Date/Time
Date(s) - 28/09/2022
3:45 pm - 5:15 pm

Speaker: Steve Cygu

Topic: Computational inference and prediction in public health

Date of Presentation: Wednesday, September 28, 2022

Location:  BSB 121 and online

Abstract

Using computational approaches utilizing large datasets to investigate public health information is an important mechanism for institutions seeking to identify strategies for improving public health. The art in computational approaches, for example in health research, is managing the trade-offs between the two perspectives: first, inference, and second, prediction. Many techniques from statistical methods (SM) and machine learning (ML) may, in principle, be used for both perspectives. However, SM has a well-established focus on inference by building probabilistic models, which allows us to determine a quantitative measure of confidence about the magnitude of the effect. Simulation-based validation approaches can be used in conjunction with SM to explicitly verify assumptions and redefine the specified model, if necessary. On the other hand, ML uses general-purpose algorithms to find patterns that best predict the outcome and makes minimal assumptions about the data-generating process; and may be more effective in a number of situations. My work employs both SM- and ML-based computational approaches to investigate particular public health problems.