Public health needs GIScience (like now!)

During the last 20 years we have seen the re-emergence of diseases; emergence of new diseases in new locations and witnessed outbreaks of varying intensity and duration. Spatial epidemiology plays an important role in understanding the patterns of disease and how they change over time and across space.

The aim of this paper is to bring together a public health and geospatial data science perspective to provide a framework that will facilitate the integration of geographic information and spatial analyses at different stages of public health response so that these data and methods can be effectively used to enhance surveillance and monitoring, intervention strategies (planning and implementation of a response) and facilitate both short- and long-term forecasting.

To demonstrate elements of this framework and how it can be utilized, we selected three case studies ranging from the current the global COVID-19 Coronavirus pandemic of 2020 to more historical examples such as the John Snow Cholera outbreak of 1854 and the Ebola outbreak of 2014 in West Africa.

A variety of methods including spatial descriptive statistics, as well as methods for analysing patterns were used. The examples we provide can reveal sources of infection, connectivity between locations, delineate zones of containment and show the spread of an outbreak globally and locally across space and time.