Literature DB >> 19306886

Heterogeneity in susceptibility to infection can explain high reinfection rates.

Paula Rodrigues1, Alessandro Margheri, Carlota Rebelo, M Gabriela M Gomes.   

Abstract

Heterogeneity in susceptibility and infectivity is inherent to infectious disease transmission in nature. Here we are concerned with the formulation of mathematical models that capture the essence of heterogeneity while keeping a simple structure suitable of analytical treatment. We explore the consequences of host heterogeneity in the susceptibility to infection for epidemiological models for which immunity conferred by infection is partially protective, known as susceptible-infected-recovered-infected (SIRI) models. We analyze the impact of heterogeneity on disease prevalence and contrast the susceptibility profiles of the subpopulations at risk for primary infection and reinfection. We present a systematic study in the case of two frailty groups. We predict that the average rate of reinfection may be higher than the average rate of primary infection, which may seem paradoxical given that primary infection induces life-long partial protection. Infection generates a selection mechanism whereby fit individuals remain in S and frail individuals are transferred to R. If this effect is strong enough we have a scenario where, on average, the rate of reinfection is higher than the rate of primary infection even though each individual has a risk reduction following primary infection. This mechanism may explain high rates of tuberculosis reinfection recently reported. Finally, the enhanced benefits of vaccination strategies that target the high-risk groups are quantified.

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Year:  2009        PMID: 19306886     DOI: 10.1016/j.jtbi.2009.03.013

Source DB:  PubMed          Journal:  J Theor Biol        ISSN: 0022-5193            Impact factor:   2.691


  13 in total

1.  How host heterogeneity governs tuberculosis reinfection?

Authors:  M Gabriela M Gomes; Ricardo Aguas; João S Lopes; Marta C Nunes; Carlota Rebelo; Paula Rodrigues; Claudio J Struchiner
Journal:  Proc Biol Sci       Date:  2012-02-22       Impact factor: 5.349

2.  The size of epidemics in populations with heterogeneous susceptibility.

Authors:  Guy Katriel
Journal:  J Math Biol       Date:  2011-08-10       Impact factor: 2.259

3.  On the correlation between variance in individual susceptibilities and infection prevalence in populations.

Authors:  Alessandro Margheri; Carlota Rebelo; M Gabriela M Gomes
Journal:  J Math Biol       Date:  2015-03-22       Impact factor: 2.259

4.  Set-membership estimations for the evolution of infectious diseases in heterogeneous populations.

Authors:  Tsvetomir Tsachev; Vladimir M Veliov; Andreas Widder
Journal:  J Math Biol       Date:  2016-09-07       Impact factor: 2.259

5.  Traveling wave solutions in a two-group SIR epidemic model with constant recruitment.

Authors:  Lin Zhao; Zhi-Cheng Wang; Shigui Ruan
Journal:  J Math Biol       Date:  2018-03-21       Impact factor: 2.259

6.  Mixed Mycobacterium tuberculosis-Strain Infections Are Associated With Poor Treatment Outcomes Among Patients With Newly Diagnosed Tuberculosis, Independent of Pretreatment Heteroresistance.

Authors:  Sanghyuk S Shin; Chawangwa Modongo; Yeonsoo Baik; Christopher Allender; Darrin Lemmer; Rebecca E Colman; David M Engelthaler; Robin M Warren; Nicola M Zetola
Journal:  J Infect Dis       Date:  2018-11-05       Impact factor: 5.226

7.  Large epidemic thresholds emerge in heterogeneous networks of heterogeneous nodes.

Authors:  Hui Yang; Ming Tang; Thilo Gross
Journal:  Sci Rep       Date:  2015-08-21       Impact factor: 4.379

8.  The Risk of Tuberculosis Reinfection Soon after Cure of a First Disease Episode Is Extremely High in a Hyperendemic Community.

Authors:  Pieter Uys; Hilmarie Brand; Robin Warren; Gian van der Spuy; Eileen G Hoal; Paul D van Helden
Journal:  PLoS One       Date:  2015-12-09       Impact factor: 3.240

9.  Addressing population heterogeneity and distribution in epidemics models using a cellular automata approach.

Authors:  Leonardo López; Germán Burguerner; Leonardo Giovanini
Journal:  BMC Res Notes       Date:  2014-04-12

Review 10.  Systematic review of mathematical models exploring the epidemiological impact of future TB vaccines.

Authors:  Rebecca C Harris; Tom Sumner; Gwenan M Knight; Richard G White
Journal:  Hum Vaccin Immunother       Date:  2016-07-22       Impact factor: 3.452

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