Abstract
A pandemic can arise without warning, and it is important for those in charge of managing the outbreak to understand how diseases spread. Being able to simulate the spread of a disease in varying environments can help the world be more prepared when an outbreak occurs. The COVID-19 City Simulator allows the user to test the spread of the virus under multiple different scenarios. Parallel computing can help to make these simulations more efficient by allowing data to be gathered at a faster rate on a particle simulation. This paper shows how OpenMP and MPI can improve a pandemic simulation by cutting the runtime from over 25 s to under 10 s when 4 threads and 4 boxes are used. We also find that the speed of implementing a lockdown largely impacts the amount of cases and deaths in the city.
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This material is based in part upon work supported by the National Science Foundation under grant number IIA-1301726. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.
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Stratton, D., Garner, W., Williams, T., Harris, F.C. (2021). Modeling the COVID-19 Epidemic in a Parallelized City Simulation. In: Latifi, S. (eds) ITNG 2021 18th International Conference on Information Technology-New Generations. Advances in Intelligent Systems and Computing, vol 1346. Springer, Cham. https://doi.org/10.1007/978-3-030-70416-2_24
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DOI: https://doi.org/10.1007/978-3-030-70416-2_24
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