Evidence map›Paper›PMID 39677427›Full record

ArticlemedRxiv : the preprint server for health sciences2024

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

Abstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

13 authors.

Chelsea R BakerUniversity of California, Irvine, California, USA.ORCID 0000-0001-5501-677X
Ivan BarilarForschungszentrum, Borstel, Germany.
Leonardo S de AraujoForschungszentrum, Borstel, Germany.
Daniel M ParkerUniversity of California, Irvine, California, USA.
Kimberly FornaceNational University of Singapore.
Patrick K MoonanUS Centers for Disease Control and Prevention, Atlanta, Georgia, USA.
John E OeltmannUS Centers for Disease Control and Prevention, Atlanta, Georgia, USA.
James L TobiasUS Centers for Disease Control and Prevention, Atlanta, Georgia, USA.
Volodymyr M MininUniversity of California, Irvine, California, USA.
Chawangwa ModongoBotswana-Upenn Partnership, Gaborone, Botswana/ Victus Global Botswana Organisation, Gaborone, Botswana.
Nicola M ZetolaBotswana-Upenn Partnership, Gaborone, Botswana/ Victus Global Botswana Organisation, Gaborone, Botswana.
Stefan NiemannForschungszentrum, Borstel, Germany.
Sanghyuk S ShinUniversity of California, Irvine, California, USA.

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

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 ( 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.

Indexed as

activity spacegeographic heterogeneityinfectious disease controloutbreakspatial analysisTuberculosis transmissionwhole genome sequencing

Identifiers

PMID39677427
PMCPMC11643207

What OpenQuestion holds

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LicenceCC BY-ND
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Registered trials

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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.