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Objectives <p>Our objective was to identify potential ‘hotspots’ of tuberculosis (TB) transmission by integrating pathogen genomic and geospatial data, including activity spaces where transmission may occur (e.g. community locations such as work, school, or social venues) in addition to residential locations.</p> Methods <p>We analyzed geographic data 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 activity spaces belonging to individuals in TB outbreaks (genotypic groups with ≤ 5 single-nucleotide polymorphisms), which we compared to ungrouped participants (those not in a genotypic group of any size).</p> Results <p>We analyzed data for 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 (‘hotspots’) for each outbreak group were geographically distinct, and we found evidence of localized transmission in four of six outbreaks. Hotspots had different geographic characteristics when analyzing activity spaces compared to residential locations alone.</p> Conclusions <p>The incorporation of activity spaces in geospatial modeling of genomic data may improve the characterization of potential transmission hotspots.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月3日

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  • 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

摘要

Objectives

Our objective was to identify potential ‘hotspots’ of tuberculosis (TB) transmission by integrating pathogen genomic and geospatial data, including activity spaces where transmission may occur (e.g. community locations such as work, school, or social venues) in addition to residential locations.

Methods

We analyzed geographic data 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 activity spaces belonging to individuals in TB outbreaks (genotypic groups with ≤ 5 single-nucleotide polymorphisms), which we compared to ungrouped participants (those not in a genotypic group of any size).

Results

We analyzed data for 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 (‘hotspots’) for each outbreak group were geographically distinct, and we found evidence of localized transmission in four of six outbreaks. Hotspots had different geographic characteristics when analyzing activity spaces compared to residential locations alone.

Conclusions

The incorporation of activity spaces in geospatial modeling of genomic data may improve the characterization of potential transmission hotspots.