Modelling seasonality and viral mutation to predict the course of an influenza pandemic.
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.
Shi
P
P
Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0205, USA.
Keskinocak
P
P
Swann
J L
JL
Lee
B Y
BY
D050936:Basic Reproduction Number; D003198:Computer Simulation; D004196:Disease Outbreaks; D004812:Epidemiologic Methods; D005845:Georgia / Q000453:epidemiology; D006801:Humans; D007251:Influenza, Human / Q000453:epidemiology* / Q000821:virology; D015233:Models, Statistical; D009154:Mutation; D009975:Orthomyxoviridae / Q000235:genetics*; D012621:Seasons; D012680:Sensitivity and Specificity