Marketing and Business Analytics
Modelers typically use detailed simulation models and vary the fraction vaccinated to study outbreak control. However, there is currently no guidance for modelers on how much detail (i.e., heterogeneity) is necessary and how large a population to simulate. We provide theoretical and numerical guidance for those decisions and also analyze the benefit of a faster public health response through a stochastic simulation model in the case of measles in the United States. Theoretically, we prove that the outbreak size converges as the simulation population increases and that the outbreaks are slightly larger with a heterogeneous community structure. We find that the simulated outbreak size is not sensitive to the size of the simulated population beyond a certain size. We also observe that in case of an outbreak, a faster public health response provides benefits similar to increased vaccination. Insights from this study can inform the control and elimination measures of the ongoing coronavirus disease (COVID-19) as measles has shown to have a similar structure to COVID-19.
Coronavirus disease 2019 (COVID-19), Fast response, Heterogeneity, Infectious disease modeling, OR in health services, Population size, Simulation, Vaccination
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This work is licensed under a Creative Commons Attribution 4.0 License.
Kezban Yagci Sokat and Benjamin Armbruster. "Modeling and controlling epidemic outbreaks: The role of population size, model heterogeneity and fast response in the case of measles" Mathematics (2020): 1-18. https://doi.org/10.3390/math8111892