Update on COVID-19 Projections - Science Advisory and Modelling Consensus Tables February 1, 2022
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Update on COVID-19 Projections
Science Advisory and Modelling Consensus Tables
February 1, 2022
Key findings
• It is challenging to model the spread of COVID-19 because of changes
in testing, but other indicators suggest this phase of the Omicron wave
has plateaued or is in decline.
• Public health measures helped control this phase. Relaxation of these
measures will increase the spread of COVID-19. The size of any
resurgence is difficult to predict and will depend on vaccination, the
spread of Omicron, and changes in behaviour (e.g., mobility, masking).
• Surveillance will be important to detect changes in the trajectory of the
pandemic.
• Hospitals are now caring for the highest number of people with COVID-
19. Admissions are at highest levels across all age groups. ICU
occupancy continues to be high. Staffing in hospitals remains critical.
• Ontario data shows that vaccination (including third doses) provides
strong protection against serious illness. Increasing vaccine uptake
across all groups will reduce the impact of the pandemic.
2Ontario’s COVID-19 wastewater signal peaked around Jan 4, 2022,
which would correspond to a peak in cases around Jan 11, 2022.
• Ontario’s COVID-19 wastewater 2.0
signal is a population-weighted mean
Standardized Concentration of SARS-CoV-2 Gene Copies
of concentrations of SARS-CoV-2
gene copies across 99 wastewater
treatment plants, pumping stations 1.5
and sewersheds in all 34 public
health units.
1.0
• Taking into account the time lag until
diagnosis and reporting, the peak of
Ontario’s wastewater signal around 0.5
Jan 4, 2022 would correspond to a
peak in cases around Jan 11, 2022.
• Plausible range of SARS-CoV-2 0.0
infections that have occurred since
1
1
1
21
21
22
22
22
22
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Dec 1, 2021 based on wastewater
1-
8-
12
19
26
15
22
29
Sampling Date
signal: 1.5 to 4 million infections
Data: Wastewater Dashboard hosted by Ontario’s Ministry of the Environment, Conservation and Parks (MECP) 3
Analysis: Secretariat of the Science Advisory Table (https://covid19-sciencetable.ca/ontario-dashboard/)Provincial % test positivity and testing volumes
Nov. 28 Dec. 19 Dec. 31 Jan. 5
First Ontario New public health Changes to Additional public
Omicron case restrictions PCR testing health measures and
reported implemented criteria shift to online school
4
Data source: Public Health Ontario. PDNOC lab reportingPositivity in selected populations is also declining.
Workplace screening Repeat Testers Hospital Admission Screening
(Workplace rapid screening program across (Individuals with 40+ tests since pandemic start, (Selected Ontario hospitals, 4 for adult
Canada, twice per week, Ontario data shown) 20-79 years of age, excluding LTC residents) admissions, 5 for pediatric admissions)
3.0% 14% 0.3
Fraction of presumptive positive on total screens (%)
12% 0.25
2.5%
10%
Percent Positive (%)
0.2
Percent Positive (%)
2.0%
8%
1.5% 0.15
6%
1.0% 0.1
4%
0.5% 0.05
2%
0.0% 0% 0
1 Dec 8 Dec 15 Dec 22 Dec 29 Dec 5 Jan 12 Jan 19 Jan 1 Dec 8 Dec 15 Dec 22 Dec 29 Dec 5 Jan 12 Jan 19 Jan 20 Dec 27 Dec 3 Jan 10 Jan 17 Jan 24 Jan
2021 2021 2021 2021 2021 2022 2022 2022 2021 2021 2021 2021 2021 2022 2022 2022 2021 2021 2022 2022 2022 2022
20-39 years 40-59 years 60-79 years All Adult Adm Adult Non-COVID-19 Adm
Labour&Delivery Adm All Pediatric Adm
Pediatric Non-COVID-19 Adm
5
Data/Analysis: Creative Destruction Lab-Rapid Screening Consortium (Workplace screening). MCT using OLIS data (repeat testers), Hospital IPAC Teams and Data Analytics (hospital admission screening)10
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Workplace
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below previous levels.
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100
110
40
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70
80
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Retail & Recreation
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100
20
40
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Data: https://covid19.apple.com/mobility/ and https://www.google.com/covid19/mobility/
Analysis: Secretariat of the Science Advisory Table (https://covid19-sciencetable.ca/ontario-dashboard/)
c
6- -21
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6
Mobility, which is correlated with contacts between people, remainsHospitalizations have increased across all age groups.
New hospital admissions, all ages New hospital admissions, ≤ 19 years of age
250 12
10
200
New Hospital Admissions
New Hospital Admissions
8
150
6
100
4
50
2
0 0
1 Dec 2021 15 Dec 2021 29 Dec 2021 12 Jan 2022 1 Dec 2021 15 Dec 2021 29 Dec 2021 12 Jan 2022
≤ 1 yr 2-5 yrs 6-11 yrs 12-19 yrs 20-59 yrs ≥ 60 yrs ≤ 1 yr 2-5 yrs 6-11 yrs 12-19 yrs
Data: CCM
7
Analysis: MCTCOVID inpatient or ICU total census
500
0
1,000
1,500
2,000
2,500
3,000
3,500
4,000
4,500
Aug 26, 20
Sep 16, 20
Oct 7, 20
Oct 28, 20
Nov 18, 20
Dec 9, 20
Dec 30, 20
Jan 20, 21
Feb 10, 21
Mar 3, 21
Mar 24, 21
Apr 14, 21
May 5, 21
Patients in ICU with COVID-related critical illness
May 26, 21
Jun 16, 21
Patients in inpatient beds (incl. ICU) with active COVID19
Jul 7, 21
Jul 28, 21
Aug 18, 21
Sep 8, 21
Sep 29, 21
Oct 20, 21
Nov 10, 21
Dec 1, 21
COVID-19-positive hospitalizations are at a pandemic high.
Dec 22, 21
Jan 12, 22
8The context for modelling continues to be challenging.
• Population-level case counts have not been reliable since approximately Dec 24, 2021.
Hence, there is substantial uncertainty on transmissions over the past month given
decline in testing and high transmissibility of Omicron. This impacts estimates of prior
infection and degree of immunity.
• We have no clear estimates on the spread of SARS-CoV-2 during previous waves beyond
identified cases. This impacts estimates of the number of people with prior infection and
degree of immunity.
• Omicron appears to affect the upper respiratory tract more than the lower respiratory
tract, and therefore may have a different clinical course than previous variants, which
may affect hospital and ICU admissions and lengths of stay.
• Hospital and ICU admission data have been used to calibrate the models and to estimate
the case counts following the change in testing strategy. However, use of hospital and
ICU data presents challenges:
• Hospitalizations and ICU use are lagging indicators
• Limited data on length of stay for patients with Omicron
• It is difficult to separate hospitalizations for COVID-19 from hospitalizations with COVID-19,
particularly given ongoing uncertainty on Omicron severity and changing patterns of non-primary
COVID-19 admissions
• All these factors make it challenging to model the current wave and estimate the impact
of re-opening.
9We expect hospitalizations to rebound after reopening on January 31,
and to remain at a prolonged peak, except under the most favourable
assumptions.
Figure shows projections based on models
from two scientific teams. 6000
• Different models use different Example scenarios
approaches and assumptions.
Low community immunity
• Both models are calibrated to case counts (2M with recent infection)
(to mid-December 2021) and hospital
Moderate community immunity
occupancy (one model), or ICU occupancy
Hospital occupancy (ward only)
(2.5M with recent infection)
(one model). 4000
High community immunity
• Models assume 8M Ontarians will have (3M with recent infection)
received booster dose by end of February.
All example scenarios assume
• Scenarios differ by level of community moderate increase in contacts as Range of
immunity and changes in contacts as of of January 31, 2022
January 31, 2022. scenarios considered
• Considerable uncertainty on current 2000
restrictions lift
31 Jan: some
community immunity and changing
clinical presentation with Omicron.
• Accelerating uptake of vaccination,
including boosters, will reduce hospital
admissions.
• Expected increased supply of existing 0
therapeutics may reduce hospital Dec 2021 Jan 2022 Feb 2022 Mar 2022
admissions.
Projections informed by modeling from McMasterU, WesternU 10
Data (hospitalisations): covid-19.ontario.caICU occupancy will likely rebound after reopening on January 31.
Regardless, the pressure on ICUs will be prolonged.
Figure shows projections based on
model from one scientific team.
• Model is calibrated to case
counts (to mid-December
2021) and ICU occupancy. 750
• Range of scenarios shown
corresponds to example
ICU occupancy
scenarios in previous slide Range of
(hospitalizations). scenarios considered
• Considerable uncertainty due 500
to changing clinical
presentation with Omicron,
which may result in lower
restrictions lift
31 Jan: some
proportion of patients needing
ICU care compared to Delta.
250
• Expected increased supply of
existing therapeutics may
further reduce ICU admissions.
Dec 2021 Jan 2022 Feb 2022 Mar 2022
Projections informed by modeling from WesternU 11
Data (hospitalisations): covid-19.ontario.caAcute care capacity is strained.
Patient Transfers by 7-Day Period Required to Pandemic Period Vented Census Compared to Pre-Pandemic Hospital Worker Positivity and Isolation Rates
Redistribute Patient Volumes Across Province (2016-2019) Average
30
140 1200
Daily ICU Vented Census (CRCI and Non-
ICU to ICU
Number of patients transferred
120 25
35 1000
6 7-day Rolling
100 Acute ward bed to 20
20 average positivity
Rate per 1,000*
Acute ward bed 30 800 rate
80 Acute to Post-Acute 16 36
Care Facility (ALC) 15 7-day Rolling
CRCI)*
600 average isolation
60
7 rate (estimated)
10
40 15 76 75 400
62 95% Confidence Intervals
20 43 5
32 200 Mean Daily Vented ICU census Pre-COVID (2016--2019)
7-day Average Total Vented Census (CRCI and non-CRCI)
0 2 7-day Average non-CRCI Vented Census 0
Dec 17- Dec 24 Dec 31 Jan 7 to Jan 14 Jan 21 0
2 Dec 9 Dec 16 Dec 23 Dec 30 Dec 6 Jan 13 Jan 20 Jan
23 to Dec to Jan 6 13 to 20 to 27 2021 2021 2021 2021 2021 2022 2022 2022
20
20
20
20
20
20
20
20
30
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De
M
Ju
Se
De
un
ar
ar
n2
p2
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c2
Week of Transfer Directive
20
20
20
02
02
02
02
02
20
20
21
1
0
0
1
1
Patient transfers have resumed as hospital On January 25, 2022, the total number of patients Spread of COVID-19 affects healthcare
capacity in hardest hit regions becomes receiving mechanical ventilation was 138% of the workers with resulting reduction in staffing.
threatened. Thus far during wave 5, over historic average, and the total number of patients in * Rate includes hospital workers tested positive, but not those
exposed and quarantined, and is therefore an underestimate of
450 patients with COVID-19 have been Ontario ICUs was 111% of historic averages. staffing challenges. Data do not indicate where infection was
transferred to prevent hospital resources acquired. The vast majority of hospital workers acquire COVID-19
*CRCI: COVID-19 related critical illness
being overwhelmed. in the community and not in the hospital setting.
12
Data: Ontario Critical Care COVID Command Centre Data: CCIS provided courtesy of CCSO Data: TAHSN Human Resources Network (test positivity); Analysis: MCTVaccination continues to be highly effective against severe outcomes
(hospital and ICU admission).
Unvaccinated people currently have a 6-fold higher risk of being in the hospital and 12-fold higher risk of
being in the ICU compared to people who received 2 or 3 doses of a COVID-19 vaccine.
COVID-19 Patients in Hospital COVID-19 Patients in ICU
1,200 300
COVID-19 Patients in Hospital per 1 Million Inhabitants
COVID-19 Patients in ICU per 1 Million Inhabitants
1,000 250
Hospital Occupancy Among
800 Unvaccinated People 200
600 150 ICU Occupancy Among
Unvaccinated People
400 Hospital Occupancy Among People 100
Vaccinated with at Least 2 Doses
200 50
ICU Occupancy Among People
Vaccinated with at Least 2 Doses
0 0
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
22
22
22
22
1
1
1
1
-2
-2
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-2
-2
-2
-2
-2
-2
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-2
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-2
-2
-2
-2
-2
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ep
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ct
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- Ja
- Ja
- Ja
Se
Se
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O
O
N
N
-D
-D
-D
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-A
-A
-A
-A
-S
-S
-N
-N
-N
-N
7-
5-
7-
5-
2-
2-
11
25
11
25
21
19
21
19
10
24
14
28
10
24
14
28
16
30
16
30
Date Date
Analysis: Secretariat of the Science Advisory Table (https://covid19-sciencetable.ca/ontario-dashboard/) 13
Data: https://data.ontario.ca/ and CCM plus; estimates of patients in hospital and ICU are age standardized, see dashboard for explanationNew vaccine effectiveness data from Ontario shows strong protection
against severe outcomes with vaccination and the importance of
boosters.
14
Source: ICES (https://www.medrxiv.org/content/10.1101/2021.12.30.21268565v2)Vaccine coverage with 3rd doses in adults and with 1st and 2nd doses
has increased, but is slowing down in all age groups.
Number of Vaccinated Individuals & Doses Administered Percentage Vaccinated, by Age
250,000
Daily Number of Vaccine Doses Administered (7-Day Average)
80+ 3 Doses
12,000,000 1 Dose
2 Doses
Cumulative Number of Vaccinated Individuals
2 Doses 70-79
200,000 1 Dose
10,000,000 3 Doses
60-69
Doses per Day
8,000,000 150,000
50-59
Age (Years)
40-49
6,000,000
100,000 30-39
4,000,000
18-29
50,000
2,000,000 12-17
5-11
0 0
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
1
21
1
1
21
1
21
22
1
1
1
1
21
1
0
02
02
02
02
02
02
02
02
02
02
20
20
20
20
20
-2
-2
-2
-2
-2
-2
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-2
-2
l-2
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Percentage Vaccinated
Ap
De
- Ju
Ja
Ja
Fe
-O
M
-A
-N
-M
-D
-A
-S
7-
6-
22
4-
1-
9-
24
4-
29
16
14
10
19
11
27
Date
Data: https://data.ontario.ca 15
Analysis: Secretariat of the Science Advisory Table (https://covid19-sciencetable.ca/ontario-dashboard/)Key findings
• It is challenging to model the spread of COVID-19 because of changes
in testing, but other indicators suggest this phase of the Omicron wave
has plateaued or is in decline.
• Public health measures helped control this phase. Relaxation of these
measures will increase the spread of COVID-19. The size of any
resurgence is difficult to predict and will depend on vaccination, the
spread of Omicron, and changes in behaviour (e.g., mobility, masking).
• Surveillance will be important to detect changes in the trajectory of the
pandemic.
• Hospitals are now caring for the highest number of people with COVID-
19. Admissions are at highest levels across all age groups. ICU
occupancy continues to be high. Staffing in hospitals remains critical.
• Ontario data shows that vaccination (including third doses) provides
strong protection against serious illness. Increasing vaccine uptake
across all groups will reduce the impact of the pandemic.
16Contributors • Creative Destruction Lab-Rapid Screening Consortium • Hospital Infection Prevention And Control (IPAC) Teams and Data Analytics: Suhair AlShanteer, Michelle Barton Forbes, Steven Bernard, Craig Campbell, Michael Chow, Robert Crawford, Gerald Evans, Jayvee Guerrero, Kevin Katz, Erin Kelleher, Sarah Khan, Ethel Lagman, Melanie Lavigne, Kirk Leifso, Sheri Levesque, Reena Lovinsky, Tracy Macleod, Jennifer McCallum, Dominik Mertz, Vydia Nankoosingh, Kasey Parker, Paul Sandor, Michelle Science, Shawna Silver, Celia So, Rachel Solomon, Nisha Thampi, Alon Vaisman, Elisa Vicencio, Eugene Wong • ICES: Sarah Buchan, Hannah Chung, Kevin Brown, Peter Austin, Deshayne Fell, Jonathan Gubbay, Sharifa Nasreen, Kevin Schwartz, Maria Sundaram, Mina Tadrous, Kumanan Wilson, Sarah Wilson, Jeff Kwong • McMasterU: Irena Papst, Ben Bolker, Jonathan Dushoff, David Earn • Modeling Consensus Table: Kali Barrett, Isha Berry, Sharmistha Mishra, Ashleigh Tuite, Beate Sander • Ontario Health: Erik Hellsten, Stephen Petersen, Anna Lambrinos • Science Advisory Table: Peter Jüni, Nicolas Bodmer, Karen Born, Shujun Yan • TAHSN Human Resources Network: Sunnybrook Health Sciences Centre, North York General Hospital, Trillium Health Partners, Women’s College Hospital, Hospital for Sick Children, Unity Health Network, Humber River Regional Hospital, Scarborough Health Network • Western University/London Health Sciences Centre: Lauren Cipriano, Wael Haddara • St. Michael’s Hospital/University of Toronto: Bruno R. da Costa 17
Content and review by Modelling Consensus and Scientific Advisory
Table members and secretariat
Beate Sander,* Peter Jüni, Brian Schwartz,* Upton Allen, Vanessa Allen, Kali Barrett, Isha Berry,
Nicolas Bodmer, Isaac Bogoch, Karen Born, Kevin Brown, Sarah Buchan, Swetaprovo Chaudhuri,
Yoojin Choi, Lauren Cipriano, Troy Day, David Earn,* Gerald Evans, Jennifer Gibson, Anne Hayes,*
Michael Hillmer, Jessica Hopkins, Jeff Kwong, Fiona Kouyoumdjian, Audrey Laporte, John Lavis,
Gerald Lebovic, Brian Lewis, Stephanie Lockert, Linda Mah, Kamil Malikov, Doug Manuel, Roisin
McElroy, Allison McGeer, Michelle Murti, John McLaughlin, Sharmistha Mishra, Andrew Morris,
Samira Mubareka, Christopher Mushquash, Ayodele Odutayo, Menaka Pai, Alyssa Parpia, 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, Robert
Steiner, Ashleigh Tuite, 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.
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