Abstract
Understanding the effect of media on disease spread can help improve epidemic forecasting and uncover preventive measures to slow the spread of disease. Most previously introduced models have approximated media effect through disease incidence, making media influence dependent on the size of epidemic. We propose an alternative approach, which relies on real data about disease coverage in the news, allowing us to model low incidence/high interest diseases, such as SARS, Ebola or H1N1. We introduce a network-based model, in which disease is transmitted through local interactions between individuals and the probability of transmission is affected by media coverage. We assume that media attention increases self-protection (e.g. hand washing and compliance with social distancing), which, in turn, decreases disease model. We apply the model to the case of H1N1 transmission in Mexico City in 2009 and show how media influence—measured by the time series of the weekly count of news articles published on the outbreak—helps to explain the observed transmission dynamics. We show that incorporating the media attention based on the observed media coverage of the outbreak better estimates the disease dynamics from what would be predicted by using media function that approximate the media impact using the number of cases and rate of spread. Finally, we apply the model to a typical influenza season in Washington, DC and estimate how the transmission pattern would have changed given different levels of media coverage.
Highlights
Disease transmission takes place in a dynamic social environment, wherein individual health decisions are guided by cultural norms, peer influence, and media influence
We introduce a model of disease spread that incorporates media attention and influence in disease spread dynamics
We examined the effects of the parameters, α and λ, on the shape of the media function and the disease spread
Summary
Disease transmission takes place in a dynamic social environment, wherein individual health decisions are guided by cultural norms, peer influence, and media influence. Recognizing the importance of individuals’ actions in preventing the spread of infection, researchers are beginning to explore mathematical models that incorporate such actions [1, 2]. These models have been used to inform strategies to control the spread of disease [3] and to quantify the role of individual protective actions in controlling several outbreaks, including the 2014 Ebola outbreak in West Africa [4], the 2003 SARS outbreak in Hong Kong [5] and the 2009 H1N1 outbreak in Central Mexico [6].
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