Assessing the effectiveness of countermeasures against the spread of COVID-19 through a new mathematical model
Abstract
Started in December 2019 in Wuhan, China, the novel coronavirus (known to cause a respiratory disease known as COVID-19) has spread fast and broad and has since been devastating the entire world population. The World Health Organization has identified the spread of COVID-19 as a pandemic and millions of individuals have been infected with hundreds of thousands dying of the disease. In addition, the spread of the virus and the countermeasures taken against it have severely impacted the economy with industries such as tourism, travel, entertainment suffering the most. Schools at all levels are being closed in several countries around the world and highly anticipated events including the Tokyo 2020 Summer Olympics and Euro 2020 Championship have been canceled for this year. In summary, the spread of COVID-19 has been one of the most devastating events that affect the health and well-being of humans all around the world. A key scientific goal concerning COVID-19 is to develop mathematical models that help understand and predict its spreading behavior, as well as to provide guidelines on what can be done to limit its spread. With tight restrictions on traveling or even going outside of homes already in place in most jurisdictions, an important question is the order and time in which these restrictions can be safely eliminated. This project aims to help answer these questions by augmenting the existing models of epidemic spread by leveraging the mathematical theory developed in PIsÕ recent work. There, we studied a class of spreading processes (including information propagation in online social networks and virus propagation) under a multiple-strain model with mutations; i.e., a mutation may take place at each host leading to a different strain of the virus/information with different transmissibility from the originally acquired strain. This work bridges the disconnect between how spreading processes propagate and evolve in real life and the current mathematical and simulation models that ignore evolutionary adaptations. Our results are shown to predict accurately the epidemic threshold, expected epidemic size, and the expected fraction of individuals infected by each strain in this model. Our key finding is that classical epidemic models that do not consider the evolution of the strain lead to incorrect predictions on the spreading dynamics when mutation is present. This project aims to leverage our recent findings to i) help better understand the future spreading behavior of COVID-19; ii) help assess the effectiveness of various countermeasures taken by the authorities to slow down the spread of the virus under different scenarios; and iii) help assess the potential outcomes of removing the restrictions. At the core of our approach is to harness our findings and expertise from information sciences to help improve the current mathematical and simulation models of epidemics that do not capture evolution. We believe these results will thus have a broad impact in the current health crises as well as the future ones.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jul 09, 2020
- Source ID
- W911NF2010204
Entities
People
- Osman Yagan
Organizations
- Army Contracting Command
- Massachusetts Institute of Technology
- United States Army