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Theory versus Data: How to Calculate R0?

Overview of attention for article published in PLOS ONE, March 2007
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Title
Theory versus Data: How to Calculate R0?
Published in
PLOS ONE, March 2007
DOI 10.1371/journal.pone.0000282
Pubmed ID
Authors

Romulus Breban, Raffaele Vardavas, Sally Blower

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 R(0), is generally calculated from a population-level model. R(0) 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(0) 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(0)>1 the outbreak generates an epidemic, and if R(0)<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(0)). Surprisingly, we find that this value of R(0) 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(0) from empirical contact tracing data collected by epidemiologists and using this R(0) 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.

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Geographical breakdown

Country Count As %
United Kingdom 5 2%
United States 2 <1%
Vietnam 1 <1%
Australia 1 <1%
Brazil 1 <1%
France 1 <1%
Argentina 1 <1%
Mexico 1 <1%
Unknown 274 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 61 21%
Student > Ph. D. Student 54 19%
Student > Master 32 11%
Professor 25 9%
Student > Bachelor 23 8%
Other 49 17%
Unknown 43 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 59 21%
Medicine and Dentistry 42 15%
Mathematics 36 13%
Biochemistry, Genetics and Molecular Biology 14 5%
Computer Science 11 4%
Other 65 23%
Unknown 60 21%