COVID-19 Outbreak Prediction with Machine Learning

Sina F. Ardabili; Amir MOSAVI; Pedram Ghamisi; Filip Ferdinand; Annamaria R. Varkonyi-Koczy; Uwe Reuter; Timon Rabczuk; Peter M. Atkinson

To elaborate on the effectiveness of enforcing such assumptions understanding standard dynamic epidemiological (e.g., susceptible-infected-recovered, SIR) models is essential [8] . The modeling strategy is formed around the assumption of transmitting the infectious disease through contacts, considering three different classes of well-mixed populations; susceptible to infection (class S), infected (class I), and the removed population (class R is devoted to those who have recovered, developed immunity, been isolated or passed away). It is further assumed that the class I transmits the infection to class S where the number of probable transmissions is proportional to the total number of contacts [9] [10] [11] . The number of individuals in the class S progresses as a time-series, often computed using a basic differential equation as follows: