Modelling seasonality and viral mutation to predict the course of an influenza pandemic.

TitleModelling seasonality and viral mutation to predict the course of an influenza pandemic.
Publication TypeJournal Article
Year of Publication2010
AuthorsShi P, Keskinocak P, Swann JL, Lee BY
JournalEpidemiol Infect
Volume138
Issue10
Pagination1472-81
Date Published2010 Oct
ISSN1469-4409
KeywordsBasic Reproduction Number, Computer Simulation, Disease Outbreaks, Epidemiologic Methods, Georgia, Humans, Influenza, Human, Models, Statistical, Mutation, Orthomyxoviridae, Seasons, Sensitivity and Specificity
Abstract

As the 2009 H1N1 influenza pandemic (H1N1) has shown, public health decision-makers may have to predict the subsequent course and severity of a pandemic. We developed an agent-based simulation model and used data from the state of Georgia to explore the influence of viral mutation and seasonal effects on the course of an influenza pandemic. We showed that when a pandemic begins in April certain conditions can lead to a second wave in autumn (e.g. the degree of seasonality exceeding 0.30, or the daily rate of immunity loss exceeding 1% per day). Moreover, certain combinations of seasonality and mutation variables reproduced three-wave epidemic curves. Our results may offer insights to public health officials on how to predict the subsequent course of an epidemic or pandemic based on early and emerging viral and epidemic characteristics and what data may be important to gather.

DOI10.1017/S0950268810000300
Alternate JournalEpidemiol. Infect.
PubMed ID20158932
PubMed Central IDPMC3779923
Grant ListR01 LM009132 / LM / NLM NIH HHS / United States
U54 GM088491 / GM / NIGMS NIH HHS / United States
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