March 17, 2023
Structured decision making with geospatial modelling outputs with Lucy Harrison
Disease surveillance aims to collect data at different times or locations, to improve our understanding of where the disease is or to assist public health authorities to distribute control measures, vaccines and medical resources. As such, the range of possible aims of disease surveillance is diverse. When the spatial distribution of a disease is uncertain, a statistical model can be used to estimate it. This talk outlines the application of structured decision making to the selection of sites for surveillance of Plasmodium knowlesi malaria in western Indonesia. P. knowlesi is a simian strain of malaria that is the most common cause of the disease in Malaysia and is increasingly identified elsewhere in Southeast Asia. In spite of this, surveillance outside of Malaysia is sparse and the spatial extent of transmission is uncertain. I outline a flexible workflow to support selection of primary healthcare centres for surveillance of P. knowlesi malaria. The use of these methods ensures the quantitative involvement of statistical modelling outputs in policy decisions.