Speaker: Douglas Alem, University of Edinburgh Business School
Date & Time: September 30, 2022, Friday 13:30
Place: EA-409
Title: Long-lasting insecticidal nets campaigns for malaria control considering prioritization and equity
Abstract: Malaria is still a major health concern in the North region of Brazil. The tropical weather combined with its poor environmental and socioeconomic aspects creates favorable conditions for the cycle of malaria transmission in this area, which accounts for 99% of malaria cases in the country. One of the most effective strategies to prevent malaria is based on the use of long-lasting insecticidal nets (LLINs or simply bed nets) among people in malaria-endemic regions. A critical challenge in public health is to ensure that LLINs are effectively and equitably allocated to those that need them the most. In this talk, we show some promising mathematical programming approaches that can help health organizations to allocate and distribute LLINs. In particular, we show how to evaluate a Malaria Vulnerability Index (MVI) to identify which areas are more prone to malaria and a simple way to factor this index within a mathematical programming approach to prioritize LLINs allocation.
Bio: Douglas Alem is currently Associate Professor in Business Analytics at the University of Edinburgh Business School and Director of MSc Decision and Data Analytics (online). From 2011 to 2018, Dr Alem was an assistant professor of industrial engineering at University of Sao Carlos (UFSCar). He joined the University of Edinburgh Business School as an assistant professor in 2018. In 2011, Dr. Alem earned a PhD in Applied Mathematics and Computer Science at University of Sao Paulo
(USP) with a thesis focusing on robust optimization and stochastic programming approaches to production planning problems under uncertainty. His main research interests include humanitarian logistics & disaster management, as well as applications of robust optimization and stochastic programming to different problems. Currently, he is working on data-driven approaches for robust stochastic optimization and on equity considerations in food aid distribution to vulnerable people.
