Models of epidemics: when contact repetition and clustering should be included
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Models of epidemics: when contact repetition and clustering should be included Timo Smieszek*1, Lena Fiebig2 and Roland W Scholz1 Address: 1Institute for Environmental Decisions, Natural and Social Science Interface, ETH Zurich, Universitaetsstrasse 22, 8092 Zurich, Switzerland and 2Department of Public Health and Epidemiology, Swiss Tropical Institute, Socinstrasse 57, 4051 Basel, Switzerland Email: Timo Smieszek* - [email protected]; Lena Fiebig - [email protected]; Roland W Scholz - [email protected] * Corresponding author
Published: 29 June 2009 Theoretical Biology and Medical Modelling 2009, 6:11
doi:10.1186/1742-4682-6-11
Received: 5 March 2009 Accepted: 29 June 2009
This article is available from: http://www.tbiomed.com/content/6/1/11 © 2009 Smieszek et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract Background: The spread of infectious disease is determined by biological factors, e.g. the duration of the infectious period, and social factors, e.g. the arrangement of potentially contagious contacts. Repetitiveness and clustering of contacts are known to be relevant factors influencing the transmission of droplet or contact transmitted diseases. However, we do not yet completely know under what conditions repetitiveness and clustering should be included for realistically modelling disease spread. Methods: We compare two different types of individual-based models: One assumes random mixing without repetition of contacts, whereas the other assumes that the same contacts repeat day-by-day. The latter exists in two variants, with and without clustering. We systematically test and compare how the total size of an outbreak differs between these model types depending on the key parameters transmission probability, number of contacts per day, duration of the infectious period, different levels of clustering and varying proportions of repetitive contacts. Results: The simulation runs under different parameter constellations provide the following results: The difference between both model types is highest for low numbers of contacts per day and low transmission probabilities. The number of contacts and the transmission probability have a higher influence on this difference than the duration of the infectious period. Even when only minor parts of the daily contacts are repetitive and clustered can there be relevant differences compared to a purely random mixing model. Conclusion: We show that random mixing models provide acceptable estimates of the total outbreak size if the number of contacts per day is high or if the per-contact transmission probability is high, as seen in typical childhood diseases such as measles. In the case of very short infectious periods, for instance, as in Norovirus, m
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