Data-driven spatiotemporal mathematical models to support malaria elimination

Leah Edelstein-Keshet Prize

Introduced by: Ellen Baake, President of ESMTB

Wednesday, June 16 at 01:15am (PDT)
Wednesday, June 16 at 09:15am (BST)
Wednesday, June 16 05:15pm (KST)

Share this

Plenary-06 : Leah Edelstein-Keshet Prize

Jennifer A. Flegg

Associate Professor in Applied mathematics in the School of Mathematics and Statistics
University of Melbourne, Australia


The effect of malaria on the developing world is devastating. Each year there are more than 200 million cases and over 400000 deaths, with children under the age of five the most vulnerable. Ambitious malaria elimination targets have been set by the World Health Organization for 2030. These involve the elimination of the disease in at least 35 countries. However, these malaria elimination targets rest precariously on being able to treat the disease appropriately; a difficult feat with the emergence and spread of antimalarial drug resistance. In this talk, I will introduce several statistical and mathematical models that are being used to help support malaria elimination, including monitoring the emergence and spread of antimalarial drug resistance. Results will be presented from a Bayesian geostatistical model that have generated spatiotemporal predictions of resistance based on prevalence data available only at discrete study locations and times. In this way, the model output provides insight into the spatiotemporal spread of resistance that the discrete data points alone cannot provide. I will discuss how the results of these models have been used to update public health policy and support ongoing malaria elimination efforts.

Hosted by SMB2021 Follow
Virtual conference of the Society for Mathematical Biology, 2021.