tetano
Editor, Senior Moderator
BMC Public Health. 2014 Sep 29;14(1):1019. [Epub ahead of print]
A large-scale immuno-epidemiological simulation of influenza A epidemics.
Lukens S, DePasse J, Rosenfeld R, Ghedin E, Mochan E, Brown ST, Grefenstette J, Burke DS, Swigon D, Clermont G.
Abstract
BACKGROUND:
Agent based models (ABM) are useful to explore population-level scenarios of disease spread andcontainment, but typically characterize infected individuals using simplified models of infection andsymptoms dynamics. Adding more realistic models of individual infections and symptoms may helpto create more realistic population level epidemic dynamics.
METHODS:
Using an equation-based, host-level mathematical model of influenza A virus infection, we develop afunction that expresses the dependence of infectivity and symptoms of an infected individual on initialviral load, age, and viral strain phenotype. We incorporate this response function in a population-scaleagent-based model of influenza A epidemic to create a hybrid multiscale modeling framework thatreflects 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 symptomsmeasured in humans. By linearization of symptoms responses of the host-level model we obtaina map of the parameters of the model that characterizes clinical phenotypes of influenza infection andimmune response variability over the population. At the population-level model, we analyze the effectof individualizing viral response in agent-based model by simulating epidemics across AlleghenyCounty, 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 ofpopulation-level effects of individual differences in infections and symptoms, with minimal additionalcomputational cost compared to the existing population-level simulations.
PMID:
25266818
[PubMed - as supplied by publisher]
Free full text
http://www.ncbi.nlm.nih.gov/pubmed/25266818
A large-scale immuno-epidemiological simulation of influenza A epidemics.
Lukens S, DePasse J, Rosenfeld R, Ghedin E, Mochan E, Brown ST, Grefenstette J, Burke DS, Swigon D, Clermont G.
Abstract
BACKGROUND:
Agent based models (ABM) are useful to explore population-level scenarios of disease spread andcontainment, but typically characterize infected individuals using simplified models of infection andsymptoms dynamics. Adding more realistic models of individual infections and symptoms may helpto create more realistic population level epidemic dynamics.
METHODS:
Using an equation-based, host-level mathematical model of influenza A virus infection, we develop afunction that expresses the dependence of infectivity and symptoms of an infected individual on initialviral load, age, and viral strain phenotype. We incorporate this response function in a population-scaleagent-based model of influenza A epidemic to create a hybrid multiscale modeling framework thatreflects 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 symptomsmeasured in humans. By linearization of symptoms responses of the host-level model we obtaina map of the parameters of the model that characterizes clinical phenotypes of influenza infection andimmune response variability over the population. At the population-level model, we analyze the effectof individualizing viral response in agent-based model by simulating epidemics across AlleghenyCounty, 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 ofpopulation-level effects of individual differences in infections and symptoms, with minimal additionalcomputational cost compared to the existing population-level simulations.
PMID:
25266818
[PubMed - as supplied by publisher]
Free full text
http://www.ncbi.nlm.nih.gov/pubmed/25266818