We are using epidemic percolation networks and survival analysis to estimate transmissibility of infectious disease.
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.
Funding provided by the National Institute of Allergy and Infectious Diseases.