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      Predicting the number of total COVID-19 cases and deaths in Brazil by the Gompertz model

      research-article
      Nonlinear Dynamics
      Springer Netherlands
      Model Gompertz, Minimal error method, Inverse problem, Covid-19

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          Abstract

          In this work, we estimate the total number of infected and deaths by COVID-19 in Brazil and two Brazilian States (Rio de Janeiro and Sao Paulo). To obtain the unknown data, we use an iterative method in the Gompertz model, whose formulation is well known in the field of biology. Based on data collected from the Ministry of Health from February 26, 2020, to July 2, 2020, we predict, from July 3 to 9 and at the end of the epidemic, the number of infected and killed for the whole country and for the Brazilian states of Sao Paulo and Rio de Janeiro. We estimate, until July 9, 2020, a total of 1,709,755 cases and 65,384 deaths in Brazil, 331,718 cases and 15,621 deaths in Sao Paulo, 134,454 cases and 11,574 deaths in Rio de Janeiro. We also estimate the basic reproduction number \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R_0$$\end{document} for Brazil and its two states. The estimated values \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(R_0)$$\end{document} were 1.3, 1.3, and 1.4 for Brazil, Sao Paulo, and Rio de Janeiro, respectively. The results show a good fit between the observed data and those obtained by the Gompertz. The proposed methodology can also be applied to other countries and Brazilian states, and we provide an executable as well as the source code for a straightforward application of the method on such data.

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          Most cited references17

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          The use of Gompertz models in growth analyses, and new Gompertz-model approach: An addition to the Unified-Richards family

          The Gompertz model is well known and widely used in many aspects of biology. It has been frequently used to describe the growth of animals and plants, as well as the number or volume of bacteria and cancer cells. Numerous parametrisations and re-parametrisations of varying usefulness are found in the literature, whereof the Gompertz-Laird is one of the more commonly used. Here, we review, present, and discuss the many re-parametrisations and some parameterisations of the Gompertz model, which we divide into T i (type I)- and W 0 (type II)-forms. In the W 0-form a starting-point parameter, meaning birth or hatching value (W 0), replaces the inflection-time parameter (T i ). We also propose new “unified” versions (U-versions) of both the traditional T i -form and a simplified W 0-form. In these, the growth-rate constant represents the relative growth rate instead of merely an unspecified growth coefficient. We also present U-versions where the growth-rate parameters return absolute growth rate (instead of relative). The new U-Gompertz models are special cases of the Unified-Richards (U-Richards) model and thus belong to the Richards family of U-models. As U-models, they have a set of parameters, which are comparable across models in the family, without conversion equations. The improvements are simple, and may seem trivial, but are of great importance to those who study organismal growth, as the two new U-Gompertz forms give easy and fast access to all shape parameters needed for describing most types of growth following the shape of the Gompertz model.
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            Forecasting COVID-19

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              Modeling and prediction of COVID-19 in Mexico applying mathematical and computational models

              Highlights • Mathematical and computational models are used to predict cases of COVID-19 in Mexico. • The data is obtained through the Daily Technical Report issued by the Mexican Ministry of Health. • Gompertz, Logistic and Artificial Neural Network perform the modeling of the cases confirmed by COVID-19 with an R2>0.999. • Logistic, Gompertz and inverse Artificial Neural Network predicts the maximum number of new daily cases on May 8th, June 25th and May12th, 2020, respectively. • The Gompertz, Logistic and inverse Artificial Neural Network models predict different number of cases of COVID-19 at the end of the epidemic.
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                Author and article information

                Contributors
                jhimyunac@gmail.com
                Journal
                Nonlinear Dyn
                Nonlinear Dyn
                Nonlinear Dynamics
                Springer Netherlands (Dordrecht )
                0924-090X
                1573-269X
                3 November 2020
                : 1-7
                Affiliations
                National Laboratory of Scientific Computation, Petrópolis, Rio de Janeiro Brazil
                Author information
                http://orcid.org/0000-0002-3732-6034
                Article
                6056
                10.1007/s11071-020-06056-w
                7606032
                33162673
                97c3a6c5-cf1d-4a56-afb1-712479020fb5
                © Springer Nature B.V. 2020

                This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

                History
                : 20 July 2020
                : 24 October 2020
                Funding
                Funded by: Conselho Nacional de Desenvolvimento Científico e Tecnológico (BR)
                Award ID: 301330/2020-4
                Award Recipient :
                Categories
                Original Paper

                model gompertz,minimal error method,inverse problem,covid-19

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