Version 1
: Received: 28 December 2020 / Approved: 29 December 2020 / Online: 29 December 2020 (14:34:24 CET)
How to cite:
Asai, G. Model Estimation for Sars-Cov-2 Peak Contagion (Mespc): Brazil’s Case for the First and Second Wave. Preprints2020, 2020120730. https://doi.org/10.20944/preprints202012.0730.v1
Asai, G. Model Estimation for Sars-Cov-2 Peak Contagion (Mespc): Brazil’s Case for the First and Second Wave. Preprints 2020, 2020120730. https://doi.org/10.20944/preprints202012.0730.v1
Asai, G. Model Estimation for Sars-Cov-2 Peak Contagion (Mespc): Brazil’s Case for the First and Second Wave. Preprints2020, 2020120730. https://doi.org/10.20944/preprints202012.0730.v1
APA Style
Asai, G. (2020). Model Estimation for Sars-Cov-2 Peak Contagion (Mespc): Brazil’s Case for the First and Second Wave. Preprints. https://doi.org/10.20944/preprints202012.0730.v1
Chicago/Turabian Style
Asai, G. 2020 "Model Estimation for Sars-Cov-2 Peak Contagion (Mespc): Brazil’s Case for the First and Second Wave" Preprints. https://doi.org/10.20944/preprints202012.0730.v1
Abstract
With data for SARS-CoV-2 and with many countries entering the second wave of contagion required the improvement of the forecasting model, structuring its model to forecast the peak of the first and second contagion wave in Brazil. The Model Estimation for SARS-CoV-2 Peak Contagion (MESPC) was structured, capable of estimating the peak of contagion for SARS-CoV-2 in the first and second waves, as the main objective of this work. Using the MESPC model, it was possible to estimate, with a certain reliability degree, the peak of contagion for the first and second waves in Brazil, with one day difference from the real to the forecast. It is possible to use MESPC to forecast the peak of contagion for several regions, provided that the necessary structure and calibration are respected.
Keywords
Monte Carlo Simulation; Bayesian statistics; SARS-CoV-2; Covid-19; model estimation
Subject
Computer Science and Mathematics, Algebra and Number Theory
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
