Evidence map›Paper›PMID 37081530›Full record

ArticleEmerging infectious diseases2023

Use of High-Resolution Geospatial and Genomic Data to Characterize Recent Tuberculosis Transmission, Botswana.

Chelsea R Baker, Ivan Barilar, Leonardo S de Araujo, Anne W Rimoin, Daniel M Parker, Rosanna Boyd, James L Tobias, Patrick K Moonan, Eleanor S Click, Alyssa Finlay and 6 more

Abstract read
In one paragraph

Article in Emerging infectious diseases, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Quantifying the community transmission ofEmerging microbes & infections · 2026
    Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. UShER-TB: Scalable, Comprehensive, Accessible Phylogenomic Analysis ofmedRxiv : the preprint server for health sciences · 2025
    Article
  9. Article
  10. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

Chelsea R Baker
Ivan Barilar
Leonardo S de Araujo
Anne W Rimoin
Daniel M Parker
Rosanna Boyd
James L Tobias
Patrick K Moonan
Eleanor S Click
Alyssa Finlay
John E Oeltmann
Vladimir N Minin
Chawangwa Modongo
Nicola M Zetola
Stefan Niemann
Sanghyuk S Shin

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
NIAID NIH HHS R01 AI097045NIAID NIH HHS R01 AI147336PEPFAR
6 · The paper itself

Abstract

Combining genomic and geospatial data can be useful for understanding Mycobacterium tuberculosis transmission in high-burden tuberculosis (TB) settings. We performed whole-genome sequencing on M. tuberculosis DNA extracted from sputum cultures from a population-based TB study conducted in Gaborone, Botswana, during 2012-2016. We determined spatial distribution of cases on the basis of shared genotypes among isolates. We considered clusters of isolates with ≤5 single-nucleotide polymorphisms identified by whole-genome sequencing to indicate recent transmission and clusters of ≥10 persons to be outbreaks. We obtained both molecular and geospatial data for 946/1,449 (65%) participants with culture-confirmed TB; 62 persons belonged to 5 outbreaks of 10-19 persons each. We detected geospatial clustering in just 2 of those 5 outbreaks, suggesting heterogeneous spatial patterns. Our findings indicate that targeted interventions applied in smaller geographic areas of high-burden TB identified using integrated genomic and geospatial data might help interrupt TB transmission during outbreaks.

Indexed as

Mycobacterium tuberculosisTuberculosisBotswanaGenomicsGenotypeHumansbacteriaBotswanageographic heterogeneityinfectious disease controloutbreaksrespiratory infectionsspatial analysistuberculosis and other mycobacteriawhole-genome sequencing

Identifiers

PMID37081530
PMCPMC10124643

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Registered trials

None linked

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.