@article{sykes_galvis_kathleen c. o'hara_corzo_machado_2023, title={Estimating the effectiveness of control actions on African swine fever transmission in commercial swine populations in the United States}, volume={217}, ISSN={["1873-1716"]}, url={https://doi.org/10.1016/j.prevetmed.2023.105962}, DOI={10.1016/j.prevetmed.2023.105962}, abstractNote={Given the proximity of African swine fever (ASF) to the U.S., there is an urgent need to better understand the possible dissemination pathways of the virus within the U.S. swine industry and to evaluate mitigation strategies. Here, we extended PigSpread, a farm-level spatially-explicit stochastic compartmental transmission model incorporating six transmission routes including between-farm swine movements, vehicle movements, and local spread, to model the dissemination of ASF. We then examined the effectiveness of control actions similar to the ASF national response plan. The average number of secondary infections during the first 60 days of the outbreak was 49 finisher farms, 17 nursery farms, 5 sow farms, and less than one farm in other production types. The between-farm movements of swine were the predominant route of ASF transmission with an average contribution of 71.1%, while local spread and movement of vehicles were less critical with average contributions of 14.6% and 14.4%. We demonstrated that the combination of quarantine, depopulation, movement restrictions, contact tracing, and enhanced surveillance, was the most effective mitigation strategy, resulting in an average reduction of 79.0% of secondary cases by day 140 of the outbreak. Implementing these control actions led to a median of 495,619 depopulated animals, 357,789 diagnostic tests, and 54,522 movement permits. Our results suggest that the successful elimination of an ASF outbreak is likely to require the deployment of all control actions listed in the ASF national response plan for more than 140 days, as well as estimating the resources needed for depopulation, testing, and movement permits under these controls.}, journal={PREVENTIVE VETERINARY MEDICINE}, author={Sykes, Abagael L. and Galvis, Jason A. and Kathleen C. O'Hara and Corzo, Cesar and Machado, Gustavo}, year={2023}, 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{sykes_silva_holtkamp_mauch_osemeke_linhares_machado_2021, title={Interpretable machine learning applied to on-farm biosecurity and porcine reproductive and respiratory syndrome virus}, volume={10}, ISSN={["1865-1682"]}, url={https://doi.org/10.1111/tbed.14369}, DOI={10.1111/tbed.14369}, abstractNote={Effective biosecurity practices in swine production are key in preventing the introduction and dissemination of infectious pathogens. Ideally, on-farm biosecurity practices should be chosen by their impact on bio-containment and bio-exclusion, however quantitative supporting evidence is often unavailable. Therefore, the development of methodologies capable of quantifying and ranking biosecurity practices according to their efficacy in reducing disease risk have the potential to facilitate better informed choices of biosecurity practices. Using survey data on biosecurity practices, farm demographics, and previous outbreaks from 139 herds, a set of machine learning algorithms were trained to classify farms by porcine reproductive and respiratory syndrome virus status, depending on their biosecurity practices and farm demographics, to produce a predicted outbreak risk. A novel interpretable machine learning toolkit, MrIML-biosecurity, was developed to benchmark farms and production systems by predicted risk, and quantify the impact of biosecurity practices on disease risk at individual farms. Quantifying the variable impact on predicted risk 50% of 42 variables were associated with fomite spread while 31% were associated with local transmission. Results from machine learning interpretations identified similar results, finding substantial contribution to predicted outbreak risk from biosecurity practices relating to: the turnover and number of employees; the surrounding density of swine premises and pigs; the sharing of haul trailers; distance from the public road; and farm production type. In addition, the development of individualized biosecurity assessments provides the opportunity to better guide biosecurity implementation on a case-by-case basis. Finally, the flexibility of the MrIML-biosecurity toolkit gives it the potential to be applied to wider areas of biosecurity benchmarking, to address biosecurity weaknesses in other livestock systems and industry relevant diseases. This article is protected by copyright. All rights reserved.}, journal={TRANSBOUNDARY AND EMERGING DISEASES}, publisher={Wiley}, author={Sykes, Abagael L. and Silva, Gustavo S. and Holtkamp, Derald J. and Mauch, Broc W. and Osemeke, Onyekachukwu and Linhares, Daniel C. L. and Machado, Gustavo}, year={2021}, month={Nov} }