Mathematical modeling of COVID-19 transmission and mitigation strategies in the population of Ontario, Canada

Ashleigh Tuite; David N Fisman; Amy L Greer

is the (which was not peer-reviewed) The copyright holder for this preprint . https://doi.org/10.1101/2020.03.24.20042705 doi: medRxiv preprint 1 0 Interpretation COVID-19 poses an extraordinary challenge to societies. While severe illness, particularly in older individuals, is frequent enough to overwhelm a society's ICU capacity (19) , mild unrecognized illness (particularly in younger individuals) contributes to spread (20) , and epidemics may only be recognized when superspreader events occur (21) , often in vulnerable settings like health care facilities (22) . In contrast to SARS (23), the high frequency of mild cases means that strategies which focus on case identification and isolation alone are likely to fail (22) . Control strategies for COVID-19 thus need to balance competing risks: the risks of mortality and health system collapse, on the one hand, against economic risks and attendant hardships (and health consequences) on the other. In this work, we evaluated plausible strategies for attenuating the COVID-19 epidemic in Ontario, Canada. We focussed on ICU resources for two reasons: first, because this component of most health systems represents a scarce resource prone to being saturated; and secondly because such saturation results in abrupt surges in casefatality, as individuals with acute respiratory distress syndrome will die quickly without the . CC-BY-NC 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity.