Using genomic epidemiology and geographic activity spaces to investigate tuberculosis outbreaks in Botswana.

Chelsea R Baker, Ivan Barilar, Leonardo S de Araujo, Daniel M Parker, Kimberly Fornace, Patrick K Moonan, John E Oeltmann, James L Tobias, Volodymyr M Minin, Chawangwa Modongo, Nicola M Zetola, Stefan Niemann, Sanghyuk S Shin
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Abstract

Background: The integration of genomic and geospatial data into infectious disease transmission analyses typically includes residential locations and excludes other activity spaces where transmission may occur (e.g. work, school, or social venues). The objective of this analysis was to explore residential as well as other activity spaces of tuberculosis (TB) outbreaks to identify potential geospatial 'hotspots' of transmission.

Methods: We analyzed data that included geospatial coordinates for residence and other activity spaces collected during 2012-2016 for the Kopanyo Study, a population-based study of TB transmission in Botswana. We included participants with results from whole genome sequencing conducted on archived samples from the original study. We used a spatial log-Gaussian Cox process model to detect core areas of increased activity spaces of individuals belonging to TB outbreaks (genotypic groups with ≤5 single-nucleotide polymorphisms), which we compared to ungrouped participants (those not in a genotypic group of any size).

Findings: We analyzed data collected from 636 participants, including 70 participants belonging to six outbreak groups with a combined total of 293 locations, and 566 ungrouped participants with a combined total of 2289 locations. Core areas of activity space for each outbreak group were geographically distinct, and we found evidence of localized transmission in four of six outbreaks. For most of the outbreaks, including activity space data led to the detection of larger areas of higher spatial intensity and more focal points compared to residential location alone.

Interpretation: Geospatial analysis using activity space data (social gathering places as well as residence) may lead to improved understanding of areas of infectious disease transmission compared to using residential data alone.

Funding: This work was supported by funding from the National Institute of Allergy and Infectious Diseases R01AI097045, R01AI147336, and R01AI170204.

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利用基因组流行病学和地理活动空间调查博茨瓦纳的结核病爆发。
背景:将基因组学和地理空间数据整合到传染病传播分析中通常包括居住地,而不包括可能发生传播的其他活动场所(如工作、学校或社交场所)。本分析的目的是探索结核病爆发的居住地及其他活动场所,以确定潜在的地理空间传播 "热点":我们分析了 2012-2016 年期间收集的 Kopanyo 研究数据,其中包括居住地和其他活动场所的地理空间坐标,该研究是一项基于人口的博茨瓦纳结核病传播研究。我们纳入了对原始研究的存档样本进行全基因组测序并得出结果的参与者。我们使用空间对数-高斯考克斯过程模型来检测属于结核病爆发(单核苷酸多态性≤5的基因型群体)的个人活动空间增加的核心区域,并将其与未分组参与者(不属于任何规模的基因型群体)进行比较:我们分析了从 636 名参与者收集到的数据,其中 70 名参与者属于 6 个疫情爆发组,总计 293 个地点;566 名未分组参与者,总计 2289 个地点。每个疫情爆发群体的核心活动区域在地理位置上截然不同,我们在六次疫情爆发中的四次发现了局部传播的证据。在大多数疫情中,与仅检测居住地点相比,包含活动空间数据可检测到更大范围、更高空间强度和更多焦点:解释:与仅使用居住地数据相比,使用活动空间数据(社交聚会场所以及居住地)进行地理空间分析可能会加深对传染病传播区域的了解:这项工作得到了美国国家过敏与传染病研究所(National Institute of Allergy and Infectious Diseases)R01AI097045、R01AI147336 和 R01AI170204 的资助。
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