Abstract
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.
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Appendix
Appendix
Number of interactions by time in the Copenhagen dataset.
Numbers of affected individuals by time in measles simulations without any interventions. Each curve is an average of five simulations with the same infection seed; the pointwise median of thirty curves is shown in black.
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Siu, C., Migler, T. (2020). Vaccination Strategies on a Robust Contact Network. In: Cherifi, H., Gaito, S., Mendes, J., Moro, E., Rocha, L. (eds) Complex Networks and Their Applications VIII. COMPLEX NETWORKS 2019. Studies in Computational Intelligence, vol 881. Springer, Cham. https://doi.org/10.1007/978-3-030-36687-2_26
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DOI: https://doi.org/10.1007/978-3-030-36687-2_26
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