Effectiveness of non-pharmaceutical interventions to reduce SARS-CoV-2 transmission in Canada and their association with COVID-19 hospitalization rates

Background: Non-pharmaceutical interventions (NPIs) aim to reduce the incidence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections mostly by limiting contacts between people where virus transmission can occur. However, NPIs limit social interactions and have negative impacts on economic, physical, mental and social well-being. It is, therefore, important to assess the impact of NPIs on reducing the number of coronavirus disease 2019 (COVID-19) cases and hospitalizations to justify their use. Methods: Dynamic regression models accounting for autocorrelation in time series data were used with data from six Canadian provinces (British Columbia, Alberta, Saskatchewan, Manitoba, Ontario, Quebec) to assess (1) the effect of NPIs (measured using a stringency index) on SARS-CoV-2 transmission (measured by the effective reproduction number), and (2) the effect of the number of hospitalized COVID-19 patients on the stringency index. Results: Increasing stringency index was associated with a statistically significant decrease in the transmission of SARS-CoV-2 in Alberta, Saskatchewan, Manitoba, Ontario and Quebec. The effect of stringency on transmission was time-lagged in all of these provinces except for Ontario. In all provinces except for Saskatchewan, increasing hospitalization rates were associated with a statistically significant increase in the stringency index. The effect of hospitalization on stringency was time-lagged. Conclusion: These results suggest that NPIs have been effective in Canadian provinces, and that their implementation has been, in part, a response to increasing hospitalization rates of COVID-19 patients.

Optimal Staged Reopening Schedule Based on ICU Capacity: A Model-Informed Strategy

Reopening plans and strategies remain to be a top priority issue, post a pandemic wave, for economic recovery with a relatively safe community. We use a transmission dynamics model, parameterized through model fitting to cumulative incidence data during different social distancing escalation phases, to identify the optimal timing of reopening based on social-distancing de-escalation in a population. We use the Province of Ontario, Canada as a case study. The optimization is subject to the constraint that a future COVID-19 outbreak will not lead to the need of acute and intensive care unit beds beyond their local public health capacity. We minimize the cost, the total number of contacts lost until we reach a certain targeted date. We illustrate this optimization process by considering a particular de-escalation strategy that simply ‘reverses’ the escalation steps. We consider several scenarios depending on the number of de-escalation phases (characterized by an increase in the daily number of contacts), and different lengths of the period between a date when the cost is evaluated until a targeted data when the constraint is to be removed (for example, the anticipated date when a mass COVID-19 vaccination or an effective treatment becomes available) so that normal contacts prior to COVID-19 pandemic can be resumed. In the case of Ontario, we conclude that resuming 80% of pre-pandemic activity level should not happen 2 months before the vaccine/drug treatment becomes available. We also show that improving contact tracing and diagnosis capacity has a significant effect on the reopening date, whereas increasing ICU beds capacity has only a minor effect. © 2022, Springer Nature Switzerland AG.

Modelling scenarios of the epidemic of COVID-19 in Canada

Background: Severe acute respiratory syndrome virus 2 (SARS-CoV-2), likely a bat-origin coronavirus, spilled over from wildlife to humans in China in late 2019, manifesting as a respiratory disease. Coronavirus disease 2019 (COVID-19) spread initially within China and then globally, resulting in a pandemic.

Scenario tree and adaptive decision making on optimal type and timing for intervention and social-economic activity changes to manage the COVID-19 pandemic

We introduce a novel approach to inform the re-opening plan followed by a post-pandemic lockdown by integrating a stochastic optimization technique with a disease transmission model. We assess Ontarios re-opening plans as a case-study. Taking into account the uncertainties in contact rates during different re-opening phases, we find the optimal timing for the upcoming re-opening phase that maximizes the relaxation of social contacts under uncertainties, while not overwhelming the health system capacity before the arrival of effective therapeutics or vaccines.