Pandemic modeling and planning

During a pandemic disease outbreak, it is important to have an accurate estimate of the number and location of individuals affected. The efforts to do so require a multi-disciplinary collaboration since most of the factors involved are interrelated. Much of the research in this area is dedicated to obtaining the same objectives using homogenous mixing models, where the affected population is classified into three groups; susceptible, infectious and removed. However, to better understand the properties of an outbreak, there are more factors to be explained than the estimated number alone. It is also important to obtain information regarding the area where the disease may spread, the risk levels in the different parts of that area and the possible direction it may continue to spread. Other factors to be considered are the transmission mode of the disease and the role of public facilities such as public transport and buildings, e.g., offices and grocery stores, in the disease transmission. Additional concerns include addressing pedestrian foot traffic as well as children and transient populations.

This research uses agent-based simulation models and contact network models to obtain the estimate the spread of disease through a population generated using data from census, public transportation, school system, etc. sources. We build algorithms to optimize public health mitigation strategies that account for both resource costs and societal costs based on simulation outcomes. We also apply graph theory concepts, specifically focusing on the critical node detection problem (CNDP), to identify optimal individuals in the population to vaccinate. We then use rule-mining techniques to extract actionable vaccination prioritization policies. We further develop machine learning and statistical approaches to predict simulation outcomes and display them in a user-friendly interface to facilitate knowledge translation.

Hanin Afzal
Hanin Afzal
MASc student