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Abstract
We propose herein a mathematical model to predict the COVID-19 evolution and evaluate the impact of governmental decisions on this evolution, attempting to explain the long duration of the pandemic in the 26 Brazilian states and their capitals well as in the Federative Unit. The prediction was performed based on the growth rate of new cases in a stable period, and the graphics plotted with the significant governmental decisions to evaluate the impact on the epidemic curve in each Brazilian state and city. Analysis of the predicted new cases was correlated with the total number of hospitalizations and deaths related to COVID-19. Because Brazil is a vast country, with high heterogeneity and complexity of the regional/local characteristics and governmental authorities among Brazilian states and cities, we individually predicted the epidemic curve based on a specific stable period with reduced or minimal interference on the growth rate of new cases. We found good accuracy, mainly in a short period (weeks). The most critical governmental decisions had a significant temporal impact on pandemic curve growth. A good relationship was found between the predicted number of new cases and the total number of inpatients and deaths related to COVID-19. In summary, we demonstrated that interventional and preventive measures directly and significantly impact the COVID-19 pandemic using a simple mathematical model. This model can easily be applied, helping, and directing health and governmental authorities to make further decisions to combat the pandemic.
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1 Applied NanoFemto Technologies, LLC, Lowell, USA (GRID:grid.455232.7)
2 Cruzeiro do Sul University, Interdisciplinary Program of Health Sciences, Sao Paulo, Brazil (GRID:grid.411936.8) (ISNI:0000 0001 0366 4185)
3 Kaiser Southern California Permanente Medical Group, Riverside, USA (GRID:grid.411936.8)
4 Faculdade Dinâmica do Vale do Piranga, Medical School, Ponte Nova, Brazil (GRID:grid.411936.8); Universidade Federal de Viçosa, Laboratory of Epidemiological and Computational Methods in Health, Department of Medicine and Nursing, Viçosa, Brazil (GRID:grid.12799.34) (ISNI:0000 0000 8338 6359)
5 University of Sao Paulo, School of Pharmaceutical Sciences, Sao Paulo, Brazil (GRID:grid.11899.38) (ISNI:0000 0004 1937 0722); Food Research Center (FoRC), Sao Paulo, Brazil (GRID:grid.11899.38)
6 Federal University of Pernambuco, Recife, Brazil (GRID:grid.411227.3) (ISNI:0000 0001 0670 7996)
7 Cruzeiro do Sul University, Interdisciplinary Program of Health Sciences, Sao Paulo, Brazil (GRID:grid.411936.8) (ISNI:0000 0001 0366 4185); Butantan Institute, Sao Paulo, Brazil (GRID:grid.418514.d) (ISNI:0000 0001 1702 8585)