Elucidating the influence of wild boar density on African swine fever spread in wild boar populations, Italy, 2022–2023

B. H. Hayes, J. S. Lim, M. Andraud, T. Vergne
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Abstract

Wild boar density has been suggested to play a role in shaping African swine fever (ASF) transmission patterns. To provide quantitative estimates of the influence of wild boar density on ASF spread, a spatially-explicit detection-delay SIR mechanistic model of ASF transmission among density-explicit wild boar habitat was developed and parameterised to observed epidemic data in northern Italy from January 2022 through September 2023. Wild boar density estimates were generated by the ENETWILD consortium. Infectious periods, local prevalence at time of first detection, detection rates, and seasonal recovery rates were estimated directly from surveillance data. Eight models were constructed utilizing static and seasonal transmission rates along with linear relationships between habitat susceptibility/infectivity and wild boar density. Transmission rate, relative susceptibility, and relative infectivity were estimated by fitting each model to the observed epidemic using sequential Monte Carlo approximate Bayesian computation. The model that most closely fit the full data used a seasonal transmission rate but did not support a wild boar density effect on ASF spread across the entire study period. However, further analyses of the model outputs suggest that wild boar density likely played a role in shaping ASF transmission patterns during the second wave only (October 2022 – September 2023). This observation could be due to a lack of power in the first wave, lower surveillance rates in that period, or be from density estimates no longer reflecting the true wild boar density distributions upon the start of the second wave. These results demonstrate that wild boar density impacted ASF propagation in northern Italy. Further investigation by estimating parameters for individual epidemic waves could be beneficial to better characterise the wave-specific impact of wild boar density. The model developed here could be used in other contexts to evaluate if the influence of wild boar density is present across epidemic scenarios.

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阐明野猪密度对非洲猪瘟在野猪种群中传播的影响,意大利,2022-2023 年
野猪密度被认为在形成非洲猪瘟(ASF)传播模式中起着一定作用。为了定量评估野猪密度对非洲猪瘟传播的影响,我们开发了一个空间明确的检测延迟 SIR 机械模型,用于评估非洲猪瘟在密度明确的野猪栖息地之间的传播,并根据 2022 年 1 月至 2023 年 9 月在意大利北部观测到的疫情数据对该模型进行了参数化。野猪密度估计值由 ENETWILD 联盟生成。感染期、首次检测时的当地流行率、检测率和季节性恢复率直接从监测数据中估算得出。利用静态和季节性传播率以及栖息地易感性/感染性与野猪密度之间的线性关系构建了八个模型。传播率、相对易感性和相对感染性是通过使用连续蒙特卡罗近似贝叶斯计算方法将每个模型与观察到的疫情进行拟合来估算的。最接近全部数据的模型使用了季节性传播率,但不支持野猪密度对整个研究期间 ASF 传播的影响。然而,对模型输出结果的进一步分析表明,野猪密度很可能只在第二波(2022 年 10 月至 2023 年 9 月)期间对 ASF 传播模式的形成起了作用。造成这一现象的原因可能是第一波研究的力量不足、该阶段的监测率较低,或者是由于第二波研究开始后,密度估计值不再反映真实的野猪密度分布。这些结果表明,野猪密度影响了 ASF 在意大利北部的传播。通过估算各个疫情波的参数进行进一步调查,有助于更好地描述野猪密度对特定波的影响。此处开发的模型可用于其他场合,以评估野猪密度是否会对各种流行情况产生影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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