Summary: Mathematical model incorporated several strategies to help flatten the COVID-19 curve, focusing on scarcity in hospital resources over two years. The models showed current physical distancing can help maintain healthcare capacity and reduce infections. The model also allowed for periodic economic and psychological breaks from social restrictions.
Source: CMAJ
A new modeling paper, using data from Ontario, indicates that dynamic physical distancing and other measures could help maintain health system capacity and prevent intensive care units (ICUs) from becoming overwhelmed because of COVID-19, while allowing periodic psychological and economic breaks from restrictions. The paper is published in CMAJ (Canadian Medical Association Journal) .
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,” says Dr. Ashleigh Tuite, assistant professor of epidemiology at the Dalla Lana School of Public Health, University of Toronto. “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.”
The authors modeled several strategies to flatten the epidemic curve, focusing on scarce ICU resources, which can quickly become overwhelmed, over a 2-year period. These include the following scenarios:
- Base case with limited testing, isolation and quarantine and an estimated 56% of the population becoming infected, projections estimated 107,000 hospital admissions and 55,000 cases in the ICU at the peak of the epidemic.
- Fixed duration interventions using physical distancing and case finding over 12 and 18 months substantially reduced the number of people infected, with varying results depending on how aggressive the implemented physical distancing measures were.
- Dynamic interventions turning off and on interventions to respond to the current state of the epidemic was projected to be effective at reducing the proportion of the population affected at the end of 2 years. This could involve dynamic physical distancing, which would intermittently ease some restrictions to provide periodic psychological and economic relief. For example, when implemented dynamically over 13 months, the median overall attack rate was reduced to 2%.
“There are likely going to be a series of ups and downs with dynamic interventions as transmission waxes and wanes,” says Dr. Tuite. “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.”
“Repeated outbreaks of COVID-19 will most likely occur because of the reintroduction of infection from other countries until a vaccine is developed or we develop herd immunity in which much of the population has developed antibodies to the virus,” says co-author Dr. David Fisman, professor of epidemiology at the Dalla Lana School of Public Health.
About this COVID-19 research article
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CMAJ
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Original Research: Closed access
“Mathematical modelling of COVID-19 transmission and mitigation strategies in the population of Ontario, Canada”. Ashleigh R. Tuite et al.
CMAJ doi:10.1503/cmaj.200476.
Abstract
Mathematical modeling of COVID-19 transmission and mitigation strategies in the population of Ontario, Canada
Background: Physical-distancing interventions are being used in Canada to slow the spread of severe acute respiratory syndrome coronavirus 2, but it is not clear how effective they will be. We evaluated how different nonpharmaceutical interventions could be used to control the coronavirus disease 2019 (COVID-19) pandemic and reduce the burden on the health care system.
Methods: We used an age-structured compartmental model of COVID-19 transmission in the population of Ontario, Canada. We compared a base case with limited testing, isolation and quarantine to scenarios with the following: enhanced case finding, restrictive physical-distancing measures, or a combination of enhanced case finding and less restrictive physical distancing. Interventions were either implemented for fixed durations or dynamically cycled on and off, based on projected occupancy of intensive care unit (ICU) beds. We present medians and credible intervals from 100 replicates per scenario using a 2-year time horizon.
Results: We estimated that 56% (95% credible interval 42%–63%) of the Ontario population would be infected over the course of the epidemic in the base case. At the epidemic peak, we projected 107 000 (95% credible interval 60 760–149 000) cases in hospital (non-ICU) and 55 500 (95% credible interval 32 700–75 200) cases in ICU. For fixed-duration scenarios, all interventions were projected to delay and reduce the height of the epidemic peak relative to the base case, with restrictive physical distancing estimated to have the greatest effect. Longer duration interventions were more effective. Dynamic interventions were projected to reduce the proportion of the population infected at the end of the 2-year period and could reduce the median number of cases in ICU below current estimates of Ontario’s ICU capacity.
Interpretation: Without substantial physical distancing or a combination of moderate physical distancing with enhanced case finding, we project that ICU resources would be overwhelmed. Dynamic physical distancing could maintain health-system capacity and also allow periodic psychological andeconomic respite for populations.
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