COVID-19 Variant Surveillance and Social Determinants in Central Massachusetts: Development Study

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 the COVID-19 pandemic and track the impact of their interventions. The ability to track SARS-CoV-2 variants and incorporate the 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 the spatiotemporal incidence of SARS-CoV-2 variants with street-level resolution while safeguarding protected health information. The specificity and richness of the dashboard enhance the 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-CoV-2 variants in Central Massachusetts, the 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. 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, the detection of variant clusters, and a tool to display variant distribution by the social determinants of health. MAGGI was built on the Environmental Systems Research Institute 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’ spatiotemporal variants distribution. Genomic epidemiologists and public health officials use MAGGI to show the occurrence of SARS-CoV-2 genomic variants at high geographic resolution and refine the display by selecting a combination of data features such as variant subtype, subject zip codes, or date of COVID-19–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 vaccination 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.

An Old Acquaintance: Could Adenoviruses Be Our Next Pandemic Threat?

“…These conditions can be very different for different groups in a population and can lead to differences in health outcomes [ 10 ]. Although the SDOH were considered mostly for chronic pathologies such as diabetes (T2DM), obesity and high blood pressure (HBP) with SARS-CoV-2 pandemic, a preponderant role was noted in the health strategies implemented, observing that the SDOH influenced infections [ 10 , 110 ]. In Stockholm, Sweden the SARS-CoV-2 infection rate was 3 to 4 times higher in some low socioeconomical residential areas compared with the country average [ 111 ].…”

Realizing the Potential of Social Determinants Data: A Scoping Review of Approaches for Screening, Linkage, Extraction, Analysis and Interventions

“…Few studies (n = 3) aimed to integrate EHR, genomic, and public health data to study the intersection of lifestyle, genetics, and environmental influences [94][95][96]…”

An Old Acquaintance. Could Adenovirus be our Next Pandemic Threat?

“…These conditions can be very different for different groups in a population and can lead to differences in health outcomes (10). Although the SDOH were considered for chronic pathologies such as T2DM, obesity and high blood pressure (HBP) with SARS-CoV-2, a preponderant role was noted in the different state policies, observing that the SDOH influenced infections (10,114). In Stockholm, Sweden the SARS-CoV-2 infection rate was 3 to 4 times higher in some socioeconomically disadvantaged residential areas compared to the country average (115).…”