Stop the spread. This app provides a hyperlocal, “neighborhood-level” view of coronavirus (COVID-19) cases and the ability to track self-reported cases. COVID Near Me is powered by MarkLogic Data Hub Service and developed by Technology Rivers, with collaboration and integrated lab test result data from multiple official data sources.
It seems every data company has a map or dashboard that’s tracking the spread based on official results. And other applications exist that aim to encourage self-reporting of diagnosis and symptoms for more local-level tracking, but those are dependent on user activity. While self-reporting is encouraged for even more precise reporting, because of the lab test result data that is integrated in with the COVID Near Me app, users can gain insight into virus hotspots independent of the need to self-report.
The question we really want to answer about coronavirus is, “are there active cases in my neighborhood — including the official and unofficial ones?”
And wouldn’t it be nice to get an alert on your phone if you have been near a person who has now tested positive coronavirus (i.e. anonymous contact tracing)?
COVID Near Me integrates actual reported data at the county, state, and country levels, as well as test result data, test site data, and self-reported data. Once integrated into a single data hub, this information is used to generate an “infected score” that reflects the concentration of confirmed cases and symptomatic cases down to one square kilometer. This score is displayed on the app as a heat map, overlayed on top of actual reported cases and test site locations.
Integrating multiple different data sources into a single solution is made possible using MarkLogic Data Hub Service, which is a scale-out, shared-nothing architecture. This Common Criteria certified NoSQL database is a proven solution for large-scale data integration, running Healthcare.gov and many other government applications, with customers using it in the petabyte scales. The latest version of MarkLogic Server can also run embedded machine learning models.
To date, we have been able to pull together the data sets from our early partners, but as new partners are brought on board, we can continue to integrate new data sets and evolve our offering. And as more users self-report, we can have a better picture of symptomatic (under reported in actual numbers) and recovered cases, which can prove valuable for predicting the growth and location of hot spots as well as for researchers looking to connect with recovered individuals and search for medicinal solutions to the pandemic.
We are all in this together. Want to get involved in stopping the spread? We are looking for new partners to help us in the following areas: