A large-scale immuno-epidemiological simulation of influenza A epidemics.

TitleA large-scale immuno-epidemiological simulation of influenza A epidemics.
Publication TypeJournal Article
Year of Publication2014
AuthorsLukens S, DePasse J, Rosenfeld R, Ghedin E, Mochan E, Brown ST, Grefenstette J, Burke DS, Swigon D, Clermont G
JournalBMC Public Health
Volume14
Pagination1019
Date Published2014
ISSN1471-2458
Abstract

BACKGROUND: Agent based models (ABM) are useful to explore population-level scenarios of disease spread and containment, but typically characterize infected individuals using simplified models of infection and symptoms dynamics. Adding more realistic models of individual infections and symptoms may help to create more realistic population level epidemic dynamics.METHODS: Using an equation-based, host-level mathematical model of influenza A virus infection, we develop a function that expresses the dependence of infectivity and symptoms of an infected individual on initial viral load, age, and viral strain phenotype. We incorporate this response function in a population-scale agent-based model of influenza A epidemic to create a hybrid multiscale modeling framework that reflects both population dynamics and individualized host response to infection.RESULTS: At the host level, we estimate parameter ranges using experimental data of H1N1 viral titers and symptoms measured in humans. By linearization of symptoms responses of the host-level model we obtain a map of the parameters of the model that characterizes clinical phenotypes of influenza infection and immune response variability over the population. At the population-level model, we analyze the effect of individualizing viral response in agent-based model by simulating epidemics across Allegheny County, Pennsylvania under both age-specific and age-independent severity assumptions.CONCLUSIONS: We present a framework for multi-scale simulations of influenza epidemics that enables the study of population-level effects of individual differences in infections and symptoms, with minimal additional computational cost compared to the existing population-level simulations.

DOI10.1186/1471-2458-14-1019
Alternate JournalBMC Public Health
PubMed ID25266818
PubMed Central IDPMC4194421
Grant ListR01-GM83602 / GM / NIGMS NIH HHS / United States
U54 GM088491 / GM / NIGMS NIH HHS / United States
U54 GM088491 / GM / NIGMS NIH HHS / United States
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