Social distancing and stay-at-home orders have helped slow the spread of COVID-19, but they have also had a drastic impact on the world’s economy. A new study finds there might be a middle ground.
Weeks of restrictive social distancing have been a hallmark of most of the world’s coronavirus disease 2019 (COVID-19) containment efforts, but a new study based on analytical modeling suggests social distancing restrictions could be modulated over time and still be effective.
The modeling was done by investigators from the University of Toronto and the University of Guelph. The work was based on data from the province of Ontario. The findings are published in the Canadian Medical Association Journal.
In a “base case” scenario, in which limited testing, quarantining, and social distancing were used, the study estimated that more than half (56%) of the province’s population would become infected with the novel coronavirus. Approximately 107,000 people would be in the hospital at the peak of the epidemic, the study found, and 55,500 people would require intensive care unit (ICU) beds.
With that scenario as the baseline, corresponding author Ashleigh Tuite, PhD, MPH, of the Dalla Lana School of Public Health, University of Toronto, and colleagues projected how tactics such as enhanced case-finding, restrictive social distancing, and a hybrid of enhanced case-finding with less severe physical distancing might impact the disease’s toll.
The investigators found that all of the interventions had meaningful impacts and the longer such measures were in place, the better.
“Physical distancing and other public health measures can reduce COVID-19 spread, but once these measures are lifted, we’re at risk of an uptick in cases,” Tuite said, in a press release.
However, the team also found that if restrictions were cycled on and off based on how much ICU capacity was available in Ontario hospitals, the epidemic could be managed in such a way that demand for ICU beds would always be below estimates of the province’s ICU capacity. The strategy is called “dynamic response.”
“Dynamic response measures that can be turned up and down in response to where we are on the epidemic curve provide a way to curb transmission while also providing periodic breaks and a chance to return to a more normal life,” they wrote.
Tuite and colleagues say this type of strategy could be a means by which to more successfully manage the competing interests of keeping the healthcare system functional while also keeping the economy healthy.
One strength of a dynamic approach is that measures like stay-at-home orders can be implemented at the beginning of potential spikes in cases, the investigators write. In the study, the dynamic model achieved a median overall attack rate (a measure of the percentage of the population infected) down to 2% over a period of 13 months.
Such ebbs and flows of transmission are likely in the coming months, the authors write, in part because inter-provincial and international travel means people could spark new hotspots of the virus in Ontario even if the province otherwise has the virus under control at any given time.
“There are likely going to be a series of ups and downs with dynamic interventions as transmission waxes and wanes,” Tuite said.
“With our model, we show that we can modulate response measures so that we don’t overwhelm our health care system, while also attempting to lessen the societal and economic disruption of these measures.”