Explainable Machine Learning for Predicting Malaria Incidence and Severity in Ghana
When
Oct. 28, 2026, 12pm to 1pm
Event status:
This event has ended
Campus Location
Office/Remote Location
318
Description
Join us for a special lecture from Kwame Obeng!
Using real district-level malaria data from 261 districts across Ghana from 20212025, Obeng will examine the geographic distribution of malaria cases and interpolate between locations. Statistical analyses will be carried out to identify significant clusters of malaria cases. His work demonstrates how spatial patterns in malaria burden can help inform public health decision-making.
(Please note: This is an in-person seminar.)
Admission Information
This event is open to all faculty, staff, and student. No registration required.