Title | Theory versus data: how to calculate R0? |

Publication Type | Journal Article |

Year of Publication | 2007 |

Authors | Breban, R [1], Vardavas R [2], Blower S [3] |

Journal | PloS one |

Volume | 2 |

Issue | 3 |

Pagination | e282 |

Date Published | 2007 |

ISSN | 1932-6203 |

Keywords | Fundamentals of Theoretical Epidemiology [4] |

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. |

URL | http://www.semel.ucla.edu/sites/all/files/biomedicalmodeling/pdf/R0/PLoSONEe282.pdf [5] |

Alternate Journal | PLoS ONE |