Using Machine Learning to Estimate Unobserved COVID-19 Infections in North America.

Background: The detection of coronavirus disease 2019 (COVID-19) cases remains a huge challenge. As of April 22, 2020, the COVID-19 pandemic continues to take its toll, with >2.6 million confirmed infections and >183,000 deaths.

Risk of a second wave of Covid-19 infections: using artificial intelligence to investigate stringency of physical distancing policies in North America.

Purpose: Accurately forecasting the occurrence of future covid-19-related cases across relaxed (Sweden) and stringent (USA and Canada) policy contexts has a renewed sense of urgency. Moreover, there is a need for a multidimensional county-level approach to monitor the second wave of covid-19 in the USA.

Method: We use an artificial intelligence framework based on timeline of policy interventions that triangulated results based on the three approaches-Bayesian susceptible-infected-recovered (SIR), Kalman filter, and machine learning.

Deep learning COVID-19 detection bias: accuracy through artificial intelligence.

Background: Detection of COVID-19 cases' accuracy is posing a conundrum for scientists, physicians, and policy-makers. As of April 23, 2020, 2.7 million cases have been confirmed, over 190,000 people are dead, and about 750,000 people are reported recovered.