Evidence map›Paper›PMID 41933361›Full record

ArticleInternational journal of health geographics2026

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 and 3 more

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In one paragraph

Article in International journal of health geographics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

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No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

13 authors.

Chelsea R BakerSue & Bill Gross School of Nursing, University of California Irvine, 854 Medical Sciences Quad, Irvine, CA, 92697, USA.
Ivan BarilarMolecular Mycobacteriology, Forschungszentrum Borstel, Borstel, Germany.
Leonardo S de AraujoMolecular Mycobacteriology, Forschungszentrum Borstel, Borstel, Germany.
Daniel M ParkerPopulation Health & Disease Prevention, University of California Irvine, CA, Irvine, USA.
Kimberly FornaceNational University of Singapore, Singapore, Singapore.
Patrick K MoonanUS Centers for Disease Control and Prevention, Atlanta, GA, USA.
John E OeltmannUS Centers for Disease Control and Prevention, Atlanta, GA, USA.
James L TobiasPeraton, 875 Explorer Street, VA, Reston, USA.
Volodymyr M MininStatistics, University of California Irvine, Irvine, USA.
Chawangwa ModongoVictus Global Botswana Organisation, Gaborone, Botswana.
Nicola M Zetola *Augusta University School of Medicine, GA, Augusta, USA.
Stefan Niemann *Molecular Mycobacteriology, Forschungszentrum Borstel, Borstel, Germany.
Sanghyuk S Shin *Sue & Bill Gross School of Nursing, University of California Irvine, 854 Medical Sciences Quad, Irvine, CA, 92697, USA. ssshin2@uci.edu.

Funding

Improved understanding of TB transmission by accounting for within-host heterogeneity of M. tuberculosis: A population-based molecular epidemiology study in a high HIV prevalent settingR01AI147336 · NIAID · UNIVERSITY OF CALIFORNIA-IRVINE · PI SHIN, SANGHYUK SAM · 2020 to 2024
$3.3M
Dynamics of TB and MDR TB transmission in areas with high HIV prevalenceR01AI097045 · NIAID · UNIVERSITY OF PENNSYLVANIA · PI ZETOLA, NICOLA MARIO · 2011 to 2015
$2.9M
The effect of HIV on SARS-CoV-2 transmission and emergence of variants of concernR01AI170204 · NIAID · UNIVERSITY OF CALIFORNIA-IRVINE · PI MODONGO, CHAWANGWA, NIEMANN, STEFAN · 2022 to 2025
$2.4M
NIAID NIH HHS R01 AI097045NIAID NIH HHS R01 AI147336NIAID NIH HHS R01 AI170204
6 · The paper itself

Abstract

objectivesOur 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.

methodsWe 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).

resultsWe 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.

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

Indexed as

Disease OutbreaksGenomicsMycobacterium tuberculosisTuberculosisBotswanaFemaleHumansMalePolymorphism, Single NucleotideActivity spaceGeographic heterogeneityOutbreakSpatial analysisTuberculosis transmissionWhole genome sequencing

Identifiers

PMID41933361
PMCPMC13173891

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.