JMIR Publications

ABSTRACT

Background:

Public health scientists have used spatial tools such as web-based Geographical Information System (GIS) applications to monitor and forecast the progression of COVID-19 pandemic and track the impact of their interventions. The ability to track SARS-CoV2 variants and incorporate social determinants of health with street-level granularity can facilitate the identification of local outbreaks, highlight variant-specific geospatial epidemiology, and inform effective interventions. We developed a novel dashboard, the University of MAssachusetts’ Graphical user interface for Geographic Information (MAGGI) variant tracking system that combines GIS, health-associated sociodemographic data, and viral genomic data to visualize spatial-temporal incidence of SARS-CoV2 variants with street-level resolution while safeguarding protected health information (PHI). The specificity and richness of the dashboard enhance local understanding of variant introductions and transmissions so that appropriate public health strategies can be devised and evaluated.

Objective:

We developed a web-based dashboard that simultaneously visualizes the geographic distribution of SARS-CoV2 variants in central Massachusetts, social determinants of health, and vaccination data to support public health efforts to locally mitigate the impact of the COVID-19 pandemic.

Methods:

MAGGI uses a server-client model-based system enabling users to access data and visualizations via an encrypted web browser, thus securing patient health information (PHI). We integrated data from electronic medical records, SARS-CoV-2 genomic analysis, and public health resources. We developed the following functionalities into MAGGI: spatial and temporal selection capability by zip codes of interest, detection of variant clusters, and a tool to display variant distribution by social determinants of health. MAGGI was built on the Environmental Systems Research Institute (ESRI) ecosystem and is readily adaptable to monitor other infectious diseases and their variants in real-time.

Results:

We created a geo-referenced database and added sociodemographic and viral genomic data to the ArcGIS dashboard that interactively displays central Massachusetts spatial‐temporal variants distribution. Our researchers use MAGGI to show the occurrence of SARS-CoV-2 genomic variants at high geographic resolution and refine the display by combinatorically selecting data features such as variant subtype, subject zip codes, or date of COVID-positive sample collection. Furthermore, they use it to scale time and space to visualize association patterns between socioeconomics, social vulnerability based on the Centers for Disease Control and Prevention’s social vulnerability index, and vaccine rates. We launched the system at the University of Massachusetts Chan Medical School to support internal research projects starting in March 2021.

Conclusions:

We developed a COVID-19 Variant Surveillance dashboard to advance our geospatial technologies to study SARS-CoV-2 variants transmission dynamics. This real-time GIS-based tool exemplifies how spatial informatics can support public health officials, genomics epidemiologists, infectious disease specialists, and other researchers to track and study the spread patterns of SARS-CoV-2 variants in our communities.