Literature DB >> 9733654

Quantifying the intrinsic transmission dynamics of tuberculosis.

T C Porco1, S M Blower.   

Abstract

Previously we have formulated transmission models of untreated tuberculosis epidemics (Blower et al., Nature, Medicine 1 (1995), 815-821); in this paper, we present time-dependent uncertainty and sensitivity analyses in order to quantitatively understand the transmission dynamics of tuberculosis epidemics in the absence of treatment. The time-dependent uncertainty analysis enabled us to evaluate the variability in the epidemiological outcome variables of the model during the progression of a tuberculosis epidemic. Calculated values (from the uncertainty analysis) for the disease incidence, disease prevalence, and mortality rates were approximately consistent with historical data. The time-dependent sensitivity analysis revealed that only a few of the model's input parameters significantly affected the severity of a tuberculosis epidemic; these parameters were the disease reactivation rate, the fraction of infected individuals who develop tuberculosis soon after infection, the number of individuals that an infectious individual infects per year, the disease death rate, and the population recruitment rate. Our analysis demonstrates that it is possible to improve our understanding of the behavior of tuberculosis epidemics by applying time-dependent uncertainty and sensitivity analysis to a transmission model. Copyright 1998 Academic Press.

Entities:  

Mesh:

Year:  1998        PMID: 9733654     DOI: 10.1006/tpbi.1998.1366

Source DB:  PubMed          Journal:  Theor Popul Biol        ISSN: 0040-5809            Impact factor:   1.570


  25 in total

1.  The coreceptor mutation CCR5Delta32 influences the dynamics of HIV epidemics and is selected for by HIV.

Authors:  A D Sullivan; J Wigginton; D Kirschner
Journal:  Proc Natl Acad Sci U S A       Date:  2001-08-21       Impact factor: 11.205

2.  Using approximate Bayesian computation to estimate tuberculosis transmission parameters from genotype data.

Authors:  Mark M Tanaka; Andrew R Francis; Fabio Luciani; S A Sisson
Journal:  Genetics       Date:  2006-04-19       Impact factor: 4.562

3.  The 1918 influenza pandemic hastened the decline of tuberculosis in the United States: an age, period, cohort analysis.

Authors:  Andrew Noymer
Journal:  Vaccine       Date:  2011-07-22       Impact factor: 3.641

Review 4.  Sensitivity analysis of infectious disease models: methods, advances and their application.

Authors:  Jianyong Wu; Radhika Dhingra; Manoj Gambhir; Justin V Remais
Journal:  J R Soc Interface       Date:  2013-07-17       Impact factor: 4.118

5.  Modeling the joint epidemics of TB and HIV in a South African township.

Authors:  Nicolas Bacaër; Rachid Ouifki; Carel Pretorius; Robin Wood; Brian Williams
Journal:  J Math Biol       Date:  2008-04-15       Impact factor: 2.259

6.  Inferring epidemiological parameters on the basis of allele frequencies.

Authors:  Tanja Stadler
Journal:  Genetics       Date:  2011-05-05       Impact factor: 4.562

Review 7.  Epidemiological models of Mycobacterium tuberculosis complex infections.

Authors:  Cagri Ozcaglar; Amina Shabbeer; Scott L Vandenberg; Bülent Yener; Kristin P Bennett
Journal:  Math Biosci       Date:  2012-03-01       Impact factor: 2.144

8.  Local epidemic history as a predictor of tuberculosis incidence in Saskatchewan Aboriginal communities.

Authors:  C Pepperell; A H Chang; W Wobeser; J Parsonnet; V H Hoeppner
Journal:  Int J Tuberc Lung Dis       Date:  2011-07       Impact factor: 2.373

9.  Optimal timing of drug sensitivity testing for patients on first-line tuberculosis treatment.

Authors:  Sze-Chuan Suen; Margaret L Brandeau; Jeremy D Goldhaber-Fiebert
Journal:  Health Care Manag Sci       Date:  2017-08-31

10.  A dynamic transmission model for predicting trends in Helicobacter pylori and associated diseases in the United States.

Authors:  M F Rupnow; R D Shachter; D K Owens; J Parsonnet
Journal:  Emerg Infect Dis       Date:  2000 May-Jun       Impact factor: 6.883

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.