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Demographic Control Measure Implications of Tuberculosis Infection for Migrant Workers across Taiwan Regions

Bibliographic Data

ID15512510
AuthorsSzu‐Chieh Chen (0000-0002-3998-0355, Chung Shan Medical University Hospital), Tzu‐Yun Wang (0000-0002-0561-9967, Chung Shan Medical University), Hsin-Chieh Tsai (Chung Shan Medical University), Chi‐Yun Chen (0000-0002-5536-857X, National Taiwan University), Tien‐Hsuan Lu (0000-0001-7007-1371, Dayeh University), Yi-Jun Lin (National Yang Ming Chiao Tung University), Shu‐Han You (0000-0003-4440-3138, National Taiwan Ocean University), Ying‐Fei Yang (0000-0003-0304-1628, National Taiwan University), Chung‐Min Liao (0000-0002-8360-7996, National Taiwan University, corresponding author)
Year2022
Volume19
Issue16
Pages9899-9899
Publication date2022-08-11
Open AccessYes
TypeARTICLE
VenueInternational Journal of Environmental Research and Public Health (JOURNAL)
Journal identifiersISSN: 1661-7827 • E-ISSN: 1660-4601
PublisherMultidisciplinary Digital Publishing Institute (PUBLISHER • CH)
DOI10.3390/ijerph19169899
PMID36011542
OpenAlexW4290999845
LanguageEN
References cited6

A sharp increase in migrant workers has raised concerns for TB epidemics, yet optimal TB control strategies remain unclear in Taiwan regions. This study assessed intervention efforts on reducing tuberculosis (TB) infection among migrant workers. We performed large-scale data analyses and used them to develop a control-based migrant worker-associated susceptible-latently infected-infectious-recovered (SLTR) model. We used the SLTR model to assess potential intervention strategies such as social distancing, early screening, and directly observed treatment, short-course (DOTS) for TB transmission among migrant workers and locals in three major hotspot cities from 2018 to 2023. We showed that social distancing was the best single strategy, while the best dual measure was social distancing coupled with early screening. However, the effectiveness of the triple strategy was marginally (1-3%) better than that of the dual measure. Our study provides a mechanistic framework to facilitate understanding of TB transmission dynamics between locals and migrant workers and to recommend better prevention strategies in anticipation of achieving WHO's milestones by the next decade. Our work has implications for migrant worker-associated TB infection prevention on a global scale and provides a knowledge base for exploring how outcomes can be best implemented by alternative control measure approaches

Coronavirus disease 2019 (COVID-19 · Disease · Economic growth · Economics · Environmental health · Geography · Infection control · Infectious disease (medical specialty · Migrant workers · Scale (ratio · Social distance · Transmission (telecommunications · Tuberculosis · COVID-19 epidemiological studies · Medicine · Pneumonia and Respiratory Infections · Tuberculosis Research and Epidemiology

  • Infectious Diseases of Humans

    Roxanne Marie Anderson, Roy M Anderson et al.•Infectious diseases of humans•1991

  • The Global Burden of Latent Tuberculosis Infection

    Open Access•Rein Mgj Houben, Rein M G J Houben et al.•PLoS Medicine•2016

  • Tuberculosis among migrant workers in Taiwan

    Open Access•Chia-Wen Lu, Yi-Hsuan Lee et al.•Globalization and Health•2019

  • The Use of Mathematical Models in the Study of the Epidemiology of Tuberculosis

    Hans Waaler, Hans Th Waaler et al.•American Journal of Public Health…•1962

  • Enhanced surveillance for tuberculosis among foreign-born persons, Finland, 2014–2016

    Open Access•Pirre E Räisänen, Hanna Soini et al.•BMC Public Health•2018

  • Nationwide surveillance algorithms for tuberculosis among immigrant workers from highly endemic countries following pre-entry screening in Taiwan

    Open Access•Mei-Mei Kuan•BMC Public Health•2018

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