Galvis Jason A, Jones Chris M, Prada Joaquin M, Corzo Cesar A, Machado Gustavo
Department of Population Health and Pathobiology, College of Veterinary Medicine, Raleigh, NC, USA.
Center for Geospatial Analytics, North Carolina State University, Raleigh, NC, USA.
Transbound Emerg Dis. 2022 Mar;69(2):396-412. doi: 10.1111/tbed.13997. Epub 2021 Feb 21.
A limited understanding of the transmission dynamics of swine disease is a significant obstacle to prevent and control disease spread. Therefore, understanding between-farm transmission dynamics is crucial to developing disease forecasting systems to predict outbreaks that would allow the swine industry to tailor control strategies. Our objective was to forecast weekly porcine epidemic diarrhoea virus (PEDV) outbreaks by generating maps to identify current and future PEDV high-risk areas, and simulating the impact of control measures. Three epidemiological transmission models were developed and compared: a novel epidemiological modelling framework was developed specifically to model disease spread in swine populations, PigSpread, and two models built on previously developed ecosystems, SimInf (a stochastic disease spread simulations) and PoPS (Pest or Pathogen Spread). The models were calibrated on true weekly PEDV outbreaks from three spatially related swine production companies. Prediction accuracy across models was compared using the receiver operating characteristic area under the curve (AUC). Model outputs had a general agreement with observed outbreaks throughout the study period. PoPS had an AUC of 0.80, followed by PigSpread with 0.71, and SimInf had the lowest at 0.59. Our analysis estimates that the combined strategies of herd closure, controlled exposure of gilts to live viruses (feedback) and on-farm biosecurity reinforcement reduced the number of outbreaks. On average, 76% to 89% reduction was seen in sow farms, while in gilt development units (GDU) was between 33% to 61% when deployed to sow and GDU farms located in probabilistic high-risk areas. Our multi-model forecasting approach can be used to prioritize surveillance and intervention strategies for PEDV and other diseases potentially leading to more resilient and healthier pig production systems.
对猪病传播动态的有限了解是预防和控制疾病传播的重大障碍。因此，了解农场间传播动态对于开发疾病预测系统以预测疫情爆发至关重要，这将使养猪业能够制定针对性的控制策略。我们的目标是通过生成地图来识别当前和未来的猪流行性腹泻病毒（PEDV）高风险区域，并模拟控制措施的影响，从而预测每周的PEDV疫情爆发。开发并比较了三种流行病学传播模型：专门为模拟猪群疾病传播而开发的新型流行病学建模框架PigSpread，以及基于先前开发的生态系统构建的两个模型SimInf（一种随机疾病传播模拟）和PoPS（害虫或病原体传播）。这些模型根据来自三个空间相关养猪生产公司的真实每周PEDV疫情爆发数据进行校准。使用曲线下面积（AUC）的受试者工作特征来比较各模型的预测准确性。在整个研究期间，模型输出与观察到的疫情爆发总体一致。PoPS的AUC为0.80，其次是PigSpread为0.71，SimInf最低，为0.59。我们的分析估计，猪场封闭、后备母猪受控接触活病毒（反馈）和农场生物安全强化的综合策略减少了疫情爆发的数量。平均而言，母猪场的疫情爆发数量减少了76%至89%，而部署到概率高风险区域的母猪场和后备母猪培育单元（GDU）时，GDU的疫情爆发数量减少了33%至61%。我们的多模型预测方法可用于确定PEDV和其他可能导致更具恢复力和更健康养猪生产系统的疾病的监测和干预策略的优先级。
