Climate data in infectious disease modelling

Three sessions on using climate data in infectious disease transmission modelling, a case study of malaria in Thailand

These three sessions describe how one could use climate data in infectious disease transmission modelling. Rather than a fixed curriculum, they walk through the ideas and a practical workflow (from the underlying climate science, through obtaining and manipulating climate data, to fitting a model and projecting forward) using malaria in Thailand as a running case study.

The three sessions

The material is organised into three sessions:

Session 1: Climate science primer. A remote, slide-based introduction to the physics of the climate system, reanalysis products, CMIP projections, emulators, and how these feed into risk assessments. Delivered as a presentation; the slides are linked from the Session 1 page.

Session 2: Obtaining, manipulating, and fitting climate data. A hands-on practical, in R, building the end-to-end workflow: obtaining malaria case data and climate data (reanalysis and future projections), summarising climate to the administrative units used for surveillance, linking climate to cases, and fitting a statistical model that relates climate to malaria incidence (with projections under future scenarios).

Session 3: Future directions and project planning. Mostly breakout-group discussion on what participants could and should be working on with climate data in their own contexts. A mechanistic climate-forced transmission model is included as an optional, more advanced practical.