Crowd-sourced COVID-19 reporting and assessment system.
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
Try It out
docker, fastapi, firebase, gcp, kubernetes, neo4j, python, react-native