Graduation Year


Document Type




Degree Name

Doctor of Philosophy (Ph.D.)

Degree Granting Department

Industrial and Management Systems Engineering

Major Professor

Tapas K. Das, Ph.D.

Co-Major Professor

Chaitra Gopalappa, Ph.D.

Committee Member

Ankit Shah, Dr.P.H.


Impact assessment, pandemic preparedness, intervention analysis, epidemic mitigation, public health policy


Disease outbreaks caused by existing and emerging pathogens pose a serious threat to local and global communities in the form of epidemics and pandemics, respectively. The 2009 H1N1 pandemic and the 2019 COVID-19 pandemic are exemplars of how underprepared both the developed and developing nations are at mitigating pandemics.

Mathematical modeling and simulation of disease outbreaks has served as a powerful tool to understand disease transmission dynamics. They also aid in developing effective intervention strategies. However, existing models are usually particularized to a region, or a specific disease pathogen and interventions used in these models may not translate well to novel outbreaks or existing epidemics in another region. This could be due to the dynamic nature of evolving population demographics and disease parameters. Fully understanding the time-varying transmission dynamics of diseases, the effect of disease outbreaks on varying population demographics, the impact of effective interventions (both pharmaceutical and non-pharmaceutical), is imperative for epidemic and pandemic preparedness.

The goal of this dissertation is to propose frameworks and methodologies that model epidemics or pandemics and implement them to derive useful insights for intervention strategies that effectively mitigate disease burden in a population. Simulation models such as agent-based (AB) and compartmental approaches are implemented to closely follow the epidemic outcomes in regional outbreaks. The modeling frameworks presented in this dissertation have been used to simulate both novel disease outbreaks (COVID-19) and ongoing regional epidemics (HIV/AIDS). Combinations of varying levels of intervention measures are applied within the models to assess the impact of the interventions, and the changes in trends of epidemic outcomes. Information and insights derived from these models aim to help policy makers with informed decision-making and improve public health metrics during an epidemic.

Included in

Engineering Commons