Crowd-sourced COVID-19 reporting and assessment system. Our app enables anyone to anonymously report their symptoms, visited places and contacts. You can get personalized risk assessments and support institutions with detecting infections. Our project is part of the "Health" track.
Hard to get tested: Germany for example has a theoretical capacity of 12k tests a day, which is not enough to test every person with symptoms.
Missing data collection: There is no easy way for individuals to report flu-like symptoms and related information to allow meaningful statistical inference on undetected infection chains.
Hard to keep contacts informed: In case of a suspected infection, it's emotionally stressful to reach out to family and friends to provide this potentially valuable information. For other contacts, you might not even be able to inform them. An anonymized way of reporting symptoms and meta-information would allow anybody to keep potential contacts informed.
Unable to make data-informed decisions: A longterm lockdown can not be sustained forever and public life has to go on at some point in time. A tool that can provide "real-time" predictions about the risk of visiting certain places can be invaluable for many people, especially for high-risk groups.
Cotect allows anyone to anonymously report their flu-symptoms with relevant meta-information such as visited places or events. By analyzing statistical correlations in the collected data, users will get personal risk assessments (e.g., likelihood of Covid-19 infection) based on their reports. Users, especially in the high-risk group, will also be able to get real-time information on the risk of visiting selected places (e.g., city, workplace, school). The full anonymized dataset will be made accessible to research facilities and public institutions for discovering unknown infection chains and assist with data-informed decisions on closing places or canceling events.
Cotect enables everyone to report their symptoms anonymously with relevant meta-information, such as visited places or contacts. Users can update their cases at any time to track changing symptoms, new contacts, and place visits. Cotect combines this data without storing personal identifying information. The user has full control over what information is reported and can delete this data at any time.
Personal risk assessments
By analyzing statistical correlations in the collected data, users receive personal risk assessments (e.g., the probability of infection) based on their case reports. This also includes giving users, especially in the high-risk group, real-time information about the risk of visiting selected places (e.g., city, workplace, school). With this situation probably going on for months, a tool to get a data-driven risk assessment might be helpful for many.
Support containment & mitigation
We are committed to support public institutions with containment and mitigation efforts. Our anonymized case data set will be made available to research facilities and public institutions for the discovery of unknown chains of infection and assist with data-informed decisions such as closing places or canceling events. We are also ready to cooperate with health care institutions to implement features within cotect that facilitate the testing process (e.g., search for test sites, registration, digital waiting list).
Priority on data privacy
The cotect project aims to ensure the highest level of data privacy while still allowing sophisticated data analysis. Cotect is fully GDPR-compliant and allows single-click data export and deletion. All data traffic is fully encrypted, and the data is stored with the highest security level. The only purpose of the data collection is to help with containment and mitigation. Once this goal is achieved, all data is deleted.
The cotect architecture allows scalability to millions of users. It is built with components of the GCP platform, including Kubernetes, Firebase, and the Places API, which are designed for high performance and unlimited scalability.
Sophisticated data analytics
Our data collection, infrastructure, and data model are optimized for the application of statistical and machine learning methods to detect infection chains immediately. The data collected via contact and location reporting functions are processed into a highly connected graph structure, which also offers great flexibility for integrating new information. The authorization and data verification capabilities minimize misuse and ensure the collection of high-quality data.
Open-source & non-profit
We have committed ourselves to keep the cotect project fully open-source and non-profit with a maximum of transparency. This project is designed to allow broad cooperation between different companies, organizations, and institutions to support the development and provide funding for cloud expenses.
Our first version of the app and backend is already implemented. This includes:
- reporting of symptoms, visited places, and contacts
- user authentication and crash reporting (via Firebase)
- scalable user endpoints (REST API) with monitoring (via Kubernetes)
- graph database for case reports (via Neo4j)
You can download the app (Android APK) here