  The SARS-CoV-2 (Covid-19) pandemic has caused significant strain on public
health institutions around the world. Contact tracing is an essential tool to
change the course of the Covid-19 pandemic. Manual contact tracing of Covid-19
cases has significant challenges that limit the ability of public health
authorities to minimize community infections. Personalized peer-to-peer contact
tracing through the use of mobile apps has the potential to shift the paradigm.
Some countries have deployed centralized tracking systems, but more
privacy-protecting decentralized systems offer much of the same benefit without
concentrating data in the hands of a state authority or for-profit
corporations. Machine learning methods can circumvent some of the limitations
of standard digital tracing by incorporating many clues and their uncertainty
into a more graded and precise estimation of infection risk. The estimated risk
can provide early risk awareness, personalized recommendations and relevant
information to the user. Finally, non-identifying risk data can inform
epidemiological models trained jointly with the machine learning predictor.
These models can provide statistical evidence for the importance of factors
involved in disease transmission. They can also be used to monitor, evaluate
and optimize health policy and (de)confinement scenarios according to medical
and economic productivity indicators. However, such a strategy based on mobile
apps and machine learning should proactively mitigate potential ethical and
privacy risks, which could have substantial impacts on society (not only
impacts on health but also impacts such as stigmatization and abuse of personal
data). Here, we present an overview of the rationale, design, ethical
considerations and privacy strategy of `COVI,' a Covid-19 public peer-to-peer
contact tracing and risk awareness mobile application developed in Canada.
