Update on COVID-19 Projections Science Advisory and Modelling Consensus Tables April 1, 2021 Key Findings 2 • The third wave is here and being driven by variants of concern. • Younger Ontarians are ending up in hospital. Risk of ICU admission is 2 x higher and risk of death is 1.5 x higher for the B.1.1.7 variant. • COVID-19 threatens health system ability to deal with regular ICU admissions and the ability to care for all patients. • Vaccination is not reaching the highest risk communities, delaying its impact as an effective strategy. • School disruptions have a significant and highly inequitable impact on students, parents and society. Further disruptions should be minimized. • Stay-at-home orders will control the surge, protect access to care, and increase the chance of the summer Ontarians want. 0 50 100 150 200 250 Weekly new cases per 100,000 residents Peel Lambton Toronto Hamilton York Durham Sudbury Eastern Leeds Grenville Lanark Chatham-Kent Ottawa Niagara Halton Haldimand-Norfolk Simcoe Muskoka Middlesex-London Wellington-Dufferin-Guelph Timiskaming Southwestern KFLA Grey Bruce Renfrew Hastings & PEC North Bay Parry Sound Algoma Porcupine Thunder Bay Northwestern Brant Waterloo Windsor-Essex Peterborough Haliburton KPR Huron Perth Data source: CCM Data note: Data for the most recent day have been censored to account for reporting delays March 15 March 28 Average weekly cases on: CONTROL RESTRICT PROTECT Cases have increased and are above the second highest level of the framework in most Public Health Units 3 Protect Dec 26 Province-wide lockdown 14-days for N. Ontario 28-days for S. Ontario Restrict Jan 18 First dose vaccination complete in prioritized PHUs Control Peel, 8.6% Toronto, 7.1% York, 6.4% Durham, 6.1% Thunder Bay, 5.1% Ontario, 4.7% 0 2 4 6 8 10 12 14 16 Aug 1 Aug 15 Aug 29 Sep 12 Sep 26 Oct 10 Oct 24 Nov 7 Nov 21 Dec 5 Dec 19 Jan 2 Jan 16 Jan 30 Feb 13 Feb 27 Mar 13 Specimen Date (7-day avg.) % positivity of daily testing episodes Data source: Ontario Laboratory Information System (OLIS), data up to March 26 Testing % positivity has increased and is above the second highest level of the framework 4 Lambton, 553 Sudbury, 507 Windsor-Essex, 148 Ontario, 292 100 200 300 400 500 600 700 Aug 1 Aug 15 Aug 29 Sep 12 Sep 26 Oct 10 Oct 24 Nov 7 Nov 21 Dec 5 Dec 19 Jan 2 Jan 16 Jan 30 Feb 13 Feb 27 Mar 13 Specimen Date (7-day avg.) Testing episodes per 100,000 Data source:Ontario Laboratory Information System (OLIS), data up to March 26 Testing rates are flat so case growth is not a result of more testing 5 Cases are increasing. Most new cases are variants of concern. 0 20 40 60 80 100 120 140 160 180 200 0 20 40 60 80 100 120 140 160 180 200 Rate per 100,000 Inhabitants per Week Date 7-day average for VOCs and non-VOCs combined Daily rate for non-VOCs Yellow zone Orange zone Red zone Daily rate with 7-day average for VOCs 6 Variants of concern have more severe consequences and are more fatal Hospitalization Hospitalization with VOC ICU Admission ICU Admission with VOC Death Death with VOC 7 Compared to people infected with the earlier variants, more people with COVID-19 are hospitalized, admitted to ICU, and die if they are infected with the variants of concern. Short-term case projections depend entirely on system-level public health measures and vaccination 8 Figure shows example, representative of predictions across 4 models, 3-5 scenarios each. Scenarios: Stay-at-home order assumptions: • No stay-at-home • 2 weeks starting Apr 5 • 4 weeks starting Apr 5 Vaccine assumptions: • 70% effective in preventing infection • Administered at constant rate • Administered randomly to population Predictions informed by modeling from COVID-19 ModCollab, Fields Institute, McMasterU, PHO, YorkU Data (Observed Cases): covid-19.ontario.ca Data Sources: MOH COVID Census and Critical Care Information System 0 200 400 600 800 1000 1200 1400 1600 1800 01-Sep 08-Sep 15-Sep 22-Sep 29-Sep 06-Oct 13-Oct 20-Oct 27-Oct 03-Nov 10-Nov 17-Nov 24-Nov 01-Dec 08-Dec 15-Dec 22-Dec 29-Dec 05-Jan 12-Jan 19-Jan 26-Jan 02-Feb 09-Feb 16-Feb 23-Feb 02-Mar 09-Mar 16-Mar 23-Mar Patients in Inpatient Beds with COVID19 Patients in ICU with COVID-Related Critical Illness 41.7% increase in hospitalizations over past 2 weeks COVID-19 Hospitalizations and ICU occupancy are increasing 9 Data: CCM data up to March 28. Based on date of hospital admission COVID-19 patients admitted to ICU continue to get younger 104 84 45 73 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Dec 14-20, 2021 Mar 15-21, 2021 Percentage of Weekly COVID-19 ICU Admissions by Age Date Dec 14-20, 2020 Mar 15-21, 2021 46 % 30 % 0 to 59 years 60+ years 10 As with cases, ICU projections depend entirely on system-level public health measures Predictions: COVID-19 ModCollab. Data (Observed ICU Occupancy): CCSO 11 0 50,000 100,000 150,000 200,000 250,000 The access to care deficit continues to build 12 Data Source: Wait Times Information System. Backlog estimated based on comparison of 2020/21 with 2019/20 surgical volumes Provincial surgery shutdown Cumulative pandemic￾related surgical backlog: 245,367 cases Essential workers are keeping things moving and bearing the brunt of the pandemic. Vaccination and control of workplace outbreaks will be critical. Source: Chagla Z, Ma H, Sander B, Baral S, Mishra S. (2021). Characterizing the disproportionate burden of SARS-CoV-2 variants of concern among essential workers in the Greater Toronto Area, Canada. https://www.medrxiv.org/content/10.1101/2021.03.22.21254127v1.full.pdf 13 0 500,000 1,000,000 1,500,000 16Dec2020 01Jan2021 16Jan2021 01Feb2021 16Feb2021 01Mar2021 16Mar2021 01Apr2021 Date Dose One (Cumulative) Dose Two (Cumulative) Dose 1 Administered wasdetermined based on the first Time Given for eachclient. Dose 2 Administered wasdetermined based on the last Time Given for eachclient where there is more than 1 dose administered First dose vaccine coverage expanding but remains incomplete 80 years and older - 17% incomplete; 75-79 years – 40% incomplete; 70-74 years – 72% incomplete 14 Vaccination is not reaching the highest risk populations Figure excludes long-term care vaccination Source: ICES 15 School interruptions will have significant impacts on students, families, and society Economic modeling suggests schooling impacts will have long term economic effects: • A ~3% drop in lifetime earnings for these cohorts; • Lost GDP for Canada estimated at 1.6 trillion dollars Non-COVID health risks include: • Loneliness & social isolation, • Loss of structure affecting physical activity, sleep and mental health, and • Decreased ability to detect neglect or abuse. All negative impacts are highly inequitable with greater learning loss for students facing greater disadvantage Source: Kelly Gallagher-Mackay, Elizabeth Dhuey, Lisa Hawke, Lance McCready, Sarah Oates, Prachi Srivastava, and Kathryn Underwood 16 Key Findings 17 • The third wave is here and being driven by variants of concern. • Younger Ontarians are ending up in hospital. Risk of ICU admission is 2 x higher and risk of death is 1.5 x higher for the B.1.1.7 variant. • COVID-19 threatens health system ability to deal with regular ICU admissions and the ability to care for all patients. • Vaccination is not reaching the highest risk communities, delaying its impact as an effective strategy. • School disruptions have a significant and highly inequitable impact on students, parents and society. Further disruptions should be minimized. • Stay-at-home orders will control the surge, protect access to care, and increase the chance of the summer Ontarians want. Contributors • COVID-19 Modeling Collaborative: Kali Barrett, Stephen Mac, David Naimark, Aysegul Erman, Yasin Khan, Raphael Ximenes, Sharmistha Mishra, Beate Sander • Fields Institute: Taha Jaffar, Kumar Murty • ICES: Jeff Kwong, Hannah Chung, Kinwah Fung, Michael Paterson, Susan Bronskill, Laura Rosella, Astrid Guttmann, Charles Victor, and Michael Schull, Marian Vermeulen • McMasterU: Michael Li, Irena Papst, Ben Bolker, Jonathan Dushoff, David Earn • YorkU: Jianhong Wu, Francesca Scarabel, Bushra Majeed • MOHLTC: Michael Hillmer, Kamil Malikov, Qing Huang, Jagadish Rangrej, Nam Bains, Jennifer Bridge • OH: Erik Hellsten, Stephen Petersen, Anna Lambrinos, Chris Lau, Access to Care Team • PHO: Sarah Buchan, Kevin Brown • Education Analysis: Kelly Gallagher-Mackay, Elizabeth Dhuey, Lisa Hawke, Lance McCready, Sarah Oates, Prachi Srivastava, and Kathryn Underwood. 18 Content provided by Modelling Consensus and Scientific Advisory Table members and secretariat Beate Sander,* Peter Juni, Brian Schwartz,* Kumar Murty,* Upton Allen, Vanessa Allen, Nicholas Bodmer, Isaac Bogoch, Kevin Brown, Sarah Buchan, Yoojin Choi, Troy Day, Laura Desveaux, David Earn, Gerald Evans, David Fisman, Jennifer Gibson, Anna Greenberg, Anne Hayes,* Michael Hillmer, Jessica Hopkins, Jeff Kwong, Fiona Kouyoumdjian, Audrey Laporte, John Lavis, Gerald Lebovic, Brian Lewis, Linda Mah, Kamil Malikov, Antonina Maltsev, Doug Manuel, Roisin McElroy, Allison McGeer, David McKeown, John McLaughlin, Sharmistha Mishra, Justin Morgenstern, Andrew Morris, Samira Mubareka, Laveena Munshi, Christopher Mushquash, Ayodele Odutayo, Shahla Oskooei, Menaka Pai, Samir Patel, Anna Perkhun, Bill Praamsma, Justin Presseau, Fahad Razak, Rob Reid,* Paula Rochon, Laura Rosella, Michael Schull, Arjumand Siddiqi, Chris Simpson, Arthur Slutsky, Janet Smylie, Nathan Stall, Robert Steiner, Ashleigh Tuite, Jennifer Walker, Tania Watts, Ashini Weerasinghe, Scott Weese, Xiaolin Wei, Jianhong Wu, Diana Yan, Emre Yurga * Chairs of Scientific Advisory, Evidence Synthesis, and Modelling Consensus Tables For table membership and profiles, please visit the About and Partners pages on the Science Advisory Table website. 19