In a new paper, researchers describe their development of a near-real time spatial assessment of COVID-19 cases to help guide local medical responses to clusters of outbreaks of the virus at the local level.
The paper, entitled “Geographic monitoring for early disease detection (GeoMEDD),” appeared in the Dec. 10 issue of Nature Scientific Reports and comes from researchers at Case Western Reserve University (CWRU) School of Medicine, University Hospitals (UH) Cleveland Medical Center, and Texas A & M University.
While developing a tracking system during the beginning stages of the COVID-19 pandemic, the authors realized that there was a need to refocus more traditional spatial mapping towards a more granular cluster detection methodology that provides syndromic surveillance, or early indicators of emergent disease by leveraging a health system’s access to data streams from various sources which account for location and timing of cases.
“Without such integration, there are missed opportunities for hospitals, health departments, and community leaders to mobilize early intervention activities and save lives. This information provides insights to targeted community testing opportunities, post-acute care intervention, and targeted community education in areas with community spread,” said lead author Andrew Curtis, Ph.D., Co-Director of the GIS Health & Hazards Lab and Professor in the Department of Population and Quantitative Health Sciences at the CWRU School of Medicine.
“From a hospital and health system perspective, such disease propagation can rapidly tax capacity and resources. From a humanitarian dimension, being able to communicate with local communities, municipality or even building managers requires both time and space insights,” said Curtis.
The authors propose, for example, that positive COVID-19 test results could be analyzed in real time as they flow into a health system, which when combined with severity of the associated symptoms, details such as age and previous medical