UMass Chan scientists develop web app to visualize where COVID-19 variables and clinical data are

The dashboard allows epidemiologists and public health officials to access a complex and diverse set of data about the COVID-19 pandemic in central Massachusetts in an easy-to-use app.

Geneticist Doyle Ward and colleagues at UMass Chan School of Medicine have developed a web application that allows epidemiologists, public health officials and other scientists to access genetic information about SARS-CoV-2 from central Massachusetts. The study was published in JMIR Formative Research.

Created by Qiming Shi, Ph.D., a data scientist at the Center for Clinical and Translational Sciences at UMass Chan, the app, called MAGGI (University of Massachusetts Geographical Information Graphical User Interface), relies on health-related sociodemographic and viral genetic data from more than 6000 A case of SARS-CoV-2 in central Massachusetts. By linking temporal, spatial, demographic, and nonspecific clinical data to the genetic variants of COVID-19, local epidemiologists and public health officials now have another tool to monitor, predict, and possibly track the progress of the COVID-19 pandemic.

“The dashboard gives epidemiologists and public health officials access to a complex and diverse set of data about the COVID-19 pandemic in central Massachusetts in an easy-to-use application,” said Dr. Ward, associate professor of microbiology and physiological systems and director of operations. At the Microbiome Research Center at UMass Chan. “With this tool, researchers can look at the data graphically in a meaningful way that allows them to understand what the underlying data is and how to apply it.”

The data that forms the backbone of MAGGI was collected from a number of public sources, including local vaccination rates from the Massachusetts Department of Public Health and indicators of social vulnerability from the Centers for Disease Control and Prevention. Additionally, demographic data at the ZIP code level was obtained from ZIP Code Scheduling Districts and the 2018 American Community Survey.

Ward and colleagues worked with UMass Memorial Health to obtain and archive the remaining SARS-CoV-2 positive samples collected in clinical settings. They used deep sequencing technology to reconstruct individual viral genomes and identify the variant of each COVID-19 sample.

Finally, de-identified clinical data, associated with each of the thousands of COVID-19 samples, was imported from the Clinical Data Research Repository maintained by UMass Chan. This data combines information from UMass Memorial’s electronic health systems but with all personal information that can be used to identify people who have been removed.

The result is a map of central Massachusetts with thousands of COVID-19 cases represented by color-coded dots for genetic subtypes and plotted across the region by zip code.

“We think this is unique to Central Massachusetts,” Ward said. “No one has information about COVID-19 at this level of detail.”

Maggie is more than a simple map; It is also covered with social and clinical data that researchers can use to identify patterns and respond to the epidemic. Users can access information about vaccine rates, social vulnerability, age, gender, poverty rates, sampling history, potential comorbidities, hospitalizations, clinical outcomes, and more above genetic, geographic and chronological information.

With access to this data, public health officials can monitor how the pandemic is progressing in near real time. As the shape of the pandemic evolves, health officials can craft new responses and interventions aimed at flattening infection rates or preparing for the flow of treatment in hospitals, for example. In addition, scientists can study the data to gain new insights into the virus, such as how it affects local residents.

“We can use MAGGI to measure the timing and location of COVID-19 infection and visualize associations and patterns between vaccine rates and social vulnerability at the street level,” said Carly Herbert, a doctoral student at the Morningside School of Biomedical Sciences and colleagues. Study author. “You can imagine how useful this data would be from a clinical perspective. We can use it to track emerging variables, evaluate interventions and determine if a variant behaves differently, spreads more quickly or causes more hospitalization.”

Ward said Magee and the complex data sets behind it will allow researchers to identify and observe patterns in near real time and learn about the root causes of those patterns. “It takes about a month between receiving the sample until we get the genetic sequence and the corresponding data in Magee. The hope is that this information can be used to form a response to emerging trends or patterns.

Since Magee is based on many publicly available data sets, it will likely be replicated in other sites as well. To obtain the same level of specificity would require access to high-throughput sequencing technologies as well as non-specific clinical data.

Beth A. McCormick, Worcester Foundation for Biomedical Research ChairUnderneath all this data is a unique collaboration between UMass Chan and UMass Memorial, Professor and Vice President of Microbiology and Physiological Systems and Founding Director of the Center for Microbiome Research.

Dr. said. McCormick. “The real strength of this study, in our minds, is how we were able to get such an amazing level of detail while also protecting patient privacy. It doesn’t happen without close collaboration between scientists and health providers.”

Ward and his colleagues continue to sequence the project’s new viral samples as they arrive from the clinic. The hope, as collection protocols are improved and simplified, is to improve the sequence shift for a few weeks or days to better inform public health decisions.

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