Project title: SCH: INT: Simulation and decision-analysis algorithms for integrated modeling of diseases- A Healthy Lives For All approach
Student open positions:
Graduate student open positions? YES (contact Prof Gopalappa at firstname.lastname@example.org) Position title: PhD student / Research Assistant Suitable candidates: MS or BS degree in industrial engineering, mathematics, computer science, or similar fields. Proficiency in mathematical and computational modeling, and interest in public health and policy. Interest in working with and advised by a multi-disciplinary team from industrial engineering, computer science, social epidemiology, economics, public health, and fiscal policy from UMass Amherst, Harvard, Centers for Disease Control and Prevention, and World Health Organization. Details of the project are in the following sections. Preferred start date: Spring 2020 or Fall 2020 Contact: Send an email to Dr. Gopalappa at email@example.com with your CV and statement of interest.
Undergraduate student open positions? YES (contact Dr. Gopalappa at firstname.lastname@example.org) Suitable candidates: BS students at UMass- Amherst interested in doing an independent study, or Honors thesis in topics related to this project. Contact: Send an email to Prof Gopalappa at email@example.com with your CV and one paragraph statement of interest.
National Science Foundation LINK January 1, 2020 to December 31, 2023 Principal Investigator: Dr. Chaitra Gopalappa firstname.lastname@example.org Co-Principal Investigators: UMass - Dr. Peter Haas, Computer Science; Dr. Dean Robinson, Political Science; Dr. Hari Balasubramanian, Industrial Engineering; Harvard - Dr. Jagpreet Chhatwal, Decision Sciences Collaborators and advisory members: Centers for Disease Control and Prevention, World Health Organization
It is generally accepted that social conditions, including access to essential public services, early childhood development and education, economic and food security and environmental conditions, are important determinants of individual health. Referred to collectively as Social Determinants of Health (SDOH), research in social epidemiology has shown that structural interventions aimed at improving SDOH can prevent diseases, thereby improving well-being. This project develops mathematical models and decision-support methods that enable economic analysis of structural interventions as part of an overall public health strategy.
Develop a framework for integrated multi-disease prediction and decision analysis modeling to capture the syndemic nature of diseases. As a test case, we will use high burden diseases in the US, including infectious diseases transmitted through sex or needle sharing, specifically, HIV, HPV, HBV, and HCV, and cancers of the cervix, liver, breast, and colorectal. Specific aims are to: 1) develop a computational framework for integrated multi-disease prediction modeling; 2) develop new methodologies for parameterizing the natural progression of interacting communicable and non-communicable diseases in a population influenced by socioeconomic and demographic factors; and 3) develop a dynamic decision-analytic model for evaluation of structural interventions.
System dynamics, machine learning, graph theory, numerical parametric optimization, and simulation-based control optimization.