Literature DB >> 10942787

An intuitive formulation for the reproductive number for the spread of diseases in heterogeneous populations.

J M Hyman1, J Li.   

Abstract

The thresholds for mathematical epidemiology models specify the critical conditions for an epidemic to grow or die out. The reproductive number can provide significant insight into the transmission dynamics of a disease and can guide strategies to control its spread. We define the mean number of contacts, the mean duration of infection, and the mean transmission probability appropriately for certain epidemiological models, and construct a simplified formulation of the reproductive number as the product of these quantities. When the spread of the epidemic depends strongly upon the heterogeneity of the populations, the epidemiological models must account for this heterogeneity, and the expressions for the reproductive number become correspondingly more complex. We formulate several models with different heterogeneous structures and demonstrate how to define the mean quantities for an explicit expression for the reproductive number. In complex heterogeneous models, it seems necessary to define the reproductive number for each structured subgroup or cohort and then use the average of these reproductive numbers weighted by their heterogeneity to estimate the reproductive number for the total population.

Entities:  

Mesh:

Year:  2000        PMID: 10942787     DOI: 10.1016/s0025-5564(00)00025-0

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


  14 in total

Review 1.  Perspectives on the basic reproductive ratio.

Authors:  J M Heffernan; R J Smith; L M Wahl
Journal:  J R Soc Interface       Date:  2005-09-22       Impact factor: 4.118

2.  How do pathogen evolution and host heterogeneity interact in disease emergence?

Authors:  Andrew Yates; Rustom Antia; Roland R Regoes
Journal:  Proc Biol Sci       Date:  2006-12-22       Impact factor: 5.349

3.  Determinants of cluster distribution in the molecular epidemiology of tuberculosis.

Authors:  Megan Murray
Journal:  Proc Natl Acad Sci U S A       Date:  2002-01-29       Impact factor: 11.205

4.  Dynamics of hybrid switching DS-I-A epidemic model.

Authors:  Songnan Liu; Daqing Jiang; Xiaojie Xu; Tasawar Hayat; Bashir Ahmad
Journal:  Sci Rep       Date:  2017-09-26       Impact factor: 4.379

5.  Modelling vertical transmission in vector-borne diseases with applications to Rift Valley fever.

Authors:  Nakul Chitnis; James M Hyman; Carrie A Manore
Journal:  J Biol Dyn       Date:  2013       Impact factor: 2.179

6.  Differential susceptibility epidemic models.

Authors:  James M Hyman; Jia Li
Journal:  J Math Biol       Date:  2004-12-20       Impact factor: 2.164

7.  Role of genetic heterogeneity in determining the epidemiological severity of H1N1 influenza.

Authors:  Narmada Sambaturu; Sumanta Mukherjee; Martín López-García; Carmen Molina-París; Gautam I Menon; Nagasuma Chandra
Journal:  PLoS Comput Biol       Date:  2018-03-21       Impact factor: 4.475

8.  Modelling the epidemiology of Escherichia coli ST131 and the impact of interventions on the community and healthcare centres.

Authors:  A Talaminos; L López-Cerero; J Calvillo; A Pascual; L M Roa; J Rodríguez-Baño
Journal:  Epidemiol Infect       Date:  2016-02-03       Impact factor: 4.434

9.  Estimation of the HIV basic reproduction number in rural south west Uganda: 1991-2008.

Authors:  Rebecca N Nsubuga; Richard G White; Billy N Mayanja; Leigh Anne Shafer
Journal:  PLoS One       Date:  2014-01-03       Impact factor: 3.240

10.  Dynamics of a Tularemia Outbreak in a Closely Monitored Free-Roaming Population of Wild House Mice.

Authors:  Akos Dobay; Paola Pilo; Anna K Lindholm; Francesco Origgi; Homayoun C Bagheri; Barbara König
Journal:  PLoS One       Date:  2015-11-04       Impact factor: 3.240

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.