Literature DB >> 18288789

Growth rates in epidemic models: application to a model for HIV/AIDS progression.

Jon Michael Gran1, Linn Wasmuth, Ellen J Amundsen, Bo H Lindqvist, Odd O Aalen.   

Abstract

The most common quantity used to describe the growth of an epidemic when modelling infectious diseases is the basic reproduction number R0. While R0 is most appropriate for epidemics with short-lasting infections, long-lasting infections such as HIV/AIDS may call for the use of growth rates with other properties. For a group of multi-state compartment models we define both R0, the actual reproduction number Ra(t), and the intrinsic growth rate r. We study the relationship between these different reproduction numbers and growth rates and take a brief look at how they could be estimated from actual observed data. The work is illustrated by a model for HIV/AIDS progression among homosexual men in England and Wales. We conclude that other measures of growth, in addition to R0, give important supplementary information.

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Year:  2008        PMID: 18288789     DOI: 10.1002/sim.3219

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.373


  11 in total

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4.  Prospects of elimination of HIV with test-and-treat strategy.

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Review 5.  The failure of R0.

Authors:  Jing Li; Daniel Blakeley; Robert J Smith
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6.  Growth patterns and scaling laws governing AIDS epidemic in Brazilian cities.

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Review 7.  Toward Standardizing a Lexicon of Infectious Disease Modeling Terms.

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Journal:  Front Public Health       Date:  2016-09-28

8.  Measles Cluster Detection Using Ordinal Scan Statistic Model.

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Journal:  Mater Sociomed       Date:  2018-12

9.  Can we spend our way out of the AIDS epidemic? A world halting AIDS model.

Authors:  Robert J Smith; Jing Li; Richard Gordon; Jane M Heffernan
Journal:  BMC Public Health       Date:  2009-11-18       Impact factor: 3.295

10.  Modelling HIV/AIDS epidemic among men who have sex with men in China.

Authors:  Xiaodan Sun; Yanni Xiao; Zhihang Peng; Ning Wang
Journal:  Biomed Res Int       Date:  2013-09-30       Impact factor: 3.411

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