Affiliations: Department of Biotechnology, Institute of Health Sciences, Federal University of Bahia, Av. Reitor Miguel Calmon, sn, Vale do Canela, Salvador, BA, Brazil | Tel.: +55 71 98175 5469; E-mail: [email protected]
Correspondence:
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Corresponding author: Department of Biotechnology, Institute of Health Sciences, Federal University of Bahia, Av. Reitor Miguel Calmon, sn, Vale do Canela, Salvador, BA, Brazil. Tel.: +55 71 98175 5469; E-mail: [email protected].
Abstract: COVID-19 is a severe acute respiratory syndrome caused by the new Coronavirus. COVID-19 outbreak is a Public Health Emergency of International Concern, declared by WHO, that killed more than 2 million people worldwide. Since there are no specific drugs available and vaccination campaigns are in the initial phase, or even have not begun in some countries, the main way to fight the outbreak worldwide is still based on non-pharmacological strategies, such as the use of protective equipment, social isolation and mass testing. Modeling of the disease epidemics have gained pivotal importance to guide health authorities on the decision making and applying of those strategies. Here, we present the use of the Weibull distribution to model predictions of the COVID-19 outbreak based on daily new cases and deaths data, by non-linear regression using Metropolis-Markov Chain Monte Carlo simulations. It was possible to predict the evolution of daily new cases and deaths of COVID-19 in many countries as well as the overall number of cases and deaths in the future. Modeling predictions of COVID-19 pandemic may be of importance on the evaluation of governments and health authorities mitigation procedures, since it allows one to extract parameters that may help to guide those decisions and measures, slowing down the spread of the disease.
Keywords: COVID-19, Weibull distribution, modelling, model, death toll
