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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/24130

Title: Structural differences in mixing behavior informing the role of asymptomatic infection and testing symptom heritability
Authors: Santermans, Eva
Van Kerckhove, Kim
Azmon, Amin
Edmunds, W. John
Beutels, Philippe
Faes, Christel
Hens, Niel
Issue Date: 2017
Publisher: ELSEVIER SCIENCE INC
Citation: MATHEMATICAL BIOSCIENCES, 285, p. 43-54
Abstract: Most infectious disease data is obtained from disease surveillance which is based on observations of symptomatic cases only. However, many infectious diseases are transmitted before the onset of symptoms or without developing symptoms at all throughout the entire disease course, referred to as asymptomatic transmission. Fraser and colleagues [1] showed that this type of transmission plays a key role in assessing the feasibility of intervention measures in controlling an epidemic outbreak. To account for asymptomatic transmission in epidemic models, methods often rely on assumptions that cannot be verified given the data at hand. The present study aims at assessing the contribution of social contact data from asymptomatic and symptomatic individuals in quantifying the contribution of (a)symptomatic infections. We use a mathematical model based on ordinary differential equations (ODE) and a likelihood-based approach followed by Markov Chain Monte Carlo (MCMC) to estimate the model parameters and their uncertainty. Incidence data on influenza-like illness in the initial phase of the 2009 A/H1N1pdm epidemic is used to illustrate that it is possible to estimate either the proportion of asymptomatic infections or the relative infectiousness of symptomatic versus asymptomatic infectives. Further, we introduce a model in which the chance of developing symptoms depends on the disease state of the person that transmitted the infection. In conclusion, incorporating social contact data from both asymptomatic and symptomatic individuals allows inferring on parameters associated with asymptomatic infection based on disease data from symptomatic cases only. (C) 2017 Elsevier Inc. All rights reserved.
Notes: [Santermans, Eva; Van Kerckhove, Kim; Faes, Christel; Hens, Niel] Hasselt Univ, Interuniv Inst Biostat & Stat Bioinformat, Hasselt, Belgium. [Azmon, Amin] Novartis Pharma AG, Oncol Business Unit Gen Med Affairs, Novartis Campus, Basel, Switzerland. [Edmunds, W. John] London Sch Hyg & Trop Med, Dept Infect Dis Epidemiol, Ctr Math Modelling Infect Dis, London, England. [Beutels, Philippe; Hens, Niel] Univ Antwerp, Vaccine & Infect Dis Inst, Ctr Hlth Econ Res & Modelling Infect Dis, Antwerp, Belgium.
URI: http://hdl.handle.net/1942/24130
DOI: 10.1016/j.mbs.2016.12.004
ISI #: 000394066400004
ISSN: 0025-5564
Category: A1
Type: Journal Contribution
Appears in Collections: Research publications

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