Literature DB >> 26845663

Reproduction numbers for epidemic models with households and other social structures II: Comparisons and implications for vaccination.

Frank Ball1, Lorenzo Pellis2, Pieter Trapman3.   

Abstract

In this paper we consider epidemic models of directly transmissible SIR (susceptible → infective → recovered) and SEIR (with an additional latent class) infections in fully-susceptible populations with a social structure, consisting either of households or of households and workplaces. We review most reproduction numbers defined in the literature for these models, including the basic reproduction number R0 introduced in the companion paper of this, for which we provide a simpler, more elegant derivation. Extending previous work, we provide a complete overview of the inequalities among these reproduction numbers and resolve some open questions. Special focus is put on the exponential-growth-associated reproduction number Rr, which is loosely defined as the estimate of R0 based on the observed exponential growth of an emerging epidemic obtained when the social structure is ignored. We show that for the vast majority of the models considered in the literature Rr ≥ R0 when R0 ≥ 1 and Rr ≤ R0 when R0 ≤ 1. We show that, in contrast to models without social structure, vaccination of a fraction 1-1/R0 of the population, chosen uniformly at random, with a perfect vaccine is usually insufficient to prevent large epidemics. In addition, we provide significantly sharper bounds than the existing ones for bracketing the critical vaccination coverage between two analytically tractable quantities, which we illustrate by means of extensive numerical examples.
Copyright © 2016 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Basic reproduction number; Exponential growth rate; Household; SIR epidemic; Social structure; Vaccination

Mesh:

Year:  2016        PMID: 26845663     DOI: 10.1016/j.mbs.2016.01.006

Source DB:  PubMed          Journal:  Math Biosci        ISSN: 0025-5564            Impact factor:   2.144


  8 in total

1.  Inferring R0 in emerging epidemics-the effect of common population structure is small.

Authors:  Pieter Trapman; Frank Ball; Jean-Stéphane Dhersin; Viet Chi Tran; Jacco Wallinga; Tom Britton
Journal:  J R Soc Interface       Date:  2016-08       Impact factor: 4.118

2.  Inference of epidemiological parameters from household stratified data.

Authors:  James N Walker; Joshua V Ross; Andrew J Black
Journal:  PLoS One       Date:  2017-10-18       Impact factor: 3.240

3.  SIR epidemics and vaccination on random graphs with clustering.

Authors:  Carolina Fransson; Pieter Trapman
Journal:  J Math Biol       Date:  2019-04-10       Impact factor: 2.259

4.  Key questions for modelling COVID-19 exit strategies.

Authors:  Robin N Thompson; T Déirdre Hollingsworth; Valerie Isham; Daniel Arribas-Bel; Ben Ashby; Tom Britton; Peter Challenor; Lauren H K Chappell; Hannah Clapham; Nik J Cunniffe; A Philip Dawid; Christl A Donnelly; Rosalind M Eggo; Sebastian Funk; Nigel Gilbert; Paul Glendinning; Julia R Gog; William S Hart; Hans Heesterbeek; Thomas House; Matt Keeling; István Z Kiss; Mirjam E Kretzschmar; Alun L Lloyd; Emma S McBryde; James M McCaw; Trevelyan J McKinley; Joel C Miller; Martina Morris; Philip D O'Neill; Kris V Parag; Carl A B Pearson; Lorenzo Pellis; Juliet R C Pulliam; Joshua V Ross; Gianpaolo Scalia Tomba; Bernard W Silverman; Claudio J Struchiner; Michael J Tildesley; Pieter Trapman; Cerian R Webb; Denis Mollison; Olivier Restif
Journal:  Proc Biol Sci       Date:  2020-08-12       Impact factor: 5.349

5.  Household bubbles and COVID-19 transmission: insights from percolation theory.

Authors:  Leon Danon; Lucas Lacasa; Ellen Brooks-Pollock
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2021-05-31       Impact factor: 6.237

6.  Evaluation of vaccination strategies for SIR epidemics on random networks incorporating household structure.

Authors:  Frank Ball; David Sirl
Journal:  J Math Biol       Date:  2017-06-20       Impact factor: 2.259

7.  Using statistics and mathematical modelling to understand infectious disease outbreaks: COVID-19 as an example.

Authors:  Christopher E Overton; Helena B Stage; Shazaad Ahmad; Jacob Curran-Sebastian; Paul Dark; Rajenki Das; Elizabeth Fearon; Timothy Felton; Martyn Fyles; Nick Gent; Ian Hall; Thomas House; Hugo Lewkowicz; Xiaoxi Pang; Lorenzo Pellis; Robert Sawko; Andrew Ustianowski; Bindu Vekaria; Luke Webb
Journal:  Infect Dis Model       Date:  2020-07-04

8.  Systematic selection between age and household structure for models aimed at emerging epidemic predictions.

Authors:  Lorenzo Pellis; Simon Cauchemez; Neil M Ferguson; Christophe Fraser
Journal:  Nat Commun       Date:  2020-02-14       Impact factor: 14.919

  8 in total

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