TY - JOUR
T1 - Simulation-based evaluation of school reopening strategies during COVID-19
T2 - A case study of São Paulo, Brazil
AU - Cruz, E. H.M.
AU - Maciel, J. M.
AU - Clozato, C.
AU - Serpa, M. S.
AU - Navaux, P. O.A.
AU - Meneses, E.
AU - Abdalah, M.
AU - Diener, M.
N1 - Supplementary material. The supplementary material for this article can be found at https://doi.org/10.1017/S0950268821001059 Acknowledgements. This research was partially supported by a machine allocation on the Kabré supercomputer at the Costa Rica National High Technology Center. We would like to thank Marco A. Amato for reviewing an earlier version of this paper.
PY - 2021/4/30
Y1 - 2021/4/30
N2 - During the COVID-19 pandemic, many countries opted for strict public health measures, including closing schools. After some time, they have started relaxing some of those restrictions. To avoid overwhelming health systems, predictions for the number of new COVID-19 cases need to be considered when choosing a school reopening strategy. Using a computer simulation based on a stochastic compartmental model that includes a heterogeneous and dynamic network, we analyze different strategies to reopen schools in the São Paulo Metropolitan Area, including one similar to the official reopening plan. Our model allows us to describe different types of relations between people, each type with a different infectiousness. Based on our simulations and model assumptions, our results indicate that reopening schools with all students at once has a big impact on the number of new COVID-19 cases, which could cause a collapse of the health system. On the other hand, our results also show that a controlled school reopening could possibly avoid the collapse of the health system, depending on how people follow sanitary measures. We estimate that postponing the schools' return date for after a vaccine becomes available may save tens of thousands of lives just in the São Paulo Metropolitan Area compared to a controlled reopening considering a worst case scenario. We also discuss our model constraints and the uncertainty of its parameters.
AB - During the COVID-19 pandemic, many countries opted for strict public health measures, including closing schools. After some time, they have started relaxing some of those restrictions. To avoid overwhelming health systems, predictions for the number of new COVID-19 cases need to be considered when choosing a school reopening strategy. Using a computer simulation based on a stochastic compartmental model that includes a heterogeneous and dynamic network, we analyze different strategies to reopen schools in the São Paulo Metropolitan Area, including one similar to the official reopening plan. Our model allows us to describe different types of relations between people, each type with a different infectiousness. Based on our simulations and model assumptions, our results indicate that reopening schools with all students at once has a big impact on the number of new COVID-19 cases, which could cause a collapse of the health system. On the other hand, our results also show that a controlled school reopening could possibly avoid the collapse of the health system, depending on how people follow sanitary measures. We estimate that postponing the schools' return date for after a vaccine becomes available may save tens of thousands of lives just in the São Paulo Metropolitan Area compared to a controlled reopening considering a worst case scenario. We also discuss our model constraints and the uncertainty of its parameters.
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U2 - 10.1017/S0950268821001059
DO - 10.1017/S0950268821001059
M3 - Article
C2 - 33928895
AN - SCOPUS:85105585991
SN - 0950-2688
VL - 149
JO - Epidemiology and Infection
JF - Epidemiology and Infection
M1 - e118
ER -