Estimating the size of COVID-19 epidemic outbreak

Chakrit Pongkitivanichkul; Daris Samart; Takol Tangphati; Phanit Koomhin; Pimchanok Pimton; Punsiri Dam-O; Apirak Payaka; Phongpichit Channuie

In this section, we concluded our findings on analyzing the epidemic data of cumulative infected cases from many countries as reported by WHO. In terms of a renormalisation group approach, we considered the RG-inspired logistic growth function, a.k.a. the power-law logistic growth function. The non-linear least squares regression is performed to obtain parameters from the model. We computed the uncertainty for model parameters using the squared root of the corresponding diagonal components of the covariance matrix. We carefully divided countries under consideration into 2 categories based on the estimation of the inflection point, t 0 : the maturing phase and the growth-dominated phase. We found that the outbreak has happened in a large scale and passed beyond the inflection point include China and Korea, while countries where the situation has already passed the inflection point but has not reached the plateau yet are Austria, Belgium, Japan, and Norway with Japan being close to the asymptotic number of infected cases. We observed that long-term estimations of all countries in the maturing phase for both n = 1 and n = 1 are close to each other. Based on the values of root-mean-square error, the RG-inspired logistic model is slightly preferable. Our further investigation shows that n characterizes an early stage of the epidemic. n < 1 represents countries with a strong measure at the beginning of the outbreak, where n > 1 indicates an uncontrollable event.