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

Abstract

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.

Results: Our findings suggest three important insights. First, the effective growth rate of covid-19 infections dropped in response to the approximate dates of key policy interventions. We find that the change points for spreading rates approximately coincide with the timelines of policy interventions across respective countries. Second, forecasted trend until mid-June in the USA was downward trending, stable, and linear. Sweden is likely to be heading in the other direction. That is, Sweden's forecasted trend until mid-June appears to be non-linear and upward trending. Canada appears to fall somewhere in the middle-the trend for the same period is flat. Third, a Kalman filter based robustness check indicates that by mid-June the USA will likely have close to two million virus cases, while Sweden will likely have over 44,000 covid-19 cases.

Conclusion: We show that drop in effective growth rate of covid-19 infections was sharper in the case of stringent policies (USA and Canada) but was more gradual in the case of relaxed policy (Sweden). Our study exhorts policy makers to take these results into account as they consider the implications of relaxing lockdown measures.

Keywords: Artificial intelligence; Bayesian (SIR); Covid-19; Kalman filter; Machine learning.

Fig. 1

Mapping the Oxford COVID-19 government response tracker (OxCGRT) stringency index across USA, Canada, and Sweden

Fig. 2

a (Clockwise) Effective growth rate of covid-19 cases for the USA, Bayesian SIR model forecast of new confirmed covid-19 cases for the USA, and number of new covid-19 cases in the USA. b Kalman filter prediction of confirmed covid-19 cases in the USA. c Estimated bed capacity relative to weekly change in new cases in US counties. USA counties that fall in the top right quadrant of the chart (e.g., New England, Mountain States, Midwest, and South Atlantic) may be at the most risk for a surge in infections

Fig. 2

a (Clockwise) Effective growth rate of covid-19 cases for the USA, Bayesian SIR model forecast of new confirmed covid-19 cases for the USA, and number of new covid-19 cases in the USA. b Kalman filter prediction of confirmed covid-19 cases in the USA. c Estimated bed capacity relative to weekly change in new cases in US counties. USA counties that fall in the top right quadrant of the chart (e.g., New England, Mountain States, Midwest, and South Atlantic) may be at the most risk for a surge in infections

Fig. 3

a (Clockwise) Effective growth rate of covid-19 cases for Canada, Bayesian SIR model forecast of new confirmed covid-19 cases for Canada, and number of new covid-19 cases in Canada. b Kalman filter prediction of confirmed covid-19 cases in Alberta, Ontario and Quebec

Fig. 4

a (Clockwise) Effective growth rate of covid-19 cases for Sweden; Bayesian SIR model forecast of new confirmed covid-19 cases for Sweden; and Number of new covid-19 cases in Sweden. b Kalman filter prediction of confirmed covid-19 cases in Sweden