| Paper ID | SS-4.4 |
| Paper Title |
LEVERAGING A MULTIPLE-STRAIN MODEL WITH MUTATIONS IN ANALYZING THE SPREAD OF COVID-19 |
| Authors |
Anirudh Sridhar, Princeton University, United States; Osman Yagan, Carnegie Mellon University, United States; Rashad Eletreby, Walmart Labs, United States; Simon Levin, Princeton University, United States; Joshua Plotkin, University of Pennsylvania, United States; H. Vincent Poor, Princeton University, United States |
| Session | SS-4: Data Science Methods for COVID-19 |
| Location | Gather.Town |
| Session Time: | Tuesday, 08 June, 16:30 - 17:15 |
| Presentation Time: | Tuesday, 08 June, 16:30 - 17:15 |
| Presentation |
Poster
|
| Topic |
Special Sessions: Data Science Methods for COVID-19 |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
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. |
