Can we predict the occurrence of COVID-19 cases? Considerations using a simple model of growth

Fábio A M Cássaro; Luiz F Pires

doi:10.1016/j.scitotenv.2020.138834

Can we predict the occurrence of COVID-19 cases? Considerations using a simple model of growth

Sci Total Environ. 2020 Aug 1:728:138834. doi: 10.1016/j.scitotenv.2020.138834. Epub 2020 Apr 20.

Authors

Fábio A M Cássaro¹, Luiz F Pires²

Affiliations

¹ Laboratory of Physics Applied to Soils and Environmental Sciences, Department of Physics, State University of Ponta Grossa (UEPG), 84.030-900 Ponta Grossa, PR, Brazil. Electronic address: fcassaro@uepg.br.
² Laboratory of Physics Applied to Soils and Environmental Sciences, Department of Physics, State University of Ponta Grossa (UEPG), 84.030-900 Ponta Grossa, PR, Brazil.

Abstract

This study aimed to present a simple model to follow the evolution of the COVID-19 (CV-19) pandemic in different countries. The cumulative distribution function (CDF) and its first derivative were employed for this task. The simulations showed that it is almost impossible to predict based on the initial CV-19 cases (1st 2nd or 3rd weeks) how the pandemic will evolve. However, the results presented here revealed that this approach can be used as an alternative for the exponential growth model, traditionally employed as a prediction model, and serve as a valuable tool for investigating how protective measures are changing the evolution of the pandemic.

Keywords: Coronavirus; Cumulative distribution function; Pandemic; SARS-CoV-2.

MeSH terms

Betacoronavirus
COVID-19
Computer Simulation
Coronavirus Infections / epidemiology*
Forecasting / methods*
Humans
Models, Theoretical*
Pandemics
Pneumonia, Viral / epidemiology*
SARS-CoV-2