A recursive bifurcation model for early forecasting of COVID-19 virus spread in South Korea and Germany

Julia Shen

doi:10.1038/s41598-020-77457-5

Abstract

Early forecasting of COVID-19 virus spread is crucial to decision making on lockdown or closure of cities, states or countries. In this paper we design a recursive bifurcation model for analyzing COVID-19 virus spread in different countries. The bifurcation facilitates recursive processing of infected population through linear least-squares fitting. In addition, a nonlinear least-squares fitting procedure is utilized to predict the future values of infected populations. Numerical results on the data from two countries (South Korea and Germany) indicate the effectiveness of our approach, compared to a logistic growth model and a Richards model in the context of early forecast. The limitation of our approach and future research are also mentioned at the end of this paper.

Highlights

Forecasting of COVID-19 virus spread is crucial to decision making on lockdown or closure of cities, states or countries
Coronaviruses are a group of RNA viruses; the earliest study on animal coronavirus was reported in the late 1920s8, and human coronavirus was first studied by Kendall, Bynoe and Tyrell in 1960s through extracting the viruses from patients who suffered from common c olds[9,10]
We propose a recursive bifurcation approach for early forecasting of COVID-19 virus spread

Summary

Introduction

Forecasting of COVID-19 virus spread is crucial to decision making on lockdown or closure of cities, states or countries. This calls for an accurate early forecasting model for the ongoing spread of COVID-19 virus. There have been many recent studies with respect to the COVID-19 virus spread, an accurate forecasting model for the virus spread based on data at a very early time point is still elusive.

Results

Conclusion