@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} }
 @article{acosta_cardenas_imbacuan_lentz_dietze_amaku_burbano_goncalves_ferreira_2022, title={Modelling control strategies against classical swine fever: Influence of traders and markets using static and temporal networks in Ecuador}, volume={205}, ISSN={["1873-1716"]}, DOI={10.1016/j.prevetmed.2022.105683}, abstractNote={Pig farming in Ecuador represents an important economic and cultural sector, challenged by classical swine fever (CSF). Recently, the National Veterinary Service (NVS), has dedicated its efforts to control the disease by implementing pig identification, mandatory vaccination against CSF and movement control. Our objective was to characterise pig premises according to risk criteria, to model the effect of movement restriction strategies and to consider the temporal evolution of the network. Social network analysis (SNA), SIRS (susceptible, infected, recovered, susceptible) network modelling and temporal analysis were used. The network contained 751,003 shipments and 6 million pigs from 2017 to 2019. Participating premises consisted of 144,118 backyard farms, 138 industrial farms, 21,337 traders and 51 markets. The 10 most influential markets, in the Andean highlands, received between 500 and 4600 pigs each week. The 10 most influential traders made about 3 shipments with 17 pigs per week. Simulations without control strategy resulted in an average CSF prevalence of 14.4 %; targeted movement restriction reduced the prevalence to 7.2 %, while with random movement restriction it was 13 %. Targeting the top 10 national traders and markets and one of the high-risk premises in every parish was one of the best strategies with the surveillance infrastructure available, highlighting its major influence and epidemiological importance in the network. When comparing the static network with its temporal counterpart, causal fidelity (c = 0.62) showed a 38 % overestimation in the number of transmission paths, also traversing the network required 4.39 steps, lasting approximately 233 days. In conclusion, NVS surveillance strategies could be more efficient by targeting the most at-risk premises, and in particular, taking into account the temporal information would make the risk assessment much more precise. This information could contribute to implement risk-based surveillance reducing the time to eradicate CSF and other infectious animal diseases.}, journal={PREVENTIVE VETERINARY MEDICINE}, author={Acosta, Alfredo and Cardenas, Nicolas Cespedes and Imbacuan, Cristian and Lentz, Hartmut H. K. and Dietze, Klaas and Amaku, Marcos and Burbano, Alexandra and Goncalves, Vitor S. P. and Ferreira, Fernando}, year={2022}, month={Aug} }
 @article{cardenas_sykes_lopes_machado_2022, title={Multiple species animal movements: network properties, disease dynamics and the impact of targeted control actions}, volume={53}, ISSN={["1297-9716"]}, DOI={10.1186/s13567-022-01031-2}, abstractNote={Abstract}, number={1}, journal={VETERINARY RESEARCH}, author={Cardenas, Nicolas C. and Sykes, Abagael L. and Lopes, Francisco P. N. and Machado, Gustavo}, year={2022}, month={Feb} }
 @article{acosta_cardenas_pisuna_galvis_vinueza_vasquez_grisi-filho_amaku_goncalves_ferreira_2022, title={Network analysis of pig movements in Ecuador: Strengthening surveillance of classical swine fever}, ISSN={["1865-1682"]}, DOI={10.1111/tbed.14640}, abstractNote={The analysis of domestic pig movements has become useful to understand the disease spread patterns and epidemiology, which facilitates the development of more effective animal diseases control strategies. The aim of this work was to analyse the static and spatial characteristics of the pig network, to identify its trading communities and to study the contribution of the network to the transmission of classical swine fever. In this regard, we used the pig movement records from the National veterinary service of Ecuador (2017-2019), using social network analysis and spatial analysis to construct a network with registered premises as nodes and their movements as edges. Furthermore, we also created a network of parishes as its nodes by aggregating their premises movements as edges. The annual network metrics showed an average diameter of 20.33, a number of neighbours of 2.61, a shortest path length of 4.39 and a clustering coefficient of 0.38 (small-world structure). The most frequent movements were to or from markets (55%). Backyard producers made up 89% of the network premises, and the top 2% of parishes (highest degree) contributed to 50% of the movements. The highest frequencies of movements between parishes were in the centre of the country, while the highest frequency of movements to abattoirs was in the south-west. Finally, the pattern of CSF disease outbreaks within the Ecuador network was likely the result of network transmission processes. In conclusion, our results represented the first exploratory analysis of domestic pig movements at premise and parish levels. The surveillance system could consider these results to improve its procedures and update the disease control and management policy, and allow the implementation of targeted or risk-based surveillance. This article is protected by copyright. All rights reserved.}, journal={TRANSBOUNDARY AND EMERGING DISEASES}, author={Acosta, Alfredo Javier and Cardenas, Nicolas Cespedes and Pisuna, Luis Miguel and Galvis, Jason A. and Vinueza, Rommel Lenin and Vasquez, Kleber Stalin and Grisi-Filho, Jose Henrique and Amaku, Marcos and Goncalves, Victor Salvador and Ferreira, Fernando}, year={2022}, month={Jul} }