Leveraging A Multiple-Strain Model with Mutations in Analyzing the Spread of Covid-19

• Published in: ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp. 8163 - 8167
• Main Authors: Sridhar, Anirudh, Yagan, Osman, Eletreby, Rashad, Levin, Simon A., Plotkin, Joshua B., Poor, H. Vincent
• Format: Conference Proceeding
• Language: English
• Published: IEEE 06.06.2021
• Subjects: Adaptation models; Analytical models; COVID-19; Data models; epidemic modeling; Limiting; mask-wearing; mutations; Pandemics; Simulation
• ISSN: 2379-190X
• DOI: 10.1109/ICASSP39728.2021.9414595

The spread of COVID-19 has been among the most devastating events affecting the health and well-being of humans worldwide since World War II. A key scientific goal concerning COVID-19 is to develop mathematical models that help us to understand and predict its spreading behavior, as well as to provide guidelines on what can be done to limit its spread. In this paper, we discuss how our recent work on a multiple-strain spreading model with mutations can help address some key questions concerning the spread of COVID-19. We highlight the recent reports on a mutation of SARS-CoV-2 that is thought to be more transmissible than the original strain and discuss the importance of incorporating mutation and evolutionary adaptations (together with the network structure) in epidemic models. We also demonstrate how the multiple-strain transmission model can be used to assess the effectiveness of mask-wearing in limiting the spread of COVID-19. Finally, we present simulation results to demonstrate our ideas and the utility of the multiple-strain model in the context of COVID-19.