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A Probabilistic Transmission and Population Dynamic Model to Assess Tuberculosis Infection Risk

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  • Chung‐Min Liao
  • Yi‐Hsien Cheng
  • Yi‐Jun Lin
  • Nan‐Hung Hsieh
  • Tang‐Luen Huang
  • Chia‐Pin Chio
  • Szu‐Chieh Chen
  • Min‐Pei Ling

Abstract

The purpose of this study was to examine tuberculosis (TB) population dynamics and to assess potential infection risk in Taiwan. A well‐established mathematical model of TB transmission built on previous models was adopted to study the potential impact of TB transmission. A probabilistic risk model was also developed to estimate site‐specific risks of developing disease soon after recent primary infection, exogenous reinfection, or through endogenous reactivation (latently infected TB) among Taiwan regions. Here, we showed that the proportion of endogenous reactivation (53–67%) was larger than that of exogenous reinfection (32–47%). Our simulations showed that as epidemic reaches a steady state, age distribution of cases would finally shift toward older age groups dominated by latently infected TB cases as a result of endogenous reactivation. A comparison of age‐weighted TB incidence data with our model simulation output with 95% credible intervals revealed that the predictions were in an apparent agreement with observed data. The median value of overall basic reproduction number (R0) in eastern Taiwan ranged from 1.65 to 1.72, whereas northern Taiwan had the lowest R0 estimate of 1.50. We found that total TB incidences in eastern Taiwan had 25–27% probabilities of total proportion of infected population exceeding 90%, whereas there were 36–66% probabilities having exceeded 20% of total proportion of infected population attributed to latently infected TB. We suggested that our Taiwan‐based analysis can be extended to the context of developing countries, where TB remains a substantial cause of elderly morbidity and mortality.

Suggested Citation

  • Chung‐Min Liao & Yi‐Hsien Cheng & Yi‐Jun Lin & Nan‐Hung Hsieh & Tang‐Luen Huang & Chia‐Pin Chio & Szu‐Chieh Chen & Min‐Pei Ling, 2012. "A Probabilistic Transmission and Population Dynamic Model to Assess Tuberculosis Infection Risk," Risk Analysis, John Wiley & Sons, vol. 32(8), pages 1420-1432, August.
  • Handle: RePEc:wly:riskan:v:32:y:2012:i:8:p:1420-1432
    DOI: 10.1111/j.1539-6924.2011.01750.x
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    References listed on IDEAS

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    1. Ellen Brooks-Pollock & Ted Cohen & Megan Murray, 2010. "The Impact of Realistic Age Structure in Simple Models of Tuberculosis Transmission," PLOS ONE, Public Library of Science, vol. 5(1), pages 1-6, January.
    2. Carol Y. Lin, 2008. "Modeling Infectious Diseases in Humans and Animals by KEELING, M. J. and ROHANI, P," Biometrics, The International Biometric Society, vol. 64(3), pages 993-993, September.
    3. Neil M. Ferguson & Matt J. Keeling & W. John Edmunds & Raymond Gani & Bryan T. Grenfell & Roy M. Anderson & Steve Leach, 2003. "Planning for smallpox outbreaks," Nature, Nature, vol. 425(6959), pages 681-685, October.
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    1. Li, Tao & Rong, Lili & Zhang, Anming, 2021. "Assessing regional risk of COVID-19 infection from Wuhan via high-speed rail," Transport Policy, Elsevier, vol. 106(C), pages 226-238.

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