Modelling the effect of mass media on influenza transmission and vaccine uptake

Influenza causes annual epidemics and occasional pandemics that have claimed millions of lives throughout history. Media reports affect social behaviour during epidemics and pandemics. Changes in social behaviour, in turn, effect key epidemic measurements such as peak magnitude, time to peak, and the beginning and end of an epidemic. The extent of this effect has not been realized. Mathematical models can be employed to study the effects of mass media. In this work, previous mathematical models concerning epidemics and mass media are studied. A novel inclusion of mass media is developed through the addition of a mass media compartment in a Susceptible-Exposed-Infected-Recovered (SEIR) model to look at the effect of mass media on an epidemic. Multiple levels of social distancing are considered in the framework of an ODE model. Vaccination is included in various models for susceptible individuals. Systems of stochastic differential equation models for each of the different scenarios have been derived. An Agent-Based Monte Carlo (ABMC) simulation is used to determine the variability in these key epidemic measurements, so as to provide some insight in to the effects of mass media on epidemic data. Data can help to provide an epidemic outcome that is seen at the population level. Data is used in order to inform parameter values and the novel inclusion of media. A look to future work is also included.