(previously called "FindTheCluster")
Getting tested for COVID-19 can be challenging due to the limited availability of testing kits and overwhelming patient load on healthcare systems. Many individuals may only be mildly symptomatic, or asymptomatic - but they are often overlooked and deprioritized to undergo testing.
What it does
CheckIfCovid is a self-reporting survey for symptoms which calculates the probability of a COVID-19 infection based on the participant’s input. It works by training the data collected from other participants' responses and the data from confirmed COVID-19 cases to calculate the likelihood that the reported symptoms are associated with COVID-19.
- Instant assessment - Immediately know the probability that you have COVID-19 based on self-reported symptoms
- Geolocated symptoms - Plot out all symptoms in a map and identify clusters. Have you ever wondered if all occupants within the building you live in are also experiencing persistent cough?
- Privacy secured - No personally identifiable information will be stored.
- Third-party Integration - integrate with existing health systems through our API. If you own a product, we encourage to integrate (and anonymize) your data.
How do we collect data
- Survey - Self-reported symptoms
- Scrapers - We get data of confirmed COVID-19 cases from CSSEGIS, ECDC, GISAID, KAGGLE.
- Provide data to the government to run more targeted testing on identified clusters.
- More information dissemination or resources can be deployed to the identified clusters.
- Provides awareness to occupants of the identified clusters.
- Predict the probable locations of the next outbreaks.
We are currently organized into 3 teams and have a corresponding team page:
- Survey App (https://github.com/orgs/findthecluster/teams/survey-app)
- API App (https://github.com/orgs/findthecluster/teams/api-app)
- Data Science (https://github.com/orgs/findthecluster/teams/data-science)
We found out there are similar solutions to our Survey app:
What we want to achieve is a call to unify these data in a single location, standardize it, and apply machine learning. We will open the API and data to institutions that need them.
- Lack of dataset that had cases who reported symptoms and then tested negative. We mainly used unsupervised learning to determine whether the given symptoms of a case and it's proximity to one of the identified clusters will likely test positive