Analysis and Prediction for Confirmed COVID-19 Cases in Czech Republic with Uncertain Logistic Growth Model

Wenjian Liu,Chunxiao Ding

doi:10.3390/sym13122264

Abstract

This paper presents an uncertain logistic growth model to analyse and predict the evolution of the cumulative number of COVID-19 infection in Czech Republic. Some fundamental knowledge about the uncertain regression analysis are reviewed firstly. Stochastic regression analysis is invalid to model cumulative number of confirmed COVID-19 cases in Czech Republic, by considering the disturbance term as random variables, because that the normality test and the identical distribution test of residuals are not passed, and the residual plot does not look like a null plot in the sense of probability theory. In this case, the uncertain logistic growth model is applied by characterizing the disturbance term as uncertain variables. Then parameter estimation, residual analysis, the forecast value and confidence interval are studied. Additionally, the uncertain hypothesis test is proposed to evaluate the appropriateness of the fitted logistic growth model and estimated disturbance term. The analysis and prediction for the cumulative number of COVID-19 infection in Czech Republic can propose theoretical support for the disease control and prevention. Due to the symmetry and similarity of epidemic transmission, other regions of COVID-19 infections, or other diseases can be disposed in a similar theory and method.

Highlights

The pandemic COVID-19 remains a challenge globally, which gives rise to seriously threatens of human health, economic losses and social panic in different degree
Stochastic regression analysis is invalid to model cumulative number of confirmed COVID19 cases in Czech Republic, because that the normality test (Lilliefors test) and the identical distribution test (Kolmogorov–Smirnov test) of residuals are not passed, and the residual plot does not look like a null plot in the sense of probability theory
An uncertain logistic growth model was applied to formulated the cumulative number of COVID-19 data in Czech Republic

Summary

Introduction

The pandemic COVID-19 remains a challenge globally, which gives rise to seriously threatens of human health, economic losses and social panic in different degree. All above researches have studied and analysed the cumulative number of COVID-19 infections in China by using the uncertainty theory, from the angle of uncertain differential equation, uncertain regression analysis and uncertain time series analysis In this manuscript, the cumulative number of COVID-19 infections in Czech Republic is dealt with initially by using an uncertain logistic growth model. Parameter estimation, the forecast value, confidence interval, and the uncertain hypothesis test are used to analyse and predict the evolution of the cumulative number of confirmed COVID-19 infections in Czech Republic. Stochastic regression analysis is invalid to model cumulative number of confirmed COVID19 cases in Czech Republic, because that the normality test (Lilliefors test) and the identical distribution test (Kolmogorov–Smirnov test) of residuals are not passed, and the residual plot does not look like a null plot in the sense of probability theory. Other regions of COVID-19 infections, or other diseases can be analysed and forecasted by using the uncertain logistic growth model due to the symmetry and similarity

Uncertain Logistic Growth Model

Parameter Estimation and Residual Analysis

Forecast

Uncertain Hypothesis Test

Data and Model

Discussion