COVID-19: Modeling, Prediction, and Control

• Main Authors: Ahmad Bani Younes, Zeaid Hasan
• Format: Article
• Language: English
• Published: MDPI AG 2020-05-01
• Series: Applied Sciences
• Subjects: coronavirus; Lotka-Volterra; Kalman Filter; modeling; epidemic
• Online Access: https://www.mdpi.com/2076-3417/10/11/3666

The newly emerging severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), also known as COVID-19, has been recognized as a pandemic by the World Health Organization (WHO) on 11th March 2020. There are many unknowns about this virus to date and no vaccine or conclusive treatment due to the lack of understanding of its pathogenesis and proliferation pathways which are unknown and cannot be traced. The prime objective is to stop its spread worldwide. This article aims to provide predictions of its spread using a stochastic Lotka–Volterra model coupled with an extended Kalman Filter (EKF) algorithm to model the COVID-19 dynamics. Our results show the feasibility of utilizing this model for predicting the spread of the virus and the ability of different control measures (e.g., social distancing) on reducing the number of affected people.