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Synopsis of Projections for the COVID19 Pandemic COVID19 Projections Workgroup Arizona State University The University of Arizona Facilitated by the Arizona Department of Health Services Models as of 4/8/2020

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Page 1: Synopsis of Projections Workgroup 4.13 · 4/8/2020  · Model Comparison: External models 1 10 100 1,000 10,000 100,000 1,000,000 10,000,000 1 ‐ Apr 3 ‐ Apr 5 ‐ Apr 7 ‐ Apr

Synopsis of Projections for the COVID‐19 Pandemic

COVID‐19 Projections Workgroup Arizona State UniversityThe University of Arizona

Facilitated by the Arizona Department of Health Services

Models as of 4/8/2020

Page 2: Synopsis of Projections Workgroup 4.13 · 4/8/2020  · Model Comparison: External models 1 10 100 1,000 10,000 100,000 1,000,000 10,000,000 1 ‐ Apr 3 ‐ Apr 5 ‐ Apr 7 ‐ Apr

• Arizona‐specific projections were deemed necessary because the Institute for Health Metrics and Evaluation (IHME) University of Washington model in common use was found to be often too optimistic in its projections and does not consider a variety of scenarios.

• Modeling and projections are highly complex, and require focused attention and effort by a team of experts over several weeks and potentially months. 

• ADHS called upon partners at ASU and U of A with special previous experience doing projections and modeling, and who are working with CDC and the National Institutes of Health on COVID‐19 response.

Key findings:  • Based on the latest data and considering multiple possible scenarios, this Arizona‐

specific model predicts infections will peak around the middle of May.• This model is highly‐sensitive to physical distancing and increased temperature. • A wide range in outcomes is still feasible because of uncertainties in the number of 

undetected cases and the effectiveness of physical distancing.

Arizona‐Specific COVID‐19 Epidemic Projection Model

Page 3: Synopsis of Projections Workgroup 4.13 · 4/8/2020  · Model Comparison: External models 1 10 100 1,000 10,000 100,000 1,000,000 10,000,000 1 ‐ Apr 3 ‐ Apr 5 ‐ Apr 7 ‐ Apr

Epidemiology:

• Growth is rapid, but has slowed

• Doubling times– March 17‐24: 1.7 days

– March 25‐April 8: 5.3 days

• Does not include undetected cases

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Arizona COVID‐19 Testing

Positive Deaths

4/8: Growth is slowing down.  3/16 limited gatherings; 3/20 bars and restraurants close; 3/31 stay‐at‐home.  

4/8: ADHS data show positive confirmations are currently 7% of all tests.

Page 4: Synopsis of Projections Workgroup 4.13 · 4/8/2020  · Model Comparison: External models 1 10 100 1,000 10,000 100,000 1,000,000 10,000,000 1 ‐ Apr 3 ‐ Apr 5 ‐ Apr 7 ‐ Apr

Testing:• Information about negative tests results released March 27.– Positive test result drops to <10%.

• Range of 8%‐10% is consistent with other US cities with community spread

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Rolling weekly positive COVID‐19 test rate

Tests Positive %

4/8: Positive test rate has increased to close to 10% indicating higher burden of COVID‐19 disease in population.

Page 5: Synopsis of Projections Workgroup 4.13 · 4/8/2020  · Model Comparison: External models 1 10 100 1,000 10,000 100,000 1,000,000 10,000,000 1 ‐ Apr 3 ‐ Apr 5 ‐ Apr 7 ‐ Apr

Transmission• Early stochastic 

effects• Fast exponential 

growth• Slowing growth 

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AZ confirmed cases ‐ log scale

Early contact based spread. Limited transmission and contact tracing.  Beginnings of community infection.

Undetected disease transmission, sustained community spread, and cases increase in severity.

Reduced transmissiondue (hopefully) to social distancing and increased testing.  

Page 6: Synopsis of Projections Workgroup 4.13 · 4/8/2020  · Model Comparison: External models 1 10 100 1,000 10,000 100,000 1,000,000 10,000,000 1 ‐ Apr 3 ‐ Apr 5 ‐ Apr 7 ‐ Apr

Assumptions & Parameters

Page 7: Synopsis of Projections Workgroup 4.13 · 4/8/2020  · Model Comparison: External models 1 10 100 1,000 10,000 100,000 1,000,000 10,000,000 1 ‐ Apr 3 ‐ Apr 5 ‐ Apr 7 ‐ Apr

Assumptions & Parameters

Page 8: Synopsis of Projections Workgroup 4.13 · 4/8/2020  · Model Comparison: External models 1 10 100 1,000 10,000 100,000 1,000,000 10,000,000 1 ‐ Apr 3 ‐ Apr 5 ‐ Apr 7 ‐ Apr

Pyramid of Disease Severity • The assumed 

parameters in the model are all sourced from recent publications in the literature

• The top of the pyramid implies significant healthcare resource requirements 

TOTAL ASYMPTOMATIC CASES: 18.5%

TOTAL SYMPTOMATIC BUT NONHOSPITALIZED CASES: 65.2%

IN ICU: 7.34%(TOTAL HOSPITALIZED CASES: 16.3%)

VENTILATOR: 6.45%

DEATHS:1.61%

Page 9: Synopsis of Projections Workgroup 4.13 · 4/8/2020  · Model Comparison: External models 1 10 100 1,000 10,000 100,000 1,000,000 10,000,000 1 ‐ Apr 3 ‐ Apr 5 ‐ Apr 7 ‐ Apr

S E

IAsymp

Ipre-symp

ISymp

Ihosp D

R

Ihome

2 days 3 days

3 days

6 days

6 days

2 days

18.5%

20%

ICU

ICUREC

8 days

6 days45%

22%

Arizona COVID‐19 Model

Page 10: Synopsis of Projections Workgroup 4.13 · 4/8/2020  · Model Comparison: External models 1 10 100 1,000 10,000 100,000 1,000,000 10,000,000 1 ‐ Apr 3 ‐ Apr 5 ‐ Apr 7 ‐ Apr

Scenarios:Scenario  Description

Scenario 1.

Assumes all infections are known based on a reporting rate of 9% and ``moderate” influence of physical distancing. The estimate of unreported cases obtained by an estimate provided by Shaman et. al. 2020.  Assumes no additional mitigation.Summer effect is modeled by reducing transmission efficiency by half from May 15. 

Scenario 2.

Assumes a reporting rate of 9% and ``maximal” influence of physical distancing.  Assumes ongoing mitigation.Summer effect is modeled by reducing transmission efficiency by half from May 15.

Scenario 3.

Assumes that the current reported cases reflect the actual number of infected individuals as of 4/8/20 and moderate influence of physical distancing. Assumes no additional mitigation.Summer effect is modeled by reducing transmission efficiency by half from May 15.  

Scenario 4.

Assumes that the current reported cases reflect the actual number of infected individuals as of 4/8/20 and maximal influence of physical distancing. Assumes no ongoing mitigation.Summer effect is modeled by reducing transmission efficiency by half on May 15.  

Scenario 5.

Same as Scenario 1 with extreme summer‐time reduction in transmission efficiency (heat or distancing); Assumes no additional mitigation for physical distancing.

Page 11: Synopsis of Projections Workgroup 4.13 · 4/8/2020  · Model Comparison: External models 1 10 100 1,000 10,000 100,000 1,000,000 10,000,000 1 ‐ Apr 3 ‐ Apr 5 ‐ Apr 7 ‐ Apr

Total Infected • Total infected includes 

asymptomatic and pre‐symptomatic individuals, who may be transmitting the disease

• The sharp decline in Scenario #5 due to the reduction in transmission rate due to summer effect– Assumes May 15 for 

reduction in transmission– Summer effects not yet 

known

Page 12: Synopsis of Projections Workgroup 4.13 · 4/8/2020  · Model Comparison: External models 1 10 100 1,000 10,000 100,000 1,000,000 10,000,000 1 ‐ Apr 3 ‐ Apr 5 ‐ Apr 7 ‐ Apr

Symptomatic Infections 

• A large number of the symptomatic infections will recover at home– Due to physical 

distancing measures, we assumed that these individuals will transmit the disease at a lower rate

Page 13: Synopsis of Projections Workgroup 4.13 · 4/8/2020  · Model Comparison: External models 1 10 100 1,000 10,000 100,000 1,000,000 10,000,000 1 ‐ Apr 3 ‐ Apr 5 ‐ Apr 7 ‐ Apr

Hospitalized Infections

• A portion of the hospitalized infections are in ICU, which we track separately due to the significant resources required to care for ICU patients

• Under our mid‐range scenario (Scenario #2), the number of hospitalized patients hit 13,091 on May 23

• Scenario #4 estimates a max of 1,258 patients on May 23, similar to IHME estimates of 1,203 on April 22

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Patients in ICU• ICU resources can be 

critical to save lives• In particular, several 

sources have pointed to longer ICU stays by patients that eventually recover

• ICU stays can be as long as 14+ days for these patients

Page 15: Synopsis of Projections Workgroup 4.13 · 4/8/2020  · Model Comparison: External models 1 10 100 1,000 10,000 100,000 1,000,000 10,000,000 1 ‐ Apr 3 ‐ Apr 5 ‐ Apr 7 ‐ Apr

Patients on Ventilator

• A significant fraction of patients (~88%) need mechanical ventilators in ICU

• Rate of mortality among patients on mechanical ventilator is higher than other causes of Acute Respiratory Distress Syndrome (ARDS) (~67%)

Page 16: Synopsis of Projections Workgroup 4.13 · 4/8/2020  · Model Comparison: External models 1 10 100 1,000 10,000 100,000 1,000,000 10,000,000 1 ‐ Apr 3 ‐ Apr 5 ‐ Apr 7 ‐ Apr

Maximum Daily Counts: All scenarios

Low High Mid

Peak Infected 6,875         175,695          88,466           

Peak Hopitalized (Daily) 1,259         31,670            15,428           

Peak ICU (Daily) 591            14,981            7,126             

Peak Ventilators (Daily) 520            13,183            6,270             

Page 17: Synopsis of Projections Workgroup 4.13 · 4/8/2020  · Model Comparison: External models 1 10 100 1,000 10,000 100,000 1,000,000 10,000,000 1 ‐ Apr 3 ‐ Apr 5 ‐ Apr 7 ‐ Apr

Projected Infections: Low, medium, high

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Projected Infections: High, Med, Low(Estimate Made 4/8)

High Med Low

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Projected Hospitalizations: Low, medium, high

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Projected ICU visits: Low, medium, high

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Projected Ventilator Use: Low, medium, high

Assumes 88% ventilator utilization for ICU patients

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Model Comparison: External models

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Forecasts of peak week and peak resources ‐ COVID‐19 Arizona

Total  Infected Hospital Beds ICU Vents deaths

healthdata.org

Joe Gerard

Act now ‐worst case

Tim LantCurrent Model ‐Middle Scenario

Page 22: Synopsis of Projections Workgroup 4.13 · 4/8/2020  · Model Comparison: External models 1 10 100 1,000 10,000 100,000 1,000,000 10,000,000 1 ‐ Apr 3 ‐ Apr 5 ‐ Apr 7 ‐ Apr

Authors and Contributors:Team 1: The Biodesign Institute, Knowledge EnterpriseArizona State UniversityTim Lant, PhD, MAS; Megan Jehn, PhD; Esma Gel, PhD; Anna Muldoon, MPH; Heather Ross PhD, DNP, ANP‐BC;Neal W. Woodbury, PhD;

Team 2: The Center for Population Health SciencesArizona Health Sciences, the Zuckerman College of Public Health, and the Center forBiomedical Informatics and BiostatisticsUniversity of ArizonaJoe K. Gerald, MD, PhD;Patrick Wightman, PhD, MPP;Kathy Hiller, MD, MPH, FACEP;Vern Pilling;

Page 23: Synopsis of Projections Workgroup 4.13 · 4/8/2020  · Model Comparison: External models 1 10 100 1,000 10,000 100,000 1,000,000 10,000,000 1 ‐ Apr 3 ‐ Apr 5 ‐ Apr 7 ‐ Apr

Authors and Contributors:Team 3:  The Center for Health Information and Research (CHIR)Arizona State UniversityGeorge Runger, PhD;Anita Murcko, MD, FACP;Dieter Armbruster, PhD;Gevork Harootunian;Klim Drobnyh;Seho Kee;Maziar Roodsari;Basam Alasaly;Nassim Idouraine;Logan Cameron;

Team 4: Seidman Research Institute, WP Carey School of Business, Arizona State UniversityTim James, PhDTimothy J. Richards, PhDMark Manfredo, PhD