To infer information about the spatial distribution of health, GIS are a powerful tool. Health and ill-health are influenced by environmental as well as socio-economic variables. Many correlations between these and health have been identified and catechized using GIS, such as the distribution of hazardous blood lead concentrations  (Aboh et al., 2013) or access to health and socio-economic status (Brunsdon et al., 2011). Spatial epidemiology aims to explain and predict the spreading and spreading rates of diseases such as the infectious E. coli virus called EHEC (Kistemann et al., 2004). In the times of the global corona pandemic, the modelling of the disease distribution is more relevant than ever, and governments base their measures on scientific epidemiology models.

Useful spatial data is often found in census data or measurements of environmental factors. For some studies, surveys are conducted to collect information about perceived status and health access. Even remotely sensed data, e.g. on air quality inferred from hyperspectral satellite imagery, can be used in health geography.

References:
Brunsdon C., Comber Alexis J, & Radburn R. (2011). A spatial analysis of variations in health access: linking geography, socio-economic status and access perceptions. International Journal of Health Geographics, 1, 44.

Innocent Joy Kwame Aboh, P., Manukure Atiemo Sampson, Mp., Leticia Abra-Kom Nyaab, Mp. N., Jack Caravanos, D. C., Francis Gorman Ofosu, P., & Harriet Kuranchie-Mensah, Mp. (2013). Assessing Levels of Lead Contamination in Soil and Predicting Pediatric Blood Lead Levels in Tema, Ghana. Journal of Health and Pollution, 7.

Kistemann T., Zimmer S., Vågsholm I., & Andersson Y. (2004). GIS-Supported Investigation of Human EHEC and Cattle VTEC O157 Infections in Sweden: Geographical Distribution, Spatial Variation and Possible Risk Factors. Epidemiology and Infection, 132(3), 495.