There are more than 1 billion people in self isolation right now in the world. As local, regional and nationwide lockdowns continue on a global scale, social isolation will have an increasingly detrimental effect on the wellbeing of people. Social isolation has been linked to increased rates of alcoholism, abuse of drugs and deterioration of people’s physical and mental health. We cannot expect citizens to remain in their homes indefinitely – they will need to be let outdoors to connect with others at some point.

The problem we are solving is — how do you minimise the negative effects of social isolation ( whilst at the same time keeping the community risk of Covid-19/Coronavirus infection low when people are outdoors?

For the movement to normalcy we will need to provide people with tools that let them understand and monitor their well-being while for maintenance of safety keep the incident rate at an appropriate level until eradication measures (e.g. vaccine) can be put in place.

Why is this a problem? Societies that survive crises are the ones that adapt their behaviours. We all need to stay sane, adhere to government recommendations and keep the economy going to survive this crisis.


To help bring back a semblance of normality we will provide a tool that allows people to understand and monitor their well-being while shaping their behaviour to be conducive to a healthy lifestyle in a way that does not jeopardise their safety with respect to the current COVID-19 spread.

Our app has two recommendation modes for users – “Sane Inside” and “Safe Outside”.

Our solution is simple – we attempt to keep the majority (e.g. 90%) of the population indoors, whilst allowing the most at-risk people (heavily depressed, anxious, rebelling) to go outdoors. These ‘outdoors’ groups of people are dynamic and constantly changing, based on self-input measures of mental and physical health. These temporary and near real-time clustered risk groups (risk being either (mental) health or quarantine-rule-breaking) can potentially over time become safe ‘isolation cells’ – groups of people feeling and behaving similar to you – and therefore safe to interact with.

However, the rule still applies – at a given hour of the day, only e.g. 10% of the population is outdoors.

You can think of our app as a non-intrusive system-level crowd control married together with a light-touch health checker and productivity app.

Our solution is embedded in behavioural and data science. Components and principles of our app:

1) Smart question, or ‘nudge’ selection algorithm that avoids ‘survey fatigue’ and brings users to the ‘recommended action’ as fast as possible. E.g. Get ‘anxious/depressed’ person quickly through the user flow and recommend going for a walk outside or connecting with people who can help. If a user is feeling fine, we use this opportunity not to annoy them and perhaps instead suggest light-touch day planning or productivity nudges. Think of it as presenting an optimised-for-wellbeing to-do list (e.g. “Sunny outside! Go spend 5 minutes in your balcony and get your vitamin D dose now!”) over the course of the day. 2) If a person is sent outdoors, we estimate the relative infection risk of an area. People who reported being infected, or with symptoms, will be attributed higher infection weighting to compute the cluster risk score. Note that we would never disclose individuals’s risk or location to other users – the location risk is based on clusters of people and there is a time-delay. E.g. the ‘red area’ risk explanation would read as “This is a high-risk area, because it recently has been visited by other people”.

Based on up-to-date epidemiology and virology evidence, our app will calculate safe outdoors areas to be in, e.g.:

  • Users have to go via a planned user flow to get to the map of going outside. We would never suggest potentially infected individuals go wonder around.
  • Density of high-risk population. Keeping 1-2 meter distance between users, and receiving alerts to one’s phone if too close.
  • As the virus in the air remains active for 2 hours, we would flag such areas where a high-risk user was in as high-risk.
  • Follow scientists’ recommendations ( and minimise the % of community mingling at a given point in time.
  • Potentially, with enough data collected, we would be able to predict for different days of the week and times of day of when and where it’s safe to be outside.