardenas, Nicolas C. and Sanchez, Felipe and Lopes, Francisco P. N. and Machado, Gustavo}, year={2022}, month={Jun} }