Mathematical modelling of antimalarial drug resistance

The effect of malaria on the developing world is devastating.  Each year there are
more than 200 million cases and over 400,000 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 us being able to
identify, diagnose and treat the disease appropriately.   
  

In this talk, I will introduce several statistical and mathematical models for monitoring the
emergence and spread of antimalarial drug resistance. Results will be presented from
a geostatistical model that have generated spatio-temporal predictions of resistance based
on prevalence data. These data are available only at discrete study locations and times. In
this way, I will explain how the model output provides new insight into the spread of drug
resistance and unveils new information that existing techniques cannot provide.  I will then
discuss how the results of these models have been used to update public health policy,
showing how mathematics can help to inform strategies against malaria.