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Assessing inference of the basic reproduction number in an SIR model incorporating a growth‐scaling parameter
Author(s) -
Ganyani Tapiwa,
Faes Christel,
Chowell Gerardo,
Hens Niel
Publication year - 2018
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.7935
Subject(s) - basic reproduction number , scaling , inference , nonlinear system , action (physics) , computer science , transmission (telecommunications) , epidemic model , mathematics , scaling law , constant (computer programming) , statistical physics , mathematical optimization , artificial intelligence , population , physics , geometry , telecommunications , demography , quantum mechanics , sociology , programming language
The standard mass action, which assumes that infectious disease transmission occurs in well‐mixed populations, is popular for formulating compartmental epidemic models. Compartmental epidemic models often follow standard mass action for simplicity and to gain insight into transmission dynamics as it often performs well at reproducing disease dynamics in large populations. In this work, we formulate discrete time stochastic susceptible‐infected‐removed models with linear (standard) and nonlinear mass action structures to mimic varying mixing levels. Using simulations and real epidemic data, we demonstrate the sensitivity of the basic reproduction number to these mathematical structures of the force of infection. Our results suggest the need to consider nonlinear mass action in order to generate more accurate estimates of the basic reproduction number although its uncertainty increases due to the addition of one growth scaling parameter.