Geographic Monitoring for Early Disease Detection (GeoMEDD)

Abstract Identifying emergent patterns of coronavirus disease 2019 (COVID-19) at the local level presents a geographic challenge that is not ideally addressed using traditional spatial clustering approaches. The need is not only to identify statistically significant spatial patterns, but also develop a surveillance system to identify small numbers of cases emerging in vulnerable settings. This paper presents an approach that has been used to provide near-real time assessments of emergent disease to guide a hospital system’s intervention strategy: Geographic Monitoring for Early Disease Detection (GeoMEDD). Through integration of a spatial database and two types of clustering algorithms, GeoMEDD has the flexibility to scale in terms of required minimum members, connecting distance between cases, and time frame under consideration. GeoMEDD, has proven effective in revealing different cluster influencers and accelerators that give insight as to why the cluster exists where it does, and why it expands.