how to model “diseases” and use those models to drive ......how to model “diseases” and use...
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How to Model “Diseases” and use those Models to drive timely decisions and actions that
produce rapid benefits
Julie Swann, Ph.D.
Department Head, ISE
NC State University
Webinars that Matter in Times of Turblence18 Aug 2020
Scott Sink (Organizer/Host/Moderator)
CISE/IISE Global Webinar Program LeadPinar Kesinocak, Ph.D.
Director, Center for
Health and
Humanitarian Systems,
Ga Tech
IISE Professional Affinity Groups
IISE “Affinity Groups” are ‘Sponsoring’ this
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Housekeeping
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You will receive a follow up e-mail tomorrow around this time with
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etails.aspx?id=46729
Agenda Highlights
11:00-11:07
▪ Scott Tee-up
11:07-11:45
▪ Julie and Pinar Sharing
11:45-12:00
▪ Q&A and Wrap-up and Coming Attractions
The overarching ‘Model’ for our
Performance Excellence Webinar Series
▪ ISE’s Engineer Value
▪ We do that by Integrating Strategy, Process,
Technology and People in ways that enable and
drive Performance Excellence.
▪ The use of Data and Facts is a
component of “Technology”
▪ Analytics has become an increasingly key success
factor for ISE and many other disciplines.
▪ This webinar is the latest in a series of Webinars we
have provided on Analytics to drive improved
Decision Support and Benefits Realization and also
ways that ISE’s are contributing to addressing the
current ‘Crisis’.
Performance Excellence
Organizational Alignment
Process
Strategy
Technology
People
Trust&
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Enhancing the way you think
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Leveraging hyperconnectivity and
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Changing the way we exchange
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manage our culture.
What we do and how we do what we
do.
IISE’s Global Response—We Engineer Value (Even in Times of Major Disruption)
30 Jan 2020: How to Design and Execute Flow Workshops in Healthcare—OSU
University Hospital East Case Example (Scott Sink and Olivia Vance)
25 Feb 2020: Agile Principles and Methods to Accelerate Critical Process
Innovation and Improvement—Joan Tafoya and Caitlyn Kenney
19 March 2020: Creating Cultures to Support Performance Excellence (crucial
foundational element for surviving major disruptions!!) (David Poirier, President IISE)
Webinars that Matter in the face of COVID-19
Engineering Management Systems to Ensure
Survival and Success
If you missed these timely, great Webinars, go to this link on the
IISE Website and get to them.
https://www.iise.org/details.aspx?id=46729
Navigating your Business Through the COVID-19 Crisis—7 April
Business Continuity Strategies and Tactics in Periods of Major Disruption—16 April
James A. Tompkins Ph.D.
Chairman, Tompkins
International
Engineering Management Systems to Ensure
Survival and Success
David Poirier, P.E.
CEO The Poirier Group
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etails.aspx?id=46729
And to get to these best sellers, same location/link…..
Pro jec t “V”F r o m A n
Indus t r i a l Eng ineer ing Perspec t i ve
Steve SavoieSr Manager Industrial Engineering Process & Integration
May, 2020
Resilience Reexamined: Reengineering How We do Business and Ensure Public Safety
Best-in-Class Case Study--Utah
Vinny Monteiro
Goldratt Consulting
Member IISE Society for Health
Systems
Pioneering and Engineering a New WorldJim DobsonSenior Manager, Business Planning & Industrial EngineeringWalt Disney Parks, Experiences, and Products
Rescheduled due to Logmein Audio Failure Mode.
Now 16 June 11-12 EDT
https://attendee.gotowebinar.com/register/538471
5667845858320
The Role of Data and Information(Engineered Management Systems)
in Periods of Major Disruption
Driving Benefits Realization Faster with Operational Analytics
Jared Frederici,
North American Program Lead,
The Poirier Group
Ben Amaba,
Global Chief Technology Officer
Data Analytics and AI Elite Team,
Industrial Manufacturing
IBM
https://www.iise.org/details.aspx?id=46729
Covid-19 is Amplifying the Need for Data & Information-Based Decisions Rapidly, w/ Minimal Latency, and Maximum “Ah-Ha”.
12
Must do #1, Reduce Latency to Decision to
Results
• Data capture must be efficient, effective, reliable
• Analysis must provide “fastest path” to CDA
• Positioning strategy must result in rapid
alignment to get to correct decision fast
Must do #2, Accelerate the Triangle
• Organizations must make the quick judgement –
throw out or keep w/ 80% probability
• Support staff must integrate data creatively, from
multiple sources, rapidly using atypical tools
• Visualizations must minimize the latency to get to
the “Ah-Ha” moment
Traditional Strategies Need Modified to Position Organizations
Correctly to Stay Ahead of the Curve
Demystifying Disease Modeling: Part 1
Aug 18, 2020
Julie SwannDepartment Head, and A. Doug Allison Distinguished Professor
Industrial and Systems Engineering, NC State University
Co-founder, Center for Health and Humanitarian Systems at Georgia Tech
13
DISEASE MODELING
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Disease Modeling
• Non-communicable diseases
– Cancer, diabetes, asthma, depression
• Infectious diseases (person-to-person transmission)
– Measles, influenza, HIV, Covid-19
• Infectious diseases (vector-based)
– Malaria, guinea worm
15
PreventionDetection
& DiagnosisTreatment
Prevent recurrence
Foundational Infectious Disease Model:
SEIR
S (usceptible) E (xposed) I (nfectious) R (ecovered)
3-5
days~14
days
• Values for virus SARS-CoV-2 that causes COVID-19 have been pulled from multiple references
• Ex: https://github.com/midas-network/COVID-19/tree/master/parameter_estimates/2019_novel_coronavirus
16
Typical person infects 2-3 others
Unmitigated, # of cases doubles
in ~ 6 days
Foundational Disease Model: SEIR
• How infectious is the virus that causes COVID-19?– R0 is the average number of people infected by someone who is
infectious (at the beginning of the outbreak, without interventions)
– Doubling rate of cases is a related measure
– More infectious than influenza but less than measles
S (usceptible) E (xposed)
I (nfectious)
R (ecovered)
17
I (nfectious)
Foundational Disease Model: SEIR
S (usceptible) E (xposed) R (ecovered)
(Death)
I-Presymptomatic I-Asymptomatic
I-Symptomatic
I-Hospitalized
40%?
• Rates differ by age
• Asymptomatic infections
important
• Mortality rates
18
1% of Exposed or
3% of Symptomatic?
Foundational Disease Model: SEIR
• Reproductive rate (R0) determines herd immunity
– Recall R0 is average number of new infections caused by recently infected person (no mitigation)
• So for R0 = 3? (66.7%)
19
Dick Larson, https://pubsonline.informs.org/do/10.1287/orms.2020.03.03/full/
R0 = 2.0
• Mosquito has time
to infect 2 cows
• Outbreak cannot
grow if 50% of
cows are immune
R0 = 5.0
• Mosquito has time
to infect 5 cows
• Outbreak cannot
grow if 80% of
cows are immune
Foundational Disease Model: SEIR
S (usceptible) E (xposed) I (nfectious) R (ecovered)
3-5
days~14
days
• But what about other people??
20
https://www.washingtonpost.com/graphics/2020/world/coro
na-simulator/
Foundational Disease Model: SEIR
S (usceptible) E (xposed) I (nfectious) R (ecovered)
3-5
days
~14
days
• But what about other people??
21
https://www.washingtonpost.com/graphics/2020/world/coro
na-simulator/
Community
• Households in each
neighborhood
– Size
– Family or not
– Presence of kids
– Ages
22
Community Transmission
• Households in each census tract
– Size
– Family or not
– Presence of kids
– Ages
• Infections within
– Schools (horizontal purple)
– Workplaces (vertical blue)
– Community (1 represented)
School
School
School
Work
Work
Community
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Community Mitigation
School
School
School
Work
Work
Community• Households
– Voluntary quarantine
• Schools
– Closures, distancing, vacating
dorms
• Workplaces
– Telecommuting, essential only,
distancing
• Community
– Sports, churches, non-
essential businesses
• Face coverings (all)
24
Uses of Models
• Understand the disease better
• Forecast the future
• Gain insight about critical factors
• Evaluate policy or operational decisions
• Integrate analytics into decision making
25
COVID-19 MODELING:
PINAR KESKINOCAK (GT)
26
COVID19 MODELING AND EVALUATING INTERVENTION STRATEGIES
Pinar Keskinocak, Ph.D. Nicoleta Serban, Ph.D.
William W. George Chair and Professor, Virginia C. and Joseph C. Mello Professor,
Stewart School of Industrial & Systems Engineering Stewart School of Industrial & System Engineering
Director, Center for Health and Humanitarian Systems
Team: John Asplund, Ph.D.; Arden Baxter; Buse Eylul Oruc; Pravara Harati;
Tyler Perini; Melody Shellman; Chris Stone; Zhuoting (April) Yu
AREAS OF RESEARCH
• Agent Based Simulation Model (ABM): Project infection spread geographically and over time, utilizing detailed community-level and individual-level data
• Resource Shortage Calculator: Estimate hospital beds, ICU beds, ventilators given the estimates of new hospitalizations from ABM
• Hot Spot Detection & Prediction: Identify change points on potential outbreaks;
Statistical learning and inference after controlling for the trend due to increasing
testing
• Resource Allocation Optimization: How to allocate limited capacity (i) between diagnostic vs PCR testing; (ii) between high accuracy & slow vs lower accuracy & fast testing; (iii) of vaccine geographically and over time.
INFECTION SPREAD ESTIMATES – AGENT-BASED SIMULATION MODELING
Data: Various data sources, including
• Georgia data: Household statistics, workflow data, classroom sizes, age statistics
Modeling: Agent-based simulation
S: Susceptible
E: Exposed
IP: Presymptomatic
IA: Asymptomatic
IS: Symptomatic
R: Recovered
IH: Hospitalized
D: DeadpD
1-pD
pH
1-pH
1-pA
pA
S E IP R
IA
DIS IH
School
Household 1 Household 2
Household 3
Workplace
Community 1
Community 2
Household 4
REFERENCES FOR MODEL PARAMETERS
Parameters Estimates References
Probability of Symptomatic 0.65 [1] [2]
Probability of Hospitalization
0.00533 for age 0-19,
0.06 for age 20-64,
0.1 for age 65+
[3]
Probability of Death
0 for age 0-19,
0.0515 for age 20-64,
0.3512 for age 65+
[3]
R_02.4 [4] [5] [6]
2.3 [7]
Beta 1.12 [5]
Exposed DurationWeibull with mean 1.5 days [8]
Weibull with mean 4.6 days [9] [10]
Pre-symptomatic Duration 0.5 days [11]
Hospitalized Duration Exponential with mean 10.4 days [11] [12]
Symptomatic Duration Exponential with mean 2.9 days [13]
Symptomatic Asymptomatic Duration
Ratio 1.5 [11]
Theta 0.48 [14]
Omega 0.24 [14]
Parameters Estimates References
Average length of stay in hospital
(days)10.4 [9][10]
Percent of hospitalized young (0-
19) needing ICU treatment0% [3]
Percent of hospitalized adults
(20-64) needing ICU treatment 20.53% [3]
Percent of hospitalized elderly
(65+) needing ICU treatment28.11% [3]
Average length of stay in ICU
(days)8 [13]
Percent of ICU patients that
require ventilation64% [15]
Average length of stay on
ventilation (days)6 [16]
• Georgia-specific Covid-19 Data
o Population at-risk based on prevalence of co-morbidities
o DPH data reported on hospitalizations
o The New York Times (https://www.nytimes.com/interactive/2020/us/coronavirus-us-cases.html) -- the number of Cases and Deaths of COVID-19 for each county
• U.S. Census Bureau; American Community Survey, 2017 American Community Survey 5-year Estimates (data.census.gov)
o Age stratified population for Georgia.
o Number of family and non-family households in Georgia stratified by the number of people in the home.
o Number of children in households in Georgia.
• U.S. Census Bureau; Census Transportation Planning Products, 5-year data (2012-2016) (http://data5.ctpp.transportation.org)
o Flows for total workers (workers 16 years or older) in Georgia at the census tract level.
o Shapefile for each county in the U.S.
DATA SOURCES
SCENARIOS FOR PHYSICAL DISTANCING STRATEGIES
DAILY NEW INFECTIONS PROJECTIONS IN GEORGIA
DAILY NEW INFECTIONS PROJECTIONS IN GEORGIA
DAILY NEW INFECTIONS PROJECTIONS IN GEORGIA
HEALTHCARE RESOURCE ESTIMATION
SCENARIOS FOR K-12 SCHOOL REOPENING
HEALTH, SOCIETY, ECONOMY, ETC. –COMPLEX TRADEOFFS
S
C
Low to High
VQ
SIP
Only
VQ &
SC
S
C
Low to High
VQ
VQ &
SC
SIP
Only
S
C
Low to High
VQ
SIP
Only
VQ &
SC
HOT SPOTS
https://chhs.gatech.edu/covid19-dashboard
1. Mizumoto, K., et al., Estimating the asymptomatic proportion of coronavirus disease 2019 (COVID-19) cases on board the Diamond Princess cruise ship, Yokohama, Japan, 2020.Eurosurveillance, 2020. 25(10): p. 2000180.
2. Mandavilli, A. Infected but Feeling Fine: The Unwitting Coronavirus Spreaders. 2020 31 March 2020].
3. Team, C.C.-R., Severe Outcomes Among Patients with Coronavirus Disease 2019 (COVID-19) — United States, February 12–March 16, 2020. 2020, CDC: Morbidity and Mortality Weekly Report (MMWR).
4. Patrick GT Walker, C.W., Oliver Watson, Marc Baguelin, Kylie E C Ainslie, Sangeeta Bhatia, Samir, et al., The Global Impact of COVID-19 and Strategies for Mitigation and Suppression. 2020, Imperial College COVID-19 Response Team.
5. Li, R., et al., Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV2). Science, 2020: p. eabb3221.
6. WHO, Report of the WHO-China Joint Mission on Coronavirus Disease 2019 (COVID-19) 2020, World Health Organization.
7. Linton, N.M., et al., Incubation Period and Other Epidemiological Characteristics of 2019 Novel Coronavirus Infections with Right Truncation: A Statistical Analysis of Publicly Available Case Data. medRxiv, 2020: p. 2020.01.26.20018754.
8. Li, Q., et al., Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus–Infected Pneumonia. New England Journal of Medicine, 2020. 382(13): p. 1199-1207.
9. Neil M Ferguson, D.L., Gemma Nedjati-Gilani, Natsuko Imai, Kylie Ainslie, Marc Baguelin,, et al., Impact of non-pharmaceutical interventions (NPIs) to reduce COVID19 mortality and healthcare demand. 2020, Imperial College London.
10. Weitz, J., Intervention Serology and Interaction Substitution: Exploring the Role of `Immune Shielding' in Reducing COVID-19 Epidemic Spread 2020.
11. Riou, J., et al., Adjusted age-specific case fatality ratio during the COVID-19 epidemic in Hubei, China, January and February 2020. medRxiv, 2020: p. 2020.03.04.20031104.
12. Ganyani, T., et al., Estimating the generation interval for COVID-19 based on symptom onset data. medRxiv, 2020: p. 2020.03.05.20031815.
13. F. Zhou, T. Yu, and R. Du, Clinical course and risk factors for mortality of adult inpatients with covid-19 in wuhan, china: a retrospective cohort study, The Lancet, 2020.
14. HME COVID-19 health service utilization forecasting team, Christopher JL Murray, Forecasting COVID-19 impact on hospital bed-days, ICU-days, ventilator-days and deaths by US state in the next 4 months, medRxiv, 2020.
15. Health GDoP, Georgia Department of Public Health COVID-19 Daily Status Report. Published 2020. Updated 14 April 2020. Acessed 14 April 2020, 2020.
16. J. Xie, Z. Tong, and X. Guan, Critical care crisis and some recommendations during thecovid-19 epidemic in china, Intensive Care Med, 2020.
REFERENCES FOR MODEL PARAMETERS
Conclusions
• Disease models are often based on sophisticated
mathematics and high-performance computing
• “Forecasts are always wrong” yet insights can be helpful
• Models often assume behaviors stay the same as today,
while models (or change in risk) can change behavior
• Always challenge assumptions and input data
• Organizations can plan ahead with advanced analytics
• Engineers can help solve public health problems, too
Contact Information for our two Speakers
Dr. Pinar Keskinocak
Dr. Julie Swann
42
You can download the deck (handouts)
You will receive an e-mail tomorrow with link to recording.
You can go to this IISE link soon and get deck and recording.
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How to Model “Diseases” and use those Models to drive timely decisions and actions that produce rapid benefits
Julie Swann and Pinar Keskinocak
You can download the deck (handouts)
You will receive an e-mail tomorrow with link to recording.
You can go to this IISE link soon and get deck and recording.
https://www.iise.org/details.aspx?id=46729
Thank You!
An e-mail tomorrow from Go2Webinar will provide a link to the recording and you can also access the presentation and recording on the IISE website.
https://www.iise.org/details.aspx?id=46729
Contact us for More Info or to provide feedback:
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48