Scaling simulations in population health via machine learning

Computer simulations provide a safe alternative to taking a trial-and-error approach in the real-world. If a simulating intervention is found to be inefficient or even harmful, then it can be canceled without causing harm to real individuals. Consequently, simulations are increasingly sought after for complex social problems such as homelessness and the spread of the Human Immunodeficiency Virus (HIV). However, such complex problems can be tackled using many different interventions (e.g., increasing shelters for homelessness), each being defined by several parameters (e.g., number of beds). Simulating all possible interventions and their parameter values is prohibitive; there are too many combinations to simulate, and each simulation can take a long time to complete when the computer model is highly detailed. TO BE CONT’D

Faculty Supervisor:

Vijay Mago

Student:

Partner:

Miami University

Discipline:

Computer science

Sector:

Education

University:

Lakehead University

Program: