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A simple mathematical model for the evaluation of the long first wave of the COVID-19 pandemic in Brazil.
Tang, Yuanji; Serdan, Tamires D A; Alecrim, Amanda L; Souza, Diego R; Nacano, Bruno R M; Silva, Flaviano L R; Silva, Eliane B; Poma, Sarah O; Gennari-Felipe, Matheus; Iser-Bem, Patrícia N; Masi, Laureane N; Tang, Sherry; Levada-Pires, Adriana C; Hatanaka, Elaine; Cury-Boaventura, Maria F; Borges, Fernanda T; Pithon-Curi, Tania C; Curpertino, Marli C; Fiamoncini, Jarlei; Leandro, Carol Gois; Gorjao, Renata; Curi, Rui; Hirabara, Sandro Massao.
  • Tang Y; Applied NanoFemto Technologies, LLC, Lowell, MA, USA.
  • Serdan TDA; Interdisciplinary Program of Health Sciences, Cruzeiro do Sul University, Rua Galvao Bueno, 868, Liberdade, Sao Paulo, SP, 01506-000, Brazil.
  • Alecrim AL; Interdisciplinary Program of Health Sciences, Cruzeiro do Sul University, Rua Galvao Bueno, 868, Liberdade, Sao Paulo, SP, 01506-000, Brazil.
  • Souza DR; Interdisciplinary Program of Health Sciences, Cruzeiro do Sul University, Rua Galvao Bueno, 868, Liberdade, Sao Paulo, SP, 01506-000, Brazil.
  • Nacano BRM; Interdisciplinary Program of Health Sciences, Cruzeiro do Sul University, Rua Galvao Bueno, 868, Liberdade, Sao Paulo, SP, 01506-000, Brazil.
  • Silva FLR; Interdisciplinary Program of Health Sciences, Cruzeiro do Sul University, Rua Galvao Bueno, 868, Liberdade, Sao Paulo, SP, 01506-000, Brazil.
  • Silva EB; Interdisciplinary Program of Health Sciences, Cruzeiro do Sul University, Rua Galvao Bueno, 868, Liberdade, Sao Paulo, SP, 01506-000, Brazil.
  • Poma SO; Interdisciplinary Program of Health Sciences, Cruzeiro do Sul University, Rua Galvao Bueno, 868, Liberdade, Sao Paulo, SP, 01506-000, Brazil.
  • Gennari-Felipe M; Interdisciplinary Program of Health Sciences, Cruzeiro do Sul University, Rua Galvao Bueno, 868, Liberdade, Sao Paulo, SP, 01506-000, Brazil.
  • Iser-Bem PN; Interdisciplinary Program of Health Sciences, Cruzeiro do Sul University, Rua Galvao Bueno, 868, Liberdade, Sao Paulo, SP, 01506-000, Brazil.
  • Masi LN; Interdisciplinary Program of Health Sciences, Cruzeiro do Sul University, Rua Galvao Bueno, 868, Liberdade, Sao Paulo, SP, 01506-000, Brazil.
  • Tang S; Kaiser Southern California Permanente Medical Group, Riverside, CA, 92505, USA.
  • Levada-Pires AC; Interdisciplinary Program of Health Sciences, Cruzeiro do Sul University, Rua Galvao Bueno, 868, Liberdade, Sao Paulo, SP, 01506-000, Brazil.
  • Hatanaka E; Interdisciplinary Program of Health Sciences, Cruzeiro do Sul University, Rua Galvao Bueno, 868, Liberdade, Sao Paulo, SP, 01506-000, Brazil.
  • Cury-Boaventura MF; Interdisciplinary Program of Health Sciences, Cruzeiro do Sul University, Rua Galvao Bueno, 868, Liberdade, Sao Paulo, SP, 01506-000, Brazil.
  • Borges FT; Interdisciplinary Program of Health Sciences, Cruzeiro do Sul University, Rua Galvao Bueno, 868, Liberdade, Sao Paulo, SP, 01506-000, Brazil.
  • Pithon-Curi TC; Interdisciplinary Program of Health Sciences, Cruzeiro do Sul University, Rua Galvao Bueno, 868, Liberdade, Sao Paulo, SP, 01506-000, Brazil.
  • Curpertino MC; Medical School, Faculdade Dinâmica do Vale do Piranga, Ponte Nova, MG, Brazil.
  • Fiamoncini J; Laboratory of Epidemiological and Computational Methods in Health, Department of Medicine and Nursing, Universidade Federal de Viçosa, Viçosa, MG, Brazil.
  • Leandro CG; School of Pharmaceutical Sciences, University of Sao Paulo, Sao Paulo, Brazil.
  • Gorjao R; Food Research Center (FoRC), Sao Paulo, Brazil.
  • Curi R; Federal University of Pernambuco, Recife, Brazil.
  • Hirabara SM; Interdisciplinary Program of Health Sciences, Cruzeiro do Sul University, Rua Galvao Bueno, 868, Liberdade, Sao Paulo, SP, 01506-000, Brazil.
Sci Rep ; 11(1): 16400, 2021 08 12.
Article in English | MEDLINE | ID: covidwho-1356583
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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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Experimental Studies / Observational study / Prognostic study Topics: Long Covid Limits: Humans Country/Region as subject: South America / Brazil Language: English Journal: Sci Rep Year: 2021 Document Type: Article Affiliation country: S41598-021-95815-9

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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Experimental Studies / Observational study / Prognostic study Topics: Long Covid Limits: Humans Country/Region as subject: South America / Brazil Language: English Journal: Sci Rep Year: 2021 Document Type: Article Affiliation country: S41598-021-95815-9