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

TL;DR

This paper evaluates the effectiveness of logistic models in predicting COVID-19 epidemic outcomes across different regions, analyzing data reliability and establishing criteria for prediction confidence.

Contribution

It demonstrates the applicability of a simple saturation model for COVID-19 trend prediction and introduces criteria for assessing the reliability of early epidemic predictions.

Findings

Logistic model fits well to COVID-19 data in various countries.

Reliable predictions can be made after certain epidemic stages.

Criteria for prediction reliability are proposed based on Chinese data.

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.