Spatio-temporal information and knowledge representation of disease incidence and respective intervention strategies.

The supplementation of medical data with environmental data offers rich new insights that can improve decision-making within health systems and the healthcare profession. In this study, we simulate disease incidence for various scenarios using a mathematical model. We subsequently visualise the infectious disease spread in human populations over time and geographies. We demonstrate this for malaria, which is one of the top three causes of mortality for children under the age of 5 years in sub-Saharan Africa, and its associated interventions within Kenya. We demonstrate how information can be collected, analysed, and presented in new ways to inform key decision makers in understanding the prevalence of disease and the response to interventions.