Journal
JOURNAL OF THE ROYAL SOCIETY INTERFACE
Volume 8, Issue 59, Pages 856-867Publisher
ROYAL SOC
DOI: 10.1098/rsif.2010.0515
Keywords
epidemic modelling; H1N1 influenza; maximum likelihood; model fitting; next-generation matrix
Categories
Funding
- European FP7 grant Epiwork
- Israel Science Foundation
- Israel Ministry of Health
- Israel National Institute for Health Policy and Health Services Research
- Israel Academy of Sciences and Humanities
Ask authors/readers for more resources
This paper presents new computational and modelling tools for studying the dynamics of an epidemic in its initial stages that use both available incidence time series and data describing the population's infection network structure. The work is motivated by data collected at the beginning of the H1N1 pandemic outbreak in Israel in the summer of 2009. We formulated a new discrete-time stochastic epidemic SIR (susceptible-infected-recovered) model that explicitly takes into account the disease's specific generation-time distribution and the intrinsic demographic stochasticity inherent to the infection process. Moreover, in contrast with many other modelling approaches, the model allows direct analytical derivation of estimates for the effective reproductive number (R-e) and of their credible intervals, by maximum likelihood and Bayesian methods. The basic model can be extended to include age-class structure, and a maximum likelihood methodology allows us to estimate the model's next-generation matrix by combining two types of data: (i) the incidence series of each age group, and (ii) infection network data that provide partial information of 'who-infected-who'. Unlike other approaches for estimating the next-generation matrix, the method developed here does not require making a priori assumptions about the structure of the next-generation matrix. We show, using a simulation study, that even a relatively small amount of information about the infection network greatly improves the accuracy of estimation of the next-generation matrix. The method is applied in practice to estimate the next-generation matrix from the Israeli H1N1 pandemic data. The tools developed here should be of practical importance for future investigations of epidemics during their initial stages. However, they require the availability of data which represent a random sample of the real epidemic process. We discuss the conditions under which reporting rates may or may not influence our estimated quantities and the effects of bias.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available