SrasV1, Main, Exploration, bibRecord, 003566

Theory versus Data: How to Calculate R0?

Identifieur interne : 003566 ( Main/Exploration ); précédent : 003565; suivant : 003567

Theory versus Data: How to Calculate R0?

Auteurs : Romulus Breban ; Raffaele Vardavas ; Sally Blower

Source :

PLoS ONE [ 1932-6203 ] ; 2007.

RBID : PMC:1804098

Abstract

To predict the potential severity of outbreaks of infectious diseases such as SARS, HIV, TB and smallpox, a summary parameter, the basic reproduction number R₀, is generally calculated from a population-level model. R₀ specifies the average number of secondary infections caused by one infected individual during his/her entire infectious period at the start of an outbreak. R₀ is used to assess the severity of the outbreak, as well as the strength of the medical and/or behavioral interventions necessary for control. Conventionally, it is assumed that if R₀>1 the outbreak generates an epidemic, and if R₀<1 the outbreak becomes extinct. Here, we use computational and analytical methods to calculate the average number of secondary infections and to show that it does not necessarily represent an epidemic threshold parameter (as it has been generally assumed). Previously we have constructed a new type of individual-level model (ILM) and linked it with a population-level model. Our ILM generates the same temporal incidence and prevalence patterns as the population-level model; we use our ILM to directly calculate the average number of secondary infections (i.e., R₀). Surprisingly, we find that this value of R₀ calculated from the ILM is very different from the epidemic threshold calculated from the population-level model. This occurs because many different individual-level processes can generate the same incidence and prevalence patterns. We show that obtaining R₀ from empirical contact tracing data collected by epidemiologists and using this R₀ as a threshold parameter for a population-level model could produce extremely misleading estimates of the infectiousness of the pathogen, the severity of an outbreak, and the strength of the medical and/or behavioral interventions necessary for control.

Url:

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1804098

DOI: 10.1371/journal.pone.0000282
PubMed: 17356693
PubMed Central: 1804098

Affiliations:

Le document en format XML

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<front><div type="abstract" xml:lang="en"><p>To predict the potential severity of outbreaks of infectious diseases such as SARS, HIV, TB and smallpox, a summary parameter, the basic reproduction number R<sub>0</sub>
, is generally calculated from a population-level model. R<sub>0</sub>
 specifies the average number of secondary infections caused by one infected individual during his/her entire infectious period at the start of an outbreak. R<sub>0</sub>
 is used to assess the severity of the outbreak, as well as the strength of the medical and/or behavioral interventions necessary for control. Conventionally, it is assumed that if R<sub>0</sub>
>1 the outbreak generates an epidemic, and if R<sub>0</sub>
<1 the outbreak becomes extinct. Here, we use computational and analytical methods to calculate the average number of secondary infections and to show that it does not necessarily represent an epidemic threshold parameter (as it has been generally assumed). Previously we have constructed a new type of individual-level model (ILM) and linked it with a population-level model. Our ILM generates the same temporal incidence and prevalence patterns as the population-level model; we use our ILM to directly calculate the average number of secondary infections (i.e., R<sub>0</sub>
). Surprisingly, we find that this value of R<sub>0</sub>
 calculated from the ILM is very different from the epidemic threshold calculated from the population-level model. This occurs because many different individual-level processes can generate the same incidence and prevalence patterns. We show that obtaining R<sub>0</sub>
 from empirical contact tracing data collected by epidemiologists and using this R<sub>0</sub>
 as a threshold parameter for a population-level model could produce extremely misleading estimates of the infectiousness of the pathogen, the severity of an outbreak, and the strength of the medical and/or behavioral interventions necessary for control.</p>
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Theory versus Data: How to Calculate R0?

Theory versus Data: How to Calculate R0?

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Le document en format XML

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