Simulating and analysing infectious disease data in a heterogeneous population with migration.

Abstract:

:Mathematical modelling of infectious diseases has gained growing attention in epidemiology during the last decades. The major benefits of simulating compartmental models are the prediction of the consequences of potential interventions, a deeper understanding of epidemic dynamics and clinical decision support. The main limitation is however that several parameters are based on uncertain expert guesses (default values) and are not estimated from the study data. In this paper we build a bridge between an extension of the well-known deterministic S-I-R (Susceptible-Infectious-Removed) model which can be described with differential equations and the stochastic counterpart which can be used for statistical inference if outbreak data on an individual level are available. The possibly time-dependent transmission rate as well as the (basic) reproduction number are the main epidemiological parameters of interest. Furthermore, one important type of heterogeneity is considered: individuals may vary due to their susceptibility, i.e., risk factors for infection may be investigated. A SAS computer program is provided to simulate outbreak data for this type of setting. The statistical analysis and typical challenges with epidemic data are discussed. Given data on an individual level, the Cox-Aalen survival model that is based on a multiplicative-additive hazard structure turned out to be a suitable tool for that purpose. The results give valuable information for epidemiologists, statisticians and public health researchers.

authors

Wolkewitz M,Schumacher M

doi

10.1016/j.cmpb.2010.05.007

subject

Has Abstract

pub_date

2011-11-01 00:00:00

pages

29-36

issue

2

eissn

0169-2607

issn

1872-7565

pii

S0169-2607(10)00122-7

journal_volume

104

pub_type

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