Literature DB >> 32458995

Using Cure Models to Estimate the Serial Interval of Tuberculosis With Limited Follow-up.

Yicheng Ma, Helen E Jenkins, Paola Sebastiani, Jerrold J Ellner, Edward C Jones-López, Reynaldo Dietze, Charles R Horsburgh, Laura F White.   

Abstract

Serial interval (SI), defined as the time between symptom onset in an infector and infectee pair, is commonly used to understand infectious diseases transmission. Slow progression to active disease, as well as the small percentage of individuals who will eventually develop active disease, complicate the estimation of the SI for tuberculosis (TB). In this paper, we showed via simulation studies that when there is credible information on the percentage of those who will develop TB disease following infection, a cure model, first introduced by Boag in 1949, should be used to estimate the SI for TB. This model includes a parameter in the likelihood function to account for the study population being composed of those who will have the event of interest and those who will never have the event. We estimated the SI for TB to be approximately 0.5 years for the United States and Canada (January 2002 to December 2006) and approximately 2.0 years for Brazil (March 2008 to June 2012), which might imply a higher occurrence of reinfection TB in a developing country like Brazil.
© The Author(s) 2020. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Entities:  

Keywords:  cure models; right censoring; serial interval; tuberculosis

Mesh:

Year:  2020        PMID: 32458995      PMCID: PMC7731991          DOI: 10.1093/aje/kwaa090

Source DB:  PubMed          Journal:  Am J Epidemiol        ISSN: 0002-9262            Impact factor:   4.897


  26 in total

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8.  A likelihood-based method for real-time estimation of the serial interval and reproductive number of an epidemic.

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10.  Pandemic potential of a strain of influenza A (H1N1): early findings.

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Journal:  Science       Date:  2009-05-11       Impact factor: 47.728

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  3 in total

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Authors:  Sarah V Leavitt; Helen E Jenkins; Paola Sebastiani; Robyn S Lee; C Robert Horsburgh; Andrew M Tibbs; Laura F White
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2.  Estimation of local time-varying reproduction numbers in noisy surveillance data.

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3.  Estimation of local time-varying reproduction numbers in noisy surveillance data.

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  3 in total

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