van Leuken Jeroen P G, Havelaar Arie H, van der Hoek Wim, Ladbury Georgia A F, Hackert Volker H, Swart Arno N
Institute for Risk Assessment Sciences (IRAS), Utrecht University, Utrecht, The Netherlands ; Centre for Infectious Disease Control (CIb), National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands.
PLoS One. 2013 Dec 4;8(12):e80412. doi: 10.1371/journal.pone.0080412. eCollection 2013.
Source identification in areas with outbreaks of airborne pathogens is often time-consuming and expensive. We developed a model to identify the most likely location of sources of airborne pathogens.
As a case study, we retrospectively analyzed three Q fever outbreaks in the Netherlands in 2009, each with suspected exposure from a single large dairy goat farm. Model input consisted only of case residential addresses, day of first clinical symptoms, and human population density data. We defined a spatial grid and fitted an exponentially declining function to the incidence-distance data of each grid point. For any grid point with a fit significant at the 95% confidence level, we calculated a measure of risk. For validation, we used results from abortion notifications, voluntary (2008) and mandatory (2009) bulk tank milk sampling at large (i.e. >50 goats and/or sheep) dairy farms, and non-systematic vaginal swab sampling at large and small dairy and non-dairy goat/sheep farms. In addition, we performed a two-source simulation study.
Hotspots--areas most likely to contain the actual source--were identified at early outbreak stages, based on the earliest 2-10% of the case notifications. Distances between the hotspots and suspected goat farms varied from 300-1500 m. In regional likelihood rankings including all large dairy farms, the suspected goat farms consistently ranked first. The two-source simulation study showed that detection of sources is most clear if the distance between the sources is either relatively small or relatively large.
Our model identifies the most likely location of sources in an airborne pathogen outbreak area, even at early stages. It can help to reduce the number of potential sources to be investigated by microbial testing and to allow rapid implementation of interventions to limit the number of human infections and to reduce the risk of source-to-source transmission.
在空气传播病原体爆发的地区进行源头识别通常既耗时又昂贵。我们开发了一个模型来识别空气传播病原体最可能的源头位置。
作为一个案例研究,我们回顾性分析了2009年荷兰的三起Q热疫情,每起疫情都怀疑是由一个大型奶山羊场暴露所致。模型输入仅包括病例居住地址、首次出现临床症状的日期以及人口密度数据。我们定义了一个空间网格,并对每个网格点的发病距离数据拟合了指数衰减函数。对于任何在95%置信水平下拟合显著的网格点,我们计算了一个风险度量。为了进行验证,我们使用了流产报告结果、大型(即>50只山羊和/或绵羊)奶牛场自愿(2008年)和强制(2009年)的散装罐奶采样结果,以及大型和小型奶牛场及非奶牛场山羊/绵羊场的非系统性阴道拭子采样结果。此外,我们进行了一个双源模拟研究。
根据最早的2% - 10%的病例报告,在疫情早期阶段就确定了最有可能包含实际源头的热点区域。热点区域与疑似山羊场之间的距离在300 - 1500米之间。在包括所有大型奶牛场的区域可能性排名中,疑似山羊场始终排名第一。双源模拟研究表明,如果两个源头之间的距离相对较小或相对较大,那么源头的检测最为清晰。
我们的模型能够识别空气传播病原体爆发区域中最可能的源头位置,即使在早期阶段也是如此。它有助于减少通过微生物检测进行调查的潜在源头数量,并允许迅速实施干预措施,以限制人类感染数量并降低源头间传播的风险。