COVI White Paper

Abstract

The SARS-CoV-2 (Covid-19) pandemic has resulted in significant strain on health care and public health institutions around the world. Contact tracing is an essential tool for public health officials and local communities to change the course of the Covid-19 pandemic. Standard manual contact tracing of people infected with Covid-19, while the current gold standard, 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 applications has the potential to shift the paradigm of Covid-19 community spread. Although some countries have deployed centralized tracking systems through either GPS or Bluetooth, more privacy-protecting decentralized systems offer much of the same benefit without concentrating data in the hands of a state authority or in for-profit corporations.Additionally, machine learning methods can be used to circumvent some of the limitations of standard digital tracing by incorporating many clues (including medical conditions, self-reported symptoms, and numerous encounters with people at different risk levels, for different durations and distances) and their uncertainty into a more graded and precise estimation of infection and contagion risk. The estimated risk can be used to provide early risk awareness, personalized recommendations and relevant information to the user and connect them to health services. Finally, the non-identifying data about these risks can inform detailed epidemiological models trained jointly with the machine learning predictor, and these models can provide statistical evidence for the interaction and importance of different factors involved in the transmission of the disease. They can also be used to monitor, evaluate and optimize different health policy and confinement/deconfinement 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.

Building a Foundation for Data-Driven, Interpretable, and Robust Policy Design using the AI Economist

“…To the best of our knowledge, no other work has studied the interaction between unemployment, COVID-19, and policy interventions. Existing public health research has focused on many aspects of COVID-19, including augmenting epidemiological models for COVID-19 (37,38), and analyzing contact-tracing in agent-based simulations (39), and evaluating the efficacy of various public health policy interventions (7,40,41). An analytical approach to policy design showed that the elasticity of the fatality rate to the number of infected is a key determinant of an optimal response policy (8,42).…”

Batch Inverse-Variance Weighting: Deep Heteroscedastic Regression

“…Similarly, studies based on population such as polling or pharmaceutical trials have quantified uncertainties based on the quantity and quality of their samples. It is also possible to train on simulators, as in climate sciences [Rasp et al, 2018] or in epidemiology [Alsdurf et al, 2020], and some of them provide their estimations' uncertainty based on the simulation procedure and the inclusion of real measurements in the model.…”

Object-Process Methodology for Intelligent System Development

“…1. The presented model has strong empirical confirmation in research of complex natural systems, such as the fight against the COVID-19 pandemic [4] [5], in the treatment of cardiac diseases [6] and in the Systems Earth Science [7]. In this article will be considered the application of this architecture to creation of new artificial systems with the aid of systematic approach.…”