This research proposes to determine optimal treatment strategies, through the development of population dynamical models for disease transmission and control, which can minimize the effect of resistance emergence in the population. This work will primarily focus on influenza infection, which still inflicts substantial morbidity, mortality, and socioeconomic costs worldwide.
Postdoctoral fellow: Dr. Majid Jaberi-Douraki, Mathematics and Statistics, York University
Lead faculty member: Dr. Seyed Moghadas, Mathematics and Statistics, York University
A major pharmaceutical intervention for management of many infectious diseases is the use of antiviral drugs. However, the rise of drug resistance poses significant threats to the effectiveness of drugs. This research proposes to determine optimal treatment strategies, through the development of population dynamical models for disease transmission and control, which can minimize the effect of resistance emergence in the population. This work will primarily focus on influenza infection, which still inflicts substantial morbidity, mortality, and socioeconomic costs worldwide. We will develop mathematical models that include within-host infectious dynamics and resistance emergence in the context of disease spread in the population. Determining optimal treatment strategies is generally a challenging problem and requires the application of more sophisticated mathematical techniques, such as the use of control theory. Findings of this research can provide critical information for policymakers to optimize health decisions for antiviral strategies to achieve maximum protection of community health.