Help us get this app in many languages! We currently support: English, Portuguese, Finnish, Bulgarian, Hindi, Farsi, Urdu, Greek, Japanese, Chinese, Spanish, Vietnamese, Hungarian, Russian, Danish, Mexican, Korean, Norwegian, Hebrew.

To help, download this file, translate the English to your native language and send it to us. We'll make sure to include it on the next release! Thanks!

We are waiting for Google and Apple's approval of both applications on the respective stores. We'll update this as soon as we hear back.



Encounter is a free, open-source, cross-platform (Android & iPhone) mobile application that allows automated, private and secure contact tracing without servers or infrastructure deployment requirements. Encounter is an automated, privacy-aware, non-siloed co-presence contact tracing logging tool that could be useful to tackle the spread of this and future virus outbreaks.

How is this different from DP-3T or PEPP-PT?

  • Open-source: open-source (Apache 2.0) and available Android and iOS applications, translated to multiple languages

  • Decentralised Data Storage: there is no server receiving or analysing data

  • Voluntary end-to-end: the only way to access your data is if you willingly share it

  • No hardware MAC exposure: even though Bluetooth and WiFi are leveraged, there are no discoverable MAC addresses exchange for contact tracing

  • Privacy by design: daily rotation of UUID (Unique Universal ID), reset on delete and on share of encounter data export JSON files

  • Autonomous: the UUID fingerprinting occurs every 1 minute when the application is active, every 15 minutes on the background using Google Nearby APIs. No private information, no location

  • Useful contact tracing data: JSON is easy to process, analyse, and integrate with many data science tools

  • Future-ready: integrating Google+Apple protocol is possible if made available to us


Worldwide Data

Up-to-date worldwide data to reflect the rate of infection and whether measures are working to reduce it. The data comes directly from John Hopkins JHU CSSE, exported as a JSON format three times a day.

The data source is available at:

Personal Control & Citizen Science

Local data export for personal analysis, or to share it with authorities and health officials.

Full user-control of when and with whom the data is shared. To protect users' privacy:

  • Daily rotation of UUIDs

  • New UUID is assigned if the data is deleted

  • New UUID if the data is exported

Empowering individual actions to get ahead of the virus and break the chain of transmission.

Social Distancing

Using Bluetooth, WiFi and ultrasound, we log on your phone every minute all surrounding Encounter UUIDs. Combined, we can determine with a high degree of confidence if two of more people were in close proximity, without necessarily knowing where anyone is, every minute, with minimal battery impact.

How does contact tracing work with Encounter?

Google Nearby APIs use three different strategies to determine when two devices are close enough to connect: Wi-Fi, Bluetooth, and audio. The Wi-Fi component doesn't actually connect two devices directly over Wi-Fi, but it compares the list of Wi-Fi access points each device can sense. If the list is similar or both devices are on the same access point (the actual router), that's a good sign they're very close to each other in the real world. Bluetooth contributes by transmitting a special token which can be seen by other devices using Nearby (within 5-10 meters). And there's the ultrasound: Nearby allows phones to emit ultrasonic sounds which is imperceptible to humans, but is detected by the microphone on other phones. If two devices can "hear" each other, they're in close proximity (within 1 meter).

NOTE: Internet availability is required to leverage Google Nearby API (via WiFi or GSM data network)