A theoretical framework to identify invariant thresholds in infectious disease epidemiology

M. Gabriela M. Gomes*, Erida Gjini, Joao S. Lopes, Caetano Souto-Maior, Carlota Rebelo

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

Setting global strategies and targets for disease prevention and control often involves mathematical models. Model structure is typically subject to intense scrutiny, such as confrontation with empirical data and alternative formulations, while a less frequently challenged aspect is the widely adopted reduction of parameters to their average values. Focusing on endemic diseases, we use a general transmission model to explain how mean field approximations decrease the estimated R0 from prevalence data, while threshold phenomena - such as the epidemic and reinfection thresholds - remain invariant. This results in an underestimation of the effort required to control disease, which may be particularly severe when the approximation inappropriately places transmission estimates below important thresholds. These concepts are widely applicable across endemic pathogen systems.

Original languageEnglish
Pages (from-to)97-102
Number of pages6
JournalJournal of Theoretical Biology
Volume395
Early online date8 Feb 2016
DOIs
Publication statusPublished - 21 Apr 2016

Funding

We thank Professor Antonio Coutinho for constructively challenging the practical usage of earlier versions, urging us to aim for finer concepts. We also thank two anonymous reviewers for constructive comments. MGMG is grateful to Instituto Gulbenkian de Ciência for hosting and to FCT ( IF/01346/2014 ) and CAPES (Science without Borders) for funding. CR is supported by Fundação para a Ciência e Tecnologia , UID/MAT/04561/2013 .

Keywords

  • endemic infection
  • epidemic threshold
  • global health
  • heterogeneity
  • reinfection threshold

Fingerprint

Dive into the research topics of 'A theoretical framework to identify invariant thresholds in infectious disease epidemiology'. Together they form a unique fingerprint.

Cite this