Sykes Abagael L, Silva Gustavo S, Holtkamp Derald J, Mauch Broc W, Osemeke Onyekachukwu, Linhares Daniel C L, Machado Gustavo
Department of Population Health and Pathobiology, College of Veterinary Medicine, North Carolina State University, Raleigh, North Carolina, USA.
Veterinary Diagnostic and Production Animal Medicine Department, College of Veterinary Medicine, Iowa State University, Ames, Iowa, USA.
Transbound Emerg Dis. 2022 Jul;69(4):e916-e930. doi: 10.1111/tbed.14369. Epub 2021 Nov 16.
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 has 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. By 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.
生猪生产中有效的生物安全措施是预防传染性病原体传入和传播的关键。理想情况下，农场生物安全措施应根据其对生物遏制和生物排除的影响来选择；然而，往往缺乏定量的支持证据。因此，开发能够根据生物安全措施降低疾病风险的功效对其进行量化和排序的方法，有可能促进在更充分了解情况的基础上选择生物安全措施。利用来自139个猪群的生物安全措施、农场人口统计学和既往疫情的调查数据，训练了一组机器学习算法，根据猪繁殖与呼吸综合征病毒状态对农场进行分类，具体取决于其生物安全措施和农场人口统计学，以得出预测的疫情风险。开发了一种新型的可解释机器学习工具包MrIML - 生物安全，通过预测风险对农场和生产系统进行基准评估，并量化生物安全措施对单个农场疾病风险的影响。通过量化对预测风险的可变影响，42个变量中有50%与污染物传播相关，31%与局部传播相关。机器学习解释的结果也得出了类似的结果，发现与员工更替和数量、猪场和猪的周边密度、运输拖车的共享、与公共道路的距离以及农场生产类型相关的生物安全措施对预测的疫情风险有重大贡献。此外，个性化生物安全评估的开发为逐案更好地指导生物安全实施提供了机会。最后，MrIML - 生物安全工具包的灵活性使其有可能应用于更广泛的生物安全基准评估领域，以解决其他牲畜系统和行业相关疾病中的生物安全弱点。
