PDF(Pubmed)
Abstract：
UNASSIGNED: African swine fever (ASF) is a notifiable disease of swine that impacts global pork trade and food security. In several countries across the globe, the disease persists in wild boar (WB) populations sympatric to domestic pig (DP) operations, with continued detections in both sectors. While there is evidence of spillover and spillback between the sectors, the frequency of occurrence and relative importance of different risk factors for transmission at the wildlife-livestock interface remain unclear.
UNASSIGNED: To address this gap, we leveraged ASF surveillance data from WB and DP across Eastern Poland from 2014-2019 in an analysis that quantified the relative importance of different risk factors for explaining variation in each of the ASF surveillance data from WB and DP.
UNASSIGNED: ASF prevalence exhibited different seasonal trends across the sectors: apparent prevalence was much higher in summer (84% of detections) in DP, but more consistent throughout the year in WB (highest in winter with 45%, lowest in summer at 15%). Only 21.8% of DP-positive surveillance data included surveillance in WB nearby (within 5 km of the grid cell within the last 4 weeks), while 41.9% of WB-positive surveillance samples included any DP surveillance samples nearby. Thus, the surveillance design afforded twice as much opportunity to find DP-positive samples in the recent vicinity of WB-positive samples compared to the opposite, yet the rate of positive WB samples in the recent vicinity of a positive DP sample was 48 times as likely than the rate of positive DP samples in the recent vicinity of a positive WB sample. Our machine learning analyses found that positive samples in WB were predicted by WB-related risk factors, but not to DP-related risk factors. In contrast, WB risk factors were important for predicting detections in DP on a few spatial and temporal scales of data aggregation.
UNASSIGNED: Our results highlight that spillover from WB to DP might be more frequent than the reverse, but that the structure of current surveillance systems challenge quantification of spillover frequency and risk factors. Our results emphasize the importance of, and provide guidance for, improving cross-sector surveillance designs.
UNASSIGNED: To address this gap, we leveraged ASF surveillance data from WB and DP across Eastern Poland from 2014-2019 in an analysis that quantified the relative importance of different risk factors for explaining variation in each of the ASF surveillance data from WB and DP.
UNASSIGNED: ASF prevalence exhibited different seasonal trends across the sectors: apparent prevalence was much higher in summer (84% of detections) in DP, but more consistent throughout the year in WB (highest in winter with 45%, lowest in summer at 15%). Only 21.8% of DP-positive surveillance data included surveillance in WB nearby (within 5 km of the grid cell within the last 4 weeks), while 41.9% of WB-positive surveillance samples included any DP surveillance samples nearby. Thus, the surveillance design afforded twice as much opportunity to find DP-positive samples in the recent vicinity of WB-positive samples compared to the opposite, yet the rate of positive WB samples in the recent vicinity of a positive DP sample was 48 times as likely than the rate of positive DP samples in the recent vicinity of a positive WB sample. Our machine learning analyses found that positive samples in WB were predicted by WB-related risk factors, but not to DP-related risk factors. In contrast, WB risk factors were important for predicting detections in DP on a few spatial and temporal scales of data aggregation.
UNASSIGNED: Our results highlight that spillover from WB to DP might be more frequent than the reverse, but that the structure of current surveillance systems challenge quantification of spillover frequency and risk factors. Our results emphasize the importance of, and provide guidance for, improving cross-sector surveillance designs.
摘要：
非洲猪瘟（ASF）是一种猪的法定报告疾病，影响全球猪肉贸易和粮食安全。在全球的几个国家,该疾病持续存在于与家猪（DP）手术同属的野猪（WB）种群中，在这两个部门都有持续的检测。虽然有证据表明这些部门之间存在溢出和溢出，在野生动物-牲畜界面传播的不同危险因素的发生频率和相对重要性尚不清楚。
■为了解决这个问题，我们利用2014-2019年来自波兰东部地区WB和DP的ASF监测数据进行分析,量化了不同风险因素的相对重要性,以解释WB和DP各自ASF监测数据的差异.
■ASF患病率在各个行业中表现出不同的季节性趋势：DP的明显患病率在夏季要高得多（检测的84％），但世界银行全年更一致(冬季最高，为45%，夏季最低，为15%)。只有21.8％的DP阳性监测数据包括附近的WB监测（在过去4周内网格单元的5公里范围内），而41.9%的WB阳性监测样本包括附近的任何DP监测样本。因此，与相反的情况相比，监测设计提供了两倍的机会在最近的WB阳性样本附近发现DP阳性样本，然而,近期DP阳性样本附近的WB阳性样本比率是近期WB阳性样本附近DP阳性样本比率的48倍.我们的机器学习分析发现，WB中的阳性样本是由WB相关的危险因素预测的，但与DP相关的危险因素无关。相比之下,WB风险因素对于在数据聚合的一些时空尺度上预测DP中的检测非常重要。
■我们的结果突出表明，从WB到DP的溢出可能比相反的溢出更频繁，但是当前监测系统的结构挑战了溢出频率和风险因素的量化。我们的研究结果强调了,并提供指导，改进跨部门监控设计。
■为了解决这个问题，我们利用2014-2019年来自波兰东部地区WB和DP的ASF监测数据进行分析,量化了不同风险因素的相对重要性,以解释WB和DP各自ASF监测数据的差异.
■ASF患病率在各个行业中表现出不同的季节性趋势：DP的明显患病率在夏季要高得多（检测的84％），但世界银行全年更一致(冬季最高，为45%，夏季最低，为15%)。只有21.8％的DP阳性监测数据包括附近的WB监测（在过去4周内网格单元的5公里范围内），而41.9%的WB阳性监测样本包括附近的任何DP监测样本。因此，与相反的情况相比，监测设计提供了两倍的机会在最近的WB阳性样本附近发现DP阳性样本，然而,近期DP阳性样本附近的WB阳性样本比率是近期WB阳性样本附近DP阳性样本比率的48倍.我们的机器学习分析发现，WB中的阳性样本是由WB相关的危险因素预测的，但与DP相关的危险因素无关。相比之下,WB风险因素对于在数据聚合的一些时空尺度上预测DP中的检测非常重要。
■我们的结果突出表明，从WB到DP的溢出可能比相反的溢出更频繁，但是当前监测系统的结构挑战了溢出频率和风险因素的量化。我们的研究结果强调了,并提供指导，改进跨部门监控设计。
