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.
Subject
Applied Mathematics,Modeling and Simulation,Statistics and Probability
Reference27 articles.
1. How will country-based mitigation measures influence the course of the COVID-19 epidemic;Anderson;Lancet,2020
2. COVID-19 in South Korea;Choi;Postgrad Med J,2020
3. Innovative screening tests for COVID-19 in South Korea;Choi;Clin Exp Emerg Med,2020
4. Mathematical prediction of the time evolution of the COVID-19 pandemic in Italy by a Gauss error function and Monte Carlo simulations;Ciufolini;Eur Phys J Plus,2020
5. Eberhardt, J. N., Breuckmann, N. P., & Eberhardt, C. S. (2020). Multi-Stage Group Testing Improves Efficiency of Large-Scale COVID-19 Screening. J Clin Virol, S1386-6532(20)30124-4.
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献