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Mobile App & Algorithm for COVID-spreading preventing solution

Research and development of algorithm solution and a mobile application interface

Website: geoHealthApp

Duration: 2020

Customer location: Hannover, Germany

and Switzerland

Industry: Healthcare

Services: Cloud ● Custom Software Development ● Data Services ● Mobile App Development ● Product Discovery and Consulting ● UI/UX Design

Tech stack: Mongo Atlas ● Node.js ● React Native

Dedicated team behind the project

Roman

Technical Arhitect

Nick

Mobile Technical Lead

Anton

Lead Software Engineer

Maxim

Software Engineer

Yaroslav

Software Engineer

Taras

QA Engineer

Nadya

QA Engineer

The client

A non-profit organization based in Germany was working hard to stop the spread of coronavirus. The client was interested in validating the idea of using GPS satellite-location tracking as a tool for preventing the spread of the virus while simultaneously docking down the medical system overload.

The challenge

There was no app like this one developed before since humanity never faced this challenge before. Given that the telemedicine industry is just emerging on the horizon of neoteric medical solutions, our tasks boiled down to conducting completely new research. We had to pioneer the development of a contact tracing app to prevent the further spread of COVID-19.

Besides, we have been tasked with developing an architecture that would store location data for more than 10 million users. While thorough research of the GPS/BLE applicability and defining the tracing protocols were paramount, we have also found it exponentially crucial to limit the infrastructure expenses for our client.

We were challenged to create Europe’s 1st COVID-19 contact tracing application to be used by the health authorities and governments within the framework of fighting the spread of the virus.

What was done

In-depth preliminary research marks the beginning of any project at Ralabs. We have scrutinized the WHO’s position on the issue and it was clear that the organization defined the contact tracing interventions as one of the most efficient measures in controlling the COVID-19 outbreaks worldwide.

Implemented features:

PEPP-PT/PEPP:

Utilized the Pan-European Privacy-Preserving Proximity Tracing Protocol to ensure privacy-preserving proximity tracing, aligning with European data protection standards.

Central Server Model for Anonymous Identification:

Implemented a central server model to ensure users’ identities remained anonymous while securely managing contact tracing data.

Hybrid Mobile Application for iOS and Android:

Released a hybrid mobile application, available on both iOS and Android platforms, ensuring broad accessibility and usability.

Infrastructure for 10+ Million Users on AWS and Mongo Atlas:

Designed an auto-scalable infrastructure based on AWS and Mongo Atlas, optimized for cost-efficiency and capable of supporting over 10 million users. This infrastructure tracks location data every five minutes while balancing system load effectively.

Bluetooth-Based Intersection Algorithm R&D:

Conducted extensive research on contact-tracing protocols, assessing the accuracy of GPS locations, Bluetooth Low Energy (LE) tracing, and Beacons, culminating in a refined Bluetooth-based intersection algorithm.

Decentralized Data Storage Using Blockchain and AI:

Developed a decentralized data storage solution leveraging blockchain and AI technologies to provide full anonymization and security while ensuring compliance with EU Data Protection Guidelines and HIPAA.

Additional features

Smart caching every 14 days to decrease the amount of stored data.

Completed testing with offline real users

Fresh UI that adheres to accessibility standards to ensure a smooth and convenient user experience as well as increased responsiveness.

A stepwise description:

Research and Protocol Definition

Our research phase focused on evaluating contact tracing technologies, including Bluetooth LE and Beacons, and determining the accuracy of location-based tracking. We assessed the Pan-European Privacy-Preserving Proximity Tracing (PEPP-PT) protocol and decentralized approaches, focusing on security and privacy. Our analysis included studies of signal strength for proximity detection and GPS limitations, ensuring high accuracy for real-world applications.

Architecture Design

The platform was architected using AWS for scalability and MongoDB Atlas for secure data storage. The system supported 10+ million users, designed to auto-scale based on traffic demand. We integrated location tracking, with updates every five minutes, while optimizing costs by reducing server loads by 80%. The architecture followed a centralized model for handling anonymized data and ensuring privacy while using encryption for all communications. Additionally, we built a caching system that automatically purged data every 14 days to optimize storage

Development and Testing

Key features were developed and rigorously tested:
Monitored Risk of Infection Spreading: Implemented a traffic light system based on anonymized location data to signal users’ exposure risk. Human-in-the-Loop Document Check: Developed a feature where health authorities could validate user-submitted data, ensuring accurate infection reports. Algorithm-Based Intersection Matching: Built a precise algorithm using Bluetooth LE to detect intersections between users, ensuring high accuracy in contact tracing. This included testing various Bluetooth signal ranges to determine optimal proximity detection.

Security and Privacy

We applied full encryption (HTTPS) across all internal and external communications, ensuring no sensitive data was stored or shared unencrypted. A privacy-first approach ensured compliance with GDPR and HIPAA regulations. Data was anonymized, ensuring it could only be accessed by health authorities using strict authorization protocols.

Optimization and Cost Management

The system architecture was designed to be cost-efficient while managing massive user volumes. Through careful optimization, we reduced infrastructure costs by 80%. We leveraged Amazon Web Services’ scaling capabilities to handle traffic spikes while ensuring system stability and data integrity.

Other Features:

Algorithm-Based Intersection Matching: Enhanced with Bluetooth signal strength testing, providing a more energy-efficient solution that minimized battery consumption on users’ devices while maintaining accuracy.
Smart Caching: Introduced a system that cached location data and performed automated data purges every 14 days to minimize storage overhead.
Localization for Multiple Markets: Delivered full localization, allowing the app to operate seamlessly in English, German, and Swiss markets, ensuring adaptability for different regional needs and compliance standards.

Release and Updates:

App Store and Google Play Release: We successfully navigated the app store restrictions, launching the app on iOS and Android platforms. Localization and app optimization ensured smooth approval and launch in multiple regions.
OTA Updates: Enabled over-the-air (OTA) updates for faster deployment of new features and fixes without requiring manual user updates, ensuring continuous improvement without user disruption.