TY - JOUR
T1 - A theoretical framework to identify invariant thresholds in infectious disease epidemiology
AU - Gomes, M. Gabriela M.
AU - Gjini, Erida
AU - Lopes, Joao S.
AU - Souto-Maior, Caetano
AU - Rebelo, Carlota
N1 - Funding Information:
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 .
Publisher Copyright:
© 2016 Elsevier Ltd.
Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2016/4/21
Y1 - 2016/4/21
N2 - 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.
AB - 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.
KW - Endemic infection
KW - Epidemic threshold
KW - Global health
KW - Heterogeneity
KW - Reinfection threshold
UR - http://www.scopus.com/inward/record.url?scp=84958259435&partnerID=8YFLogxK
U2 - 10.1016/j.jtbi.2016.01.029
DO - 10.1016/j.jtbi.2016.01.029
M3 - Article
C2 - 26869215
AN - SCOPUS:84958259435
SN - 0022-5193
VL - 395
SP - 97
EP - 102
JO - Journal of Theoretical Biology
JF - Journal of Theoretical Biology
ER -