tetano
Editor, Senior Moderator
Biostatistics. 2010 Nov 11. [Epub ahead of print]
Contact intervals, survival analysis of epidemic data, and estimation of R0.
Kenah E.
Department of Biostatistics, University of Washington, Seattle, WA 98105, USA. eek4@u.washington.edu.
Abstract
We argue that the time from the onset of infectiousness to infectious contact, which we call the "contact interval," is a better basis for inference in epidemic data than the generation or serial interval. Since contact intervals can be right censored, survival analysis is the natural approach to estimation. Estimates of the contact interval distribution can be used to estimate R(0) in both mass-action and network-based models. We apply these methods to 2 data sets from the 2009 influenza A(H1N1) pandemic.
PMID: 21071607 [PubMed - as supplied by publisher]
http://www.ncbi.nlm.nih.gov/pubmed/21071607
Contact intervals, survival analysis of epidemic data, and estimation of R0.
Kenah E.
Department of Biostatistics, University of Washington, Seattle, WA 98105, USA. eek4@u.washington.edu.
Abstract
We argue that the time from the onset of infectiousness to infectious contact, which we call the "contact interval," is a better basis for inference in epidemic data than the generation or serial interval. Since contact intervals can be right censored, survival analysis is the natural approach to estimation. Estimates of the contact interval distribution can be used to estimate R(0) in both mass-action and network-based models. We apply these methods to 2 data sets from the 2009 influenza A(H1N1) pandemic.
PMID: 21071607 [PubMed - as supplied by publisher]
http://www.ncbi.nlm.nih.gov/pubmed/21071607