ai and ml for predicting covid-19
TRANSCRIPT
![Page 1: AI and ML for Predicting COVID-19](https://reader031.vdocuments.site/reader031/viewer/2022012512/618ab28248e8ed65cd6332ad/html5/thumbnails/1.jpg)
AI and ML for Predicting COVID-19
Malik Magdon-Ismail,Computer Science, Rensselaer.
Shout-Out:
Rensselear IDEA
J. Hendler, K. Bennet, J. Erickson, MANY good students.
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Scales of COVID-19
World ∼ 8 billion
Creator: M. Magdon-Ismail, November 12, 2020 AI/ML for COVID-19: 2 / 11 Two Faces →
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Scales of COVID-19
USA ∼ 330 million
Creator: M. Magdon-Ismail, November 12, 2020 AI/ML for COVID-19: 2 / 11 Two Faces →
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Scales of COVID-19
NY State ∼ 20 million
Creator: M. Magdon-Ismail, November 12, 2020 AI/ML for COVID-19: 2 / 11 Two Faces →
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Scales of COVID-19
Albany/Troy/Cap Dist ∼ 1 million
Creator: M. Magdon-Ismail, November 12, 2020 AI/ML for COVID-19: 2 / 11 Two Faces →
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Scales of COVID-19
Rensselaer ∼ 10 thousand
New Infections Over Previous 14 Days
# Students: 6806
Testing: every 7 days, 0% of students
Infections: 1.4%
Budget: 100000 tested
R0: 7.44
R(no test): 1.64
R(test): 1.64
Jan 24
Feb 13
Mar 5
Mar 25
Apr 14
0
1
5
10
20
Infe
ctio
n C
ount
(% o
f stu
dent
s)
Creator: M. Magdon-Ismail, November 12, 2020 AI/ML for COVID-19: 2 / 11 Two Faces →
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Scales of COVID-19
Party at Rensselaer ∼ 20
Chances to Get COVID on 14-Feb-2021 (no masks)
20 40 60 80 100
Size of Event/Party
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5H
ours
at E
vent/P
art
y
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.1
Creator: M. Magdon-Ismail, November 12, 2020 AI/ML for COVID-19: 2 / 11 Two Faces →
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Scales of COVID-19
vaccines, virology, genomics
Creator: M. Magdon-Ismail, November 12, 2020 AI/ML for COVID-19: 2 / 11 Two Faces →
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Two Sides of COVID Modeling
Epidemiological ModelingHarvard-model, Imperial-model, UW-model, Your-model, My-model, . . .
AI and Machine Learning PredictionWhat the data says vs. What we think ought to be.
Engineering success vs. Biological correctness.
Creator: M. Magdon-Ismail, November 12, 2020 AI/ML for COVID-19: 3 / 11 The Challenge →
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The Race To Predict Ventilator Demand
NYC Capital District
Mar/02 Mar/10 Mar/18 Mar/260
1
4
9
16
25
36
Mar/04 Mar/10 Mar/16 Mar/220
5
10
15
20
25
30
35
40
45
Infection counts: very noisy dirty data.Predictions must be local: mobility patterns, density, social distancing, weather, . . . .
Smaller regions: more noisy; more sparse.
Creator: M. Magdon-Ismail, November 12, 2020 AI/ML for COVID-19: 4 / 11 A Easier Example →
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A Easier Example
True “biological” law: quadratic growth.
Quadratic Fit + Extrapolate
1 2 3 4 5 60
10
20
30
40
50
60
70
80 Observed
True Quadratic Law
Creator: M. Magdon-Ismail, November 12, 2020 AI/ML for COVID-19: 5 / 11 Regularization →
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A Easier Example
True “biological” law: quadratic growth.
Quadratic Fit + Extrapolate
1 2 3 4 5 60
10
20
30
40
50
60
70
80 Observed
True Quadratic Law
Quadratic Fit
Creator: M. Magdon-Ismail, November 12, 2020 AI/ML for COVID-19: 5 / 11 Regularization →
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A Easier Example
True “biological” law: quadratic growth.
Quadratic Fit + Extrapolate Linear Fit + Extrapolate
1 2 3 4 5 60
10
20
30
40
50
60
70
80 Observed
True Quadratic Law
Quadratic Fit
1 2 3 4 5 60
10
20
30
40
50
60
70
80 Observed
True Quadratic Law
Linear Fit
Creator: M. Magdon-Ismail, November 12, 2020 AI/ML for COVID-19: 5 / 11 Regularization →
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A Easier Example
True “biological” law: quadratic growth.
Quadratic Fit + Extrapolate Linear Fit + Extrapolate
1 2 3 4 5 60
10
20
30
40
50
60
70
80
1 2 3 4 5 60
10
20
30
40
50
60
70
80
Eout ≈ 34 Eout ≈ 14 ✓
Creator: M. Magdon-Ismail, November 12, 2020 AI/ML for COVID-19: 5 / 11 Regularization →
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A Stunning Nugget From The Theory of Learning
When there is noise,
Simpler can be better than correct.
1 2 3 4 5 60
10
20
30
40
50
60
70
80
1 2 3 4 5 60
10
20
30
40
50
60
70
80
What we would like to learn versus what we can learn.The data determines what we can learn
Harvard-model, Imperial-model, UW-model, Your-model, My-model, . . .
Creator: M. Magdon-Ismail, November 12, 2020 AI/ML for COVID-19: 6 / 11 Let’s Predict →
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A Stunning Nugget From The Theory of Learning
When there is noise,
Simpler can be better than correct.
1 2 3 4 5 60
10
20
30
40
50
60
70
80
1 2 3 4 5 60
10
20
30
40
50
60
70
80
What we would like to learn versus what we can learn.The data determines what we can learn
Harvard-model, Imperial-model, UW-model, Your-model, Simple–robust–adaptable model, . . .
Creator: M. Magdon-Ismail, November 12, 2020 AI/ML for COVID-19: 6 / 11 Let’s Predict →
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Let’s Predict For The Capital District
Mar/04 Apr/03 May/03 Jun/020
50
100
150
200
250
How quickly is it spreading?
How large is the pasture?
Capital District ∼ 1M.
Extrapolation is hard.
Creator: M. Magdon-Ismail, November 12, 2020 AI/ML for COVID-19: 7 / 11 Keep It Simple →
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Let’s Predict For The Capital District
Mar/04 Apr/03 May/03 Jun/020
50
100
150
200
250
How quickly is it spreading?
How large is the pasture?
Capital District ∼ 1M.
Extrapolation is hard.
Creator: M. Magdon-Ismail, November 12, 2020 AI/ML for COVID-19: 7 / 11 Keep It Simple →
![Page 19: AI and ML for Predicting COVID-19](https://reader031.vdocuments.site/reader031/viewer/2022012512/618ab28248e8ed65cd6332ad/html5/thumbnails/19.jpg)
Let’s Predict For The Capital District
Mar/04 Apr/03 May/03 Jun/020
50
100
150
200
250
How quickly is it spreading?
How large is the pasture?
Capital District ∼ 1M.
Extrapolation is hard.
Disaster!
Creator: M. Magdon-Ismail, November 12, 2020 AI/ML for COVID-19: 7 / 11 Keep It Simple →
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Let’s Predict For The Capital District
Mar/04 Apr/03 May/03 Jun/020
50
100
150
200
250
changepoint
How quickly is it spreading?
How large is the pasture?
Capital District ∼ 1M.
Extrapolation is hard.
Changepoints make it impossible.
Disaster!
Creator: M. Magdon-Ismail, November 12, 2020 AI/ML for COVID-19: 7 / 11 Keep It Simple →
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Keep It Simple, Really Simple. But, Adaptive
U M
S
R
βMU/N
γ∆M(t − k)
(1 − γ)∆M(t − k)
U: Uninfected.
M: Contagious.
S: Symptomatic.
R: Recovered.
Parameters:N, β, α, γ.
Robust changepoints.
1 Robustly determine changepoints.
2 Robustly fit. Gray is uncertainty.
3 State persists across changepoints.
Creator: M. Magdon-Ismail, November 12, 2020 AI/ML for COVID-19: 8 / 11 COVID-War-Room →
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Keep It Simple, Really Simple. But, Adaptive
U M
S
R
βMU/N
γ∆M(t − k)
(1 − γ)∆M(t − k)
U: Uninfected.
M: Contagious.
S: Symptomatic.
R: Recovered.
Parameters:N, β, α, γ.
Robust changepoints.
1 Robustly determine changepoints.
2 Robustly fit. Gray is uncertainty.
3 State persists across changepoints.
Creator: M. Magdon-Ismail, November 12, 2020 AI/ML for COVID-19: 8 / 11 COVID-War-Room →
![Page 23: AI and ML for Predicting COVID-19](https://reader031.vdocuments.site/reader031/viewer/2022012512/618ab28248e8ed65cd6332ad/html5/thumbnails/23.jpg)
Keep It Simple, Really Simple. But, Adaptive
U M
S
R
βMU/N
γ∆M(t − k)
(1 − γ)∆M(t − k)
U: Uninfected.
M: Contagious.
S: Symptomatic.
R: Recovered.
Parameters:N, β, α, γ.
Robust changepoints.
1 Robustly determine changepoints.
2 Robustly fit. Gray is uncertainty.
3 State persists across changepoints.
Creator: M. Magdon-Ismail, November 12, 2020 AI/ML for COVID-19: 8 / 11 COVID-War-Room →
![Page 24: AI and ML for Predicting COVID-19](https://reader031.vdocuments.site/reader031/viewer/2022012512/618ab28248e8ed65cd6332ad/html5/thumbnails/24.jpg)
Keep It Simple, Really Simple. But, Adaptive
U M
S
R
βMU/N
γ∆M(t − k)
(1 − γ)∆M(t − k)
U: Uninfected.
M: Contagious.
S: Symptomatic.
R: Recovered.
Parameters:N, β, α, γ.
Robust changepoints.
1 Robustly determine changepoints.
2 Robustly fit. Gray is uncertainty.
3 State persists across changepoints.
Creator: M. Magdon-Ismail, November 12, 2020 AI/ML for COVID-19: 8 / 11 COVID-War-Room →
![Page 25: AI and ML for Predicting COVID-19](https://reader031.vdocuments.site/reader031/viewer/2022012512/618ab28248e8ed65cd6332ad/html5/thumbnails/25.jpg)
Keep It Simple, Really Simple. But, Adaptive
U M
S
R
βMU/N
γ∆M(t − k)
(1 − γ)∆M(t − k)
U: Uninfected.
M: Contagious.
S: Symptomatic.
R: Recovered.
Parameters:N, β, α, γ.
Robust changepoints.
1 Robustly determine changepoints.
2 Robustly fit. Gray is uncertainty.
3 State persists across changepoints.
How: Even simpler analytic model pre-calibrates.
Creator: M. Magdon-Ismail, November 12, 2020 AI/ML for COVID-19: 8 / 11 COVID-War-Room →
![Page 26: AI and ML for Predicting COVID-19](https://reader031.vdocuments.site/reader031/viewer/2022012512/618ab28248e8ed65cd6332ad/html5/thumbnails/26.jpg)
Keep It Simple, Really Simple. But, Adaptive
U M
S
R
βMU/N
γ∆M(t − k)
(1 − γ)∆M(t − k)
U: Uninfected.
M: Contagious.
S: Symptomatic.
R: Recovered.
Parameters:N, β, α, γ.
Robust changepoints.
We get current state:
Infected and contagious. Immune. Social distancing.
Predictions assuming stabilized behavior.
Creator: M. Magdon-Ismail, November 12, 2020 AI/ML for COVID-19: 8 / 11 COVID-War-Room →
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COVID-War-Room https://covidwarroom.idea.rpi.edu
Capital District North Carolina
All US Counties. All Countries.
Creator: M. Magdon-Ismail, November 12, 2020 AI/ML for COVID-19: 9 / 11 COVID-Back-To-School →
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COVID-Back-To-School https://covidspread.idea.rpi.edu
Who’s bringing covid to campus?
Ambient county infection rate?
COVID-War-RoomJan 19:
∼24 cases,∼20% immunity.
Creator: M. Magdon-Ismail, November 12, 2020 AI/ML for COVID-19: 10 / 11 Tools to Policy →
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COVID-Back-To-School https://covidspread.idea.rpi.edu
Infection Growth from Start of Semester
# Students: 6806
Testing: every 7 days, 0% of students
Infections: 9.1%
Budget: 100000 tested
R0: 7.44
R(no test): 1.64
R(test): 1.64
Jan 2
4
Feb 13
Mar
5
Mar 2
5
Apr 14
0
1
5
10
20
Infe
ction C
ount (%
of stu
dents
)
New Infections Over Previous 14 Days
# Students: 6806
Testing: every 7 days, 0% of students
Infections: 1.4%
Budget: 100000 tested
R0: 7.44
R(no test): 1.64
R(test): 1.64
Jan 2
4
Feb 13
Mar
5
Mar 2
5
Apr 14
0
1
5
10
20
Infe
ction C
ount (%
of stu
dents
)
Creator: M. Magdon-Ismail, November 12, 2020 AI/ML for COVID-19: 10 / 11 Tools to Policy →
![Page 30: AI and ML for Predicting COVID-19](https://reader031.vdocuments.site/reader031/viewer/2022012512/618ab28248e8ed65cd6332ad/html5/thumbnails/30.jpg)
COVID-Back-To-School https://covidspread.idea.rpi.edu
Infection Growth from Start of Semester
# Students: 6806
Testing: every 7 days, 0% of students
Infections: 9.1%
Budget: 100000 tested
R0: 7.44
R(no test): 1.64
R(test): 1.64
Jan 2
4
Feb 13
Mar
5
Mar 2
5
Apr 14
0
1
5
10
20
Infe
ction C
ount (%
of stu
dents
)
New Infections Over Previous 14 Days
# Students: 6806
Testing: every 7 days, 0% of students
Infections: 1.4%
Budget: 100000 tested
R0: 7.44
R(no test): 1.64
R(test): 1.64
Jan 2
4
Feb 13
Mar
5
Mar 2
5
Apr 14
0
1
5
10
20
Infe
ction C
ount (%
of stu
dents
)
Chances to Get COVID on 14-Feb-2021 (no masks)
20 40 60 80 100
Size of Event/Party
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
Hours
at E
vent/P
art
y
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.1Chances to Get COVID on 14-Feb-2021 (masks)
20 40 60 80 100
Size of Event/Party
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
Hours
at E
vent/P
art
y
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.1
Creator: M. Magdon-Ismail, November 12, 2020 AI/ML for COVID-19: 10 / 11 Tools to Policy →
![Page 31: AI and ML for Predicting COVID-19](https://reader031.vdocuments.site/reader031/viewer/2022012512/618ab28248e8ed65cd6332ad/html5/thumbnails/31.jpg)
COVID-Back-To-School https://covidspread.idea.rpi.edu
Infection Growth from Start of Semester
# Students: 6806
Testing: every 7 days, 0% of students
Infections: 9.1%
Budget: 100000 tested
R0: 7.44
R(no test): 1.64
R(test): 1.64
Jan 2
4
Feb 13
Mar
5
Mar 2
5
Apr 14
0
1
5
10
20
Infe
ction C
ount (%
of stu
dents
)
New Infections Over Previous 14 Days
# Students: 6806
Testing: every 7 days, 0% of students
Infections: 1.4%
Budget: 100000 tested
R0: 7.44
R(no test): 1.64
R(test): 1.64
Jan 2
4
Feb 13
Mar
5
Mar 2
5
Apr 14
0
1
5
10
20
Infe
ction C
ount (%
of stu
dents
)
Chances to Get COVID on 14-Feb-2021 (no masks)
20 40 60 80 100
Size of Event/Party
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
Hours
at E
vent/P
art
y
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.1Chances to Get COVID on 14-Feb-2021 (masks)
20 40 60 80 100
Size of Event/Party
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
Hours
at E
vent/P
art
y
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.1
Creator: M. Magdon-Ismail, November 12, 2020 AI/ML for COVID-19: 10 / 11 Tools to Policy →
![Page 32: AI and ML for Predicting COVID-19](https://reader031.vdocuments.site/reader031/viewer/2022012512/618ab28248e8ed65cd6332ad/html5/thumbnails/32.jpg)
COVID-Back-To-School https://covidspread.idea.rpi.edu
Infection Growth from Start of Semester
# Students: 6806
Testing: every 7 days, 20% of students
Infections: 1.6%
Budget: 100000 tested
R0: 7.44
R(no test): 1.06
R(test): 0.98
Jan 2
4
Feb 13
Mar
5
Mar 2
5
Apr 14
0
1
5
10
20
Infe
ction C
ount (%
of stu
dents
)
New Infections Over Previous 14 Days
# Students: 6806
Testing: every 7 days, 20% of students
Infections: 0.9%
Budget: 100000 tested
R0: 7.44
R(no test): 1.06
R(test): 0.98
Jan 2
4
Feb 13
Mar
5
Mar 2
5
Apr 14
0
1
5
10
20
Infe
ction C
ount (%
of stu
dents
)
Chances to Get COVID on 14-Feb-2021 (no masks)
20 40 60 80 100
Size of Event/Party
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
Hours
at E
vent/P
art
y
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.1Chances to Get COVID on 14-Feb-2021 (masks)
20 40 60 80 100
Size of Event/Party
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
Hours
at E
vent/P
art
y
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.1
Rensselaer: 1.5% ≈ 60. 18 infections so far.
Creator: M. Magdon-Ismail, November 12, 2020 AI/ML for COVID-19: 10 / 11 Tools to Policy →
![Page 33: AI and ML for Predicting COVID-19](https://reader031.vdocuments.site/reader031/viewer/2022012512/618ab28248e8ed65cd6332ad/html5/thumbnails/33.jpg)
Tools to Policy
We have tools to model spread at all scales.
In policy making, all scales are relevant. Decisions should take a holistic view.
The spread of COVID is just one factor that influences these decisions.
. . .
I really enjoyed giving this talk
Creator: M. Magdon-Ismail, November 12, 2020 AI/ML for COVID-19: 11 / 11