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

The coronavirus disease 2019 (COVID-19) caused by a novel coronavirus SARS-CoV-2 that emerged in the city of Wuhan, China, last year and has since expanded to a large scale COVID-19 epidemic and spread to all regions as reported by the World Health Organization (WHO) [1] causes a serious situation worldwide. Since the first reports of COVID-19, there are various attempts to estimate the final size of the spread mathematically. Among several approaches [2] [3] [4] [5] [6] [7] [8] [9] , the logistic growth models are very popular and useful to predict the final size of the epidemic [10, 11] . It is worth noting that the logistic function or logistic curve is a common "S" shape (Sigmoid curve). Logistic functions are often used in statistics and machine learning, medicine, chemistry, physics, material science, linguistics, and even agriculture. However its root somehow seems to be coincidentally originated from the high energy physics and condense matter physics' point of view. In terms of the renormalisation group (RG) framework, we define the logistic growth function as an epidemic strength function allowing us to describe the spread of disease. Its derivative with respect to time yields a new quantity which can be interpreted as the beta function of an underlying microscopic model. This frame work was first proposed in Ref. [12] to describe the underlying dynamics of disease spread by invoking the usual logistic-growth model.