Variational data assimilation with epidemic models.
Mathematical modelling is playing an increasing role in developing an understanding of the dynamics of communicable disease and assisting the construction and implementation of intervention strategies. The threat of novel emergent pathogens in human and animal hosts implies the requirement for methods that can robustly estimate epidemiological parameters and provide forecasts. Here, a technique called variational data assimilation is introduced as a means of optimally melding dynamic epidemic models with epidemiological observations and data to provide forecasts and parameter estimates. Using data from a simulated epidemic process the method is used to estimate the start time of an epidemic, to provide a forecast of future epidemic behaviour and estimate the basic reproductive ratio. A feature of the method is that it uses a basic continuous-time SIR model, which is often the first point of departure for epidemiological modelling during the early stages of an outbreak. The method is illustrated by application to data gathered during an outbreak of influenza in a school environment.
Rhodes
C J
CJ
Institute for Mathematical Sciences, Imperial College London, 53 Prince's Gate, Exhibition Road, South Kensington, London SW72PG, UK. c.rhodes@imperial.ac.uk
Hollingsworth
T D
TD
D000293:Adolescent; D000818:Animals; D002648:Child; D003141:Communicable Diseases / Q000453:epidemiology*; D004196:Disease Outbreaks; D005544:Forecasting; D006801:Humans; D007251:Influenza, Human / Q000453:epidemiology; D008954:Models, Biological; D015233:Models, Statistical; D012574:Schools