COVID-19 epidemic outcome predictions based on logistic fitting and estimation of its reliability

Abstract

Since the first outbreak of the COVID-19 epidemic at the end of 2019, data has been made available on the number of infections, deaths and recoveries for all countries of the World, and that data can be used for statistical analysis. The primary interest of this paper is how well the logistic equation can predict the outcome of COVID-19 epidemic in any regions of the World assuming that the methodology of the testing process, namely the data collection method and social behavior is not changing over the course of time. Besides the social relevance, this study has two scientific purposes: we investigate if a simple saturation model can describe the trend of the COVID-19 epidemic and if so, we would like to determine, from which point during the epidemic the fitting parameters provide reliable predictions. We also give estimations for the outcome of this epidemic in several countries based on the logistic model and the data available on 27 March, 2020. Based on the saturated cases in China, we have managed to find some criteria to judge the reliability of the predictions.

15 pages, 6 figure, 1 long table

References in corpus (6)

• Solvable delay model for epidemic spreading: the case of Covid-19 in Italy
• Predicting the ultimate outcome of the COVID-19 outbreak in Italy
• Short-term predictions of country-specific Covid-19 infection rates based on power law scaling exponents
• A simplified model for expected development of the SARS-CoV-2 (Corona) spread in Germany and US after social distancing
• Prediction of number of cases expected and estimation of the final size of coronavirus epidemic in India using the logistic model and genetic algorithm
• On the Evolution of Covid-19 in Italy: a Follow up Note

Cited by in corpus (4)

• Artificial Intelligence (AI) and Big Data for Coronavirus (COVID-19) Pandemic: A Survey on the State-of-the-Arts
• Backtesting the predictability of COVID-19
• The COVID-19 pandemic: growth patterns, power law scaling, and saturation
• Morphology and numerical characteristics of epidemic curves for SARS-Cov-II using Moyal distribution