Baker Chelsea R, Barilar Ivan, de Araujo Leonardo S, Parker Daniel M, Fornace Kimberly, Moonan Patrick K, Oeltmann John E, Tobias James L, Minin Volodymyr M, Modongo Chawangwa, Zetola Nicola M, Niemann Stefan, Shin Sanghyuk S
University of California, Irvine, California, USA.
Forschungszentrum, Borstel, Germany.
medRxiv. 2024 Dec 6:2024.12.04.24318520. doi: 10.1101/2024.12.04.24318520.
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 ( 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.
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).
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.
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.
This work was supported by funding from the National Institute of Allergy and Infectious Diseases R01AI097045, R01AI147336, and R01AI170204.
将基因组数据和地理空间数据整合到传染病传播分析中,通常包括居住地点,而排除了其他可能发生传播的活动空间(工作场所、学校或社交场所)。本分析的目的是探索结核病(TB)疫情的居住及其他活动空间,以识别潜在的地理空间传播“热点”。
我们分析了2012年至2016年期间为科帕尼奥研究收集的数据,该研究是一项基于人群的博茨瓦纳结核病传播研究,数据包括居住和其他活动空间的地理空间坐标。我们纳入了对原始研究存档样本进行全基因组测序的参与者。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疫情(单核苷酸多态性≤5的基因型组)的个体活动空间增加的核心区域,并将其与未分组的参与者(不属于任何规模基因型组的参与者)进行比较。
我们分析了从636名参与者收集的数据,其中包括属于六个疫情组的70名参与者,共有293个地点,以及566名未分组的参与者,共有2289个地点。每个疫情组的活动空间核心区域在地理上是不同的,并且我们在六个疫情中的四个中发现了局部传播的证据。对于大多数疫情,与仅使用居住地点数据相比,纳入活动空间数据导致检测到更大的高空间强度区域和更多的焦点。
与仅使用居住数据相比,使用活动空间数据(社交聚集场所和居住场所)进行地理空间分析可能会更好地理解传染病传播区域。
这项工作得到了美国国立过敏与传染病研究所R01AI097045、R01AI147336和R01AI170204的资助。