Vaccination Strategies on a Robust Contact Network
Mathematical models of disease spreading are a key factor in ensuring that we are prepared to deal with the next epidemic. They allow us to predict how an infection will spread throughout a population, thereby allowing us to make intelligent choices when attempting to contain a disease. Whether due to a lack of empirical data, a lack of computational power, a lack of biological understanding, or some combination thereof, traditional models must make sweeping, unrealistic assumptions about the behavior of a population during an epidemic.
We present the results of granular epidemic simulations using a rich social network constructed from real-world interactions, demonstrating the effects of ten potential vaccination strategies. We confirm estimates by the WHO and the CDC regarding the virulence of measles-like diseases, and we show how representing a population as a temporal graph and applying existing graph metrics can lead to more effective interventions.
KeywordsContact networks Epidemics Vaccination
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