Investigating extradomiciliary transmission of tuberculosis: An exploratory approach using social network patterns of TB cases and controls and the genotyping of Mycobacterium tuberculosis

Extradomiciliary contacts have been overlooked in the study of TB transmission due to difficulties in identifying actual contacts in large populations. Complex network analysis provides a framework to model the structure of contacts, specially extradomiciliary ones. We conducted a study of incident...

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Bibliographic Details
Published in:	Tuberculosis (Edinburgh, Scotland) Vol. 125; p. 102010
Main Authors:	Pinho, Suani T.R., Pereira, Susan M., Miranda, José G.V., Duarte, Tonya A., Nery, Joilda S., de Oliveira, Maeli G., Freitas, M. Yana G.S., De Almeida, Naila A., Moreira, Fabio B., Gomes, Raoni B.C., Kerr, Ligia, Kendall, Carl, Gomes, M. Gabriela M., Bessa, Theolis C.B., Andrade, Roberto F.S., Barreto, Mauricio L.
Format:	Journal Article
Language:	English
Published:	Scotland Elsevier Ltd 01-12-2020 Elsevier Science Ltd
Subjects:	Data collection Disease transmission Genotyping Infection Information processing Molecular epidemiology Mycobacterium tuberculosis Network analysis Social factors Social interactions Social networks Social organization Sputum Tuberculosis Infection Molecular epidemiology Social networks Tuberculosis
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Summary:	Extradomiciliary contacts have been overlooked in the study of TB transmission due to difficulties in identifying actual contacts in large populations. Complex network analysis provides a framework to model the structure of contacts, specially extradomiciliary ones. We conducted a study of incident sputum-positive TB cases and healthy controls occurring in a moderate TB burden city. Cases and controls were interviewed to obtain data regarding the usual locations of residence, work, study, and leisure. Mycobacterium tuberculosis isolated from sputum was genotyped. The collected data were used to build networks based on a framework of putative social interactions indicating possible TB transmission. A user-friendly open source environment (GraphTube) was setup to extract information from the collected data. Networks based on the likelihood of patient-patient, patient-healthy, and healthy-healthy contacts were setup, depending on a constraint of geographical distance of places attended by the volunteers. Using a threshold for the geographical distance of 300 m, the differences between TB cases and controls are revealed. Several clusters formed by social network nodes with high genotypic similarity were characterized. The developed framework provided consistent results and can be used to support the targeted search of potentially infected individuals and to help to understand the TB transmission.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	1472-9792 1873-281X
DOI:	10.1016/j.tube.2020.102010