title: Structural differences in mixing behavior informing the role of asymptomatic infection and testing symptom heritability.
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.
authors.affiliation: Interuniversity Institute for Biostatistics and statistical Bioinformatics, Hasselt University, Belgium. Electronic address: eva.santermans@uhasselt.be.
authors.forename: Eva
authors.initials: E
authors.lastname: Santermans
authors.affiliation: Interuniversity Institute for Biostatistics and statistical Bioinformatics, Hasselt University, Belgium.
authors.forename: Kim
authors.initials: K
authors.lastname: Van Kerckhove
authors.affiliation: Novartis Pharma AG, Oncology Business Unit/General Medical Affairs, Novartis Campus, Basel, Switzerland.
authors.forename: Amin
authors.initials: A
authors.lastname: Azmon
authors.affiliation: Centre for the Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom.
authors.forename: W
authors.initials: W
authors.lastname: John Edmunds
authors.affiliation: Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine & Infectious Disease Institute, University of Antwerp, Belgium.
authors.forename: Philippe
authors.initials: P
authors.lastname: Beutels
authors.affiliation: Interuniversity Institute for Biostatistics and statistical Bioinformatics, Hasselt University, Belgium.
authors.forename: Christel
authors.initials: C
authors.lastname: Faes
authors.affiliation: Interuniversity Institute for Biostatistics and statistical Bioinformatics, Hasselt University, Belgium; Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine & Infectious Disease Institute, University of Antwerp, Belgium.
authors.forename: Niel
authors.initials: N
authors.lastname: Hens
mesh_terms: D058345:Asymptomatic Infections; D003141:Communicable Diseases / Q000635:transmission*; D058872:Epidemics; D006801:Humans; D007251:Influenza, Human / Q000635:transmission*; D008962:Models, Theoretical
keywords: Asymptomatic transmission; Influenza; Mathematical model; Social contact data; Symptom heritability