Part 5 Percolation approaches to disease spreadtjhladish.github.io/sismid/SISMID_Lecture5.pdf ·...
Transcript of Part 5 Percolation approaches to disease spreadtjhladish.github.io/sismid/SISMID_Lecture5.pdf ·...
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Part 5 — Percolation approaches to diseasespread
Joel C. Miller & Tom Hladish
18–20 July 2018
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SIR and percolation
SIS disease
References
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Percolation
We are going to explore a relationship between SIR disease andpercolation.This will lead to methods to
I predict epidemic probability from a single infection.
I predict final size of an epidemic.
I predict the dynamics of an epidemic.
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Recall SIR behavior
0 1 2 3 4-6
-5
-4
-3
-2
-1
log10 number infected m
log
10
pro
bab
ilit
yx
(m)
log10 number infected m
log
10
pro
bab
ilit
yx
(m)
log10 number infected m
log
10
pro
bab
ilit
yx
(m)
log10 number infected m
log
10
pro
bab
ilit
yx
(m)
log10 number infected m
log
10
pro
bab
ilit
yx
(m)
N = 100
N = 400
N = 1600
N = 6400
N = 25600
0 20 40 60 80 100-4
-3
-2
-1
number infected m
log
10
pro
bab
ilit
yx
(m)
number infected m
log
10
pro
bab
ilit
yx
(m)
number infected m
log
10
pro
bab
ilit
yx
(m)
number infected m
log
10
pro
bab
ilit
yx
(m)
number infected m
log
10
pro
bab
ilit
yx
(m)
N = 100
N = 400
N = 1600
N = 6400
N = 25600
0.0 0.2 0.4 0.6 0.8 1.00
2
4
6
8
10
proportion infected m/N
nor
mal
ized
pro
bab
ilit
yNx
(m)
proportion infected m/N
nor
mal
ized
pro
bab
ilit
yNx
(m)
proportion infected m/N
nor
mal
ized
pro
bab
ilit
yNx
(m)
proportion infected m/N
nor
mal
ized
pro
bab
ilit
yNx
(m)
proportion infected m/N
nor
mal
ized
pro
bab
ilit
yNx
(m)
N = 100
N = 400
N = 1600
N = 6400
N = 25600
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Modified model
We have a network
I An edge represents a potential transmission path (unweighted,bidirectional).
I An infected node remains infected for a single time step.
I An infected node transmits to a neighbor with probability p.
I Warning — no longer assuming continuous time
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Modified model
We have a network
I An edge represents a potential transmission path (unweighted,bidirectional).
I An infected node remains infected for a single time step.
I An infected node transmits to a neighbor with probability p.
I Warning — no longer assuming continuous time
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Modified model
We have a network
I An edge represents a potential transmission path (unweighted,bidirectional).
I An infected node remains infected for a single time step.
I An infected node transmits to a neighbor with probability p.
I Warning — no longer assuming continuous time
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Modified model
We have a network
I An edge represents a potential transmission path (unweighted,bidirectional).
I An infected node remains infected for a single time step.
I An infected node transmits to a neighbor with probability p.
I Warning — no longer assuming continuous time
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Modeling Disease Spread in a network
(1− p)2
p 2
p(1−
p)p(
1−p)(1−
p)2
p2(1− p)
p
(1−p
)
p(1− p)
1
p(1− p)2
(1−p
)p
p3
1
p2(1− p)
1
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Modeling Disease Spread in a network
(1− p)2
p 2
p(1−
p)p(
1−p)(1−
p)2
p2(1− p)
p
(1−p
)
p(1− p)
1
p(1− p)2
(1−p
)p
p3
1
p2(1− p)
1
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Modeling Disease Spread in a network
(1− p)2
p 2
p(1−
p)p(
1−p)(1−
p)2
p2(1− p)
p
(1−p
)
p(1− p)
1
p(1− p)2
(1−p
)p
p3
1
p2(1− p)
1
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Modeling Disease Spread in a network
(1− p)2
p 2
p(1−
p)p(
1−p)(1−
p)2
p2(1− p)
p
(1−p
)
p(1− p)1
p(1− p)2
(1−p
)p
p3
1
p2(1− p)
1
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Modeling Disease Spread in a network
(1− p)2
p 2
p(1−
p)p(
1−p)(1−
p)2
p2(1− p)
p
(1−p
)
p(1− p)1
p(1− p)2
(1−p
)p
p3
1
p2(1− p)
1
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Modeling Disease Spread in a network
(1− p)2
p 2
p(1−
p)p(
1−p)(1−
p)2
p2(1− p)
p
(1−p
)
p(1− p)1
p(1− p)2
(1−p
)p
p3
1
p2(1− p)
1
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Modeling Disease Spread in a network
(1− p)2
p 2
p(1−
p)p(
1−p)(1−
p)2
p2(1− p)
p
(1−p
)
p(1− p)1
p(1− p)2
(1−p
)p
p3
1
p2(1− p)
1
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Alternative perspective
I
(1− p)2
p 2
p(1−
p)p(
1−p)(1−
p)2
p2(1− p)
p
(1−p
)
p(1− p)
1
p(1− p)2
(1−p
)p
p3
1
p2(1− p)
1
At each step, if thereis an edge to cross, itis crossed with probabil-ity p. No edge is evercrossed twice.
I It is equivalent to decide in advance whether the edges will becrossed if encountered.
I
(1− p)3
p(1− p)2
p2(1− p)
p(1− p)2
p2(1− p)
p(1− p)2
p2(1− p)
p3
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Alternative perspective
I
(1− p)2
p 2
p(1−
p)p(
1−p)(1−
p)2
p2(1− p)
p
(1−p
)
p(1− p)
1
p(1− p)2
(1−p
)p
p3
1
p2(1− p)
1
At each step, if thereis an edge to cross, itis crossed with probabil-ity p. No edge is evercrossed twice.
I It is equivalent to decide in advance whether the edges will becrossed if encountered.
I
(1− p)3
p(1− p)2
p2(1− p)
p(1− p)2
p2(1− p)
p(1− p)2
p2(1− p)
p3
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Alternative perspective
I
(1− p)2
p 2
p(1−
p)p(
1−p)(1−
p)2
p2(1− p)
p
(1−p
)
p(1− p)
1
p(1− p)2
(1−p
)p
p3
1
p2(1− p)
1
At each step, if thereis an edge to cross, itis crossed with probabil-ity p. No edge is evercrossed twice.
I It is equivalent to decide in advance whether the edges will becrossed if encountered.
I
(1− p)3
p(1− p)2
p2(1− p)
p(1− p)2
p2(1− p)
p(1− p)2
p2(1− p)
p3
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Alternative perspective
I
(1− p)2
p 2
p(1−
p)p(
1−p)(1−
p)2
p2(1− p)
p
(1−p
)
p(1− p)
1
p(1− p)2
(1−p
)p
p3
1
p2(1− p)
1
At each step, if thereis an edge to cross, itis crossed with probabil-ity p. No edge is evercrossed twice.
I It is equivalent to decide in advance whether the edges will becrossed if encountered.
I
(1− p)3
p(1− p)2
p2(1− p)
p(1− p)2
p2(1− p)
p(1− p)2
p2(1− p)
p3
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Percolation in different size networksComparison of largest (red) and second largest (blue) componentsin different size networks below and above percolation threshold.
I Below threshold largest and second largest in a network areabout the same size as each other and similar size in bothnetworks
I Above threshold largest is proportional to network size.8 / 49
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More detailed comparison of network size
0.0 0.2 0.4 0.6 0.8 1.00.0
0.2
0.4
0.6
0.8
1.0
p
Lar
gest
com
pon
ent
pro
por
tion
N = 100
N = 1000
N = 10000
N = 100000
I Above the threshold, an epidemic occurs if the initial node isin the giant component.
I The entire component containing the index is infected.
I For a large network with given p, the giant component’s sizeis remarkably consistent. So the probability of an epidemicequals the proportion infected.
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0 20 40 60 80 1000.00
0.01
0.02
0.03
0.04
0.05
0.06
Number infected
Pro
bab
ilit
y
N=100
0 5 10 15 200.00
0.05
0.10
0.15
0.20
0.25
0.30
0 50 100 150 200 250 300 350 4000.000
0.002
0.004
0.006
0.008
0.010
0.012
0.014
Number infected
Pro
bab
ilit
y
N=400
0 5 10 15 200.00
0.05
0.10
0.15
0.20
0.25
0.30
0 200 400 600 800 1000 1200 1400 16000.0000
0.0005
0.0010
0.0015
0.0020
0.0025
0.0030
0.0035
Number infected
Pro
bab
ilit
y
N=1600
0 5 10 15 200.00
0.05
0.10
0.15
0.20
0.25
0.30
0 1000 2000 3000 4000 5000 60000.0000
0.0002
0.0004
0.0006
0.0008
Number infected
Pro
bab
ilit
y
N=6400
0 5 10 15 200.00
0.05
0.10
0.15
0.20
0.25
0.30
N P A100 0.237 0.423
400 0.340 0.387
1600 0.339 0.350
6400 0.365 0.366
25600 0.368 0.368
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0 20 40 60 80 1000.00
0.01
0.02
0.03
0.04
0.05
0.06
Number infected
Pro
bab
ilit
y
N=100
0 5 10 15 200.00
0.05
0.10
0.15
0.20
0.25
0.30
0 50 100 150 200 250 300 350 4000.000
0.002
0.004
0.006
0.008
0.010
0.012
0.014
Number infected
Pro
bab
ilit
y
N=400
0 5 10 15 200.00
0.05
0.10
0.15
0.20
0.25
0.30
0 200 400 600 800 1000 1200 1400 16000.0000
0.0005
0.0010
0.0015
0.0020
0.0025
0.0030
0.0035
Number infected
Pro
bab
ilit
y
N=1600
0 5 10 15 200.00
0.05
0.10
0.15
0.20
0.25
0.30
0 1000 2000 3000 4000 5000 60000.0000
0.0002
0.0004
0.0006
0.0008
Number infected
Pro
bab
ilit
y
N=6400
0 5 10 15 200.00
0.05
0.10
0.15
0.20
0.25
0.30
N P A100 0.237 0.423
400 0.340 0.387
1600 0.339 0.350
6400 0.365 0.366
25600 0.368 0.368
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Now return back to transmitting with rate β and recovering withrate γ.
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Transmission probability
I We’ll want to know the probability an infected node vtransmits to its neighbor u (assuming v is infected).
I Transmission is at rate β, and recovery is at rate γ. Theprobability of transmitting before recovering is β/(β + γ).
I Note: v transmitting to u and to w are correlated events(both depend on duration of v ’s infection), but transmissionsfrom different nodes to a single node are independent.
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Transmission probability
I We’ll want to know the probability an infected node vtransmits to its neighbor u (assuming v is infected).
I Transmission is at rate β, and recovery is at rate γ. Theprobability of transmitting before recovering is β/(β + γ).
I Note: v transmitting to u and to w are correlated events(both depend on duration of v ’s infection), but transmissionsfrom different nodes to a single node are independent.
12 / 49
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Transmission probability
I We’ll want to know the probability an infected node vtransmits to its neighbor u (assuming v is infected).
I Transmission is at rate β, and recovery is at rate γ. Theprobability of transmitting before recovering is β/(β + γ).
I Note: v transmitting to u and to w are correlated events(both depend on duration of v ’s infection), but transmissionsfrom different nodes to a single node are independent.
12 / 49
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Directed percolation analogy
Given a network G , I want to simulate the spread of an SIR diseasewith given β and γ
I I use Tom as a random number generator.
I When a node u becomes infected, I ask Tom: “how long willits infection last?”
I Then for each neighbor v I ask “will u transmit to v? When?”
I Tom decides he doesn’t like the rush to generate a randomnumber on the fly. So he does it in advance.
I For each node Tom assigns the duration its infection will last ifinfected.
I Once the duration is chosen, Tom decides which neighbors itwill transmit to and how long it will take.
I Then he reports those to me when I ask.
I Is it possible for me to know whether he is calculating inadvance or not?
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Directed percolation analogy
Given a network G , I want to simulate the spread of an SIR diseasewith given β and γ
I I use Tom as a random number generator.I When a node u becomes infected, I ask Tom: “how long will
its infection last?”I Then for each neighbor v I ask “will u transmit to v? When?”
I Tom decides he doesn’t like the rush to generate a randomnumber on the fly. So he does it in advance.
I For each node Tom assigns the duration its infection will last ifinfected.
I Once the duration is chosen, Tom decides which neighbors itwill transmit to and how long it will take.
I Then he reports those to me when I ask.
I Is it possible for me to know whether he is calculating inadvance or not?
13 / 49
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Directed percolation analogy
Given a network G , I want to simulate the spread of an SIR diseasewith given β and γ
I I use Tom as a random number generator.I When a node u becomes infected, I ask Tom: “how long will
its infection last?”I Then for each neighbor v I ask “will u transmit to v? When?”
I Tom decides he doesn’t like the rush to generate a randomnumber on the fly. So he does it in advance.
I For each node Tom assigns the duration its infection will last ifinfected.
I Once the duration is chosen, Tom decides which neighbors itwill transmit to and how long it will take.
I Then he reports those to me when I ask.
I Is it possible for me to know whether he is calculating inadvance or not?
13 / 49
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Directed percolation analogy
Given a network G , I want to simulate the spread of an SIR diseasewith given β and γ
I I use Tom as a random number generator.I When a node u becomes infected, I ask Tom: “how long will
its infection last?”I Then for each neighbor v I ask “will u transmit to v? When?”
I Tom decides he doesn’t like the rush to generate a randomnumber on the fly. So he does it in advance.
I For each node Tom assigns the duration its infection will last ifinfected.
I Once the duration is chosen, Tom decides which neighbors itwill transmit to and how long it will take.
I Then he reports those to me when I ask.
I Is it possible for me to know whether he is calculating inadvance or not?
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Directed percolation analogy
Given a network G , I want to simulate the spread of an SIR diseasewith given β and γ
I I use Tom as a random number generator.I When a node u becomes infected, I ask Tom: “how long will
its infection last?”I Then for each neighbor v I ask “will u transmit to v? When?”
I Tom decides he doesn’t like the rush to generate a randomnumber on the fly. So he does it in advance.
I For each node Tom assigns the duration its infection will last ifinfected.
I Once the duration is chosen, Tom decides which neighbors itwill transmit to and how long it will take.
I Then he reports those to me when I ask.
I Is it possible for me to know whether he is calculating inadvance or not?
13 / 49
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a b
c d
a0.084
b0.006
c4.237
d0.653
0.0450.38
8
0.19
7
0.186
0.07
8
0.144
Every number that Tom gives me is a random number that isgenerated independently of every other number. It doesn’t matterwhen he generates it.
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a b
c d
a0.084
b0.006
c4.237
d0.653
0.0450.38
8
0.19
7
0.186
0.07
8
0.144
Every number that Tom gives me is a random number that isgenerated independently of every other number. It doesn’t matterwhen he generates it.
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Typical structure
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Directed Percolation Equivalence
The following processes produce indistinguishable output:I Standard epidemic simulation:
I Take a network G .I Choose an initial infected individual.I Allow edges to transmit until the random time the individual
recovers.
I Percolation-based simulation:
I Take a network G .I Generate a new directed network H:
I For each individual u, assign a duration d of infection.I For each edge from u, determine delay t until transmitting.I If t < d , place directed edge into network with associated
time.
I Choose an initial infected individual.I Trace the disease spread following edges in H, transmitting
after the given time.
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Directed Percolation Equivalence
The following processes produce indistinguishable output:I Standard epidemic simulation:
I Take a network G .I Choose an initial infected individual.I Allow edges to transmit until the random time the individual
recovers.
I Percolation-based simulation:
I Take a network G .I Generate a new directed network H:
I For each individual u, assign a duration d of infection.I For each edge from u, determine delay t until transmitting.I If t < d , place directed edge into network with associated
time.
I Choose an initial infected individual.I Trace the disease spread following edges in H, transmitting
after the given time.
16 / 49
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Directed Percolation Equivalence
The following processes produce indistinguishable output:I Standard epidemic simulation:
I Take a network G .I Choose an initial infected individual.I Allow edges to transmit until the random time the individual
recovers.
I Percolation-based simulation:
I Take a network G .I Generate a new directed network H:
I For each individual u, assign a duration d of infection.I For each edge from u, determine delay t until transmitting.I If t < d , place directed edge into network with associated
time.
I Choose an initial infected individual.I Trace the disease spread following edges in H, transmitting
after the given time.
16 / 49
![Page 39: Part 5 Percolation approaches to disease spreadtjhladish.github.io/sismid/SISMID_Lecture5.pdf · Percolation We are going to explore a relationship between SIR disease and percolation.](https://reader036.fdocuments.in/reader036/viewer/2022062605/5fd26bec6ae80e5b09540034/html5/thumbnails/39.jpg)
Directed Percolation Equivalence
The following processes produce indistinguishable output:I Standard epidemic simulation:
I Take a network G .I Choose an initial infected individual.I Allow edges to transmit until the random time the individual
recovers.
I Percolation-based simulation:I Take a network G .
I Generate a new directed network H:
I For each individual u, assign a duration d of infection.I For each edge from u, determine delay t until transmitting.I If t < d , place directed edge into network with associated
time.
I Choose an initial infected individual.I Trace the disease spread following edges in H, transmitting
after the given time.
16 / 49
![Page 40: Part 5 Percolation approaches to disease spreadtjhladish.github.io/sismid/SISMID_Lecture5.pdf · Percolation We are going to explore a relationship between SIR disease and percolation.](https://reader036.fdocuments.in/reader036/viewer/2022062605/5fd26bec6ae80e5b09540034/html5/thumbnails/40.jpg)
Directed Percolation Equivalence
The following processes produce indistinguishable output:I Standard epidemic simulation:
I Take a network G .I Choose an initial infected individual.I Allow edges to transmit until the random time the individual
recovers.
I Percolation-based simulation:I Take a network G .I Generate a new directed network H:
I For each individual u, assign a duration d of infection.I For each edge from u, determine delay t until transmitting.I If t < d , place directed edge into network with associated
time.
I Choose an initial infected individual.I Trace the disease spread following edges in H, transmitting
after the given time.
16 / 49
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Directed Percolation Equivalence
The following processes produce indistinguishable output:I Standard epidemic simulation:
I Take a network G .I Choose an initial infected individual.I Allow edges to transmit until the random time the individual
recovers.
I Percolation-based simulation:I Take a network G .I Generate a new directed network H:
I For each individual u, assign a duration d of infection.I For each edge from u, determine delay t until transmitting.I If t < d , place directed edge into network with associated
time.
I Choose an initial infected individual.I Trace the disease spread following edges in H, transmitting
after the given time.
16 / 49
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Directed Percolation Equivalence
The following processes produce indistinguishable output:I Standard epidemic simulation:
I Take a network G .I Choose an initial infected individual.I Allow edges to transmit until the random time the individual
recovers.
I Percolation-based simulation:I Take a network G .I Generate a new directed network H:
I For each individual u, assign a duration d of infection.I For each edge from u, determine delay t until transmitting.I If t < d , place directed edge into network with associated
time.
I Choose an initial infected individual.I Trace the disease spread following edges in H, transmitting
after the given time.
16 / 49
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Comments on directed percolation
I Directed percolation can be used more generally when thereare other sources of heterogeneity in infectiousness and/orsusceptibility.
I The eventually infected nodes are exactly those nodes in theout-component of the index case.
I The probability a random node is infected follows from thesize of its in-component.
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Typical structure
I We can understand the dynamics with a “bowtie” diagram.
I Above a threshold there is a Giant Strongly ConnectedComponent HSCC
I It has an in-component HIN and an out-component HOUT .I If the index case is in HIN or HSCC then all of HSCC and
HOUT are eventually infected.I So Epidemic Probability P = E(|HIN ∪ HSCC |)/N and Attack
rate A = E(|HSCC ∪ HOUT |)/N.
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Typical structure
I We can understand the dynamics with a “bowtie” diagram.I Above a threshold there is a Giant Strongly Connected
Component HSCC
I It has an in-component HIN and an out-component HOUT .I If the index case is in HIN or HSCC then all of HSCC and
HOUT are eventually infected.I So Epidemic Probability P = E(|HIN ∪ HSCC |)/N and Attack
rate A = E(|HSCC ∪ HOUT |)/N.
18 / 49
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Typical structure
I We can understand the dynamics with a “bowtie” diagram.I Above a threshold there is a Giant Strongly Connected
Component HSCC
I It has an in-component HIN and an out-component HOUT .
I If the index case is in HIN or HSCC then all of HSCC andHOUT are eventually infected.
I So Epidemic Probability P = E(|HIN ∪ HSCC |)/N and Attackrate A = E(|HSCC ∪ HOUT |)/N.
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Typical structure
I We can understand the dynamics with a “bowtie” diagram.I Above a threshold there is a Giant Strongly Connected
Component HSCC
I It has an in-component HIN and an out-component HOUT .I If the index case is in HIN or HSCC then all of HSCC and
HOUT are eventually infected.
I So Epidemic Probability P = E(|HIN ∪ HSCC |)/N and Attackrate A = E(|HSCC ∪ HOUT |)/N.
18 / 49
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Typical structure
I We can understand the dynamics with a “bowtie” diagram.I Above a threshold there is a Giant Strongly Connected
Component HSCC
I It has an in-component HIN and an out-component HOUT .I If the index case is in HIN or HSCC then all of HSCC and
HOUT are eventually infected.I So Epidemic Probability P = E(|HIN ∪ HSCC |)/N and Attack
rate A = E(|HSCC ∪ HOUT |)/N.18 / 49
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Some consequences
The dynamic process of the epidemic is now encoded in a staticnetwork H. Studying H gives us some insight into what ishappening.
I There is a symmetry between epidemic probability andepidemic final size.
I Because edges out of a node are correlated and edges in to anode are not, P 6= A.
I The probability of an epidemic is the proportion of nodes fromwhich there is a long chain of transmissions in H.
I The final size of an epidemic with a very small initialproportion infected is the proportion of nodes which are thetarget of a long chain of transmissions in H.
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Some consequences
The dynamic process of the epidemic is now encoded in a staticnetwork H. Studying H gives us some insight into what ishappening.
I There is a symmetry between epidemic probability andepidemic final size.
I Because edges out of a node are correlated and edges in to anode are not, P 6= A.
I The probability of an epidemic is the proportion of nodes fromwhich there is a long chain of transmissions in H.
I The final size of an epidemic with a very small initialproportion infected is the proportion of nodes which are thetarget of a long chain of transmissions in H.
19 / 49
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Some consequences
The dynamic process of the epidemic is now encoded in a staticnetwork H. Studying H gives us some insight into what ishappening.
I There is a symmetry between epidemic probability andepidemic final size.
I Because edges out of a node are correlated and edges in to anode are not, P 6= A.
I The probability of an epidemic is the proportion of nodes fromwhich there is a long chain of transmissions in H.
I The final size of an epidemic with a very small initialproportion infected is the proportion of nodes which are thetarget of a long chain of transmissions in H.
19 / 49
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Some consequences
The dynamic process of the epidemic is now encoded in a staticnetwork H. Studying H gives us some insight into what ishappening.
I There is a symmetry between epidemic probability andepidemic final size.
I Because edges out of a node are correlated and edges in to anode are not, P 6= A.
I The probability of an epidemic is the proportion of nodes fromwhich there is a long chain of transmissions in H.
I The final size of an epidemic with a very small initialproportion infected is the proportion of nodes which are thetarget of a long chain of transmissions in H.
19 / 49
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Some consequences
The dynamic process of the epidemic is now encoded in a staticnetwork H. Studying H gives us some insight into what ishappening.
I There is a symmetry between epidemic probability andepidemic final size.
I Because edges out of a node are correlated and edges in to anode are not, P 6= A.
I The probability of an epidemic is the proportion of nodes fromwhich there is a long chain of transmissions in H.
I The final size of an epidemic with a very small initialproportion infected is the proportion of nodes which are thetarget of a long chain of transmissions in H.
19 / 49
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SIR epidemics in Configuration Model networks
I Consider a Configuration Model network in which we infect a(probably small) fraction of the population ρ.
I Allow the SIR disease to spread.
I We assume ρN is large enough that stochastic die-out doesnot play a major role.
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Recall our key questions
For SIR:
I P, the probability of an epidemic.
I A, the “attack rate”: the fraction infected if an epidemichappens (better named the attack ratio).
I R0, the average number of infections caused by those infectedearly in the epidemic.
I I (t), the time course of the epidemic.
For SIS:
I PI I (∞), the equilibrium level of infection
I R0
I I (t)
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R0 calculationFor SIR disease:
I The probability a newly infected individual has degree k isPn(k).
I The expected number of infections it causes given k is(k − 1) β
β+γ [it cannot reinfect the source of its infection].I So
R0 = E(number infections caused|infected early)
=∑k
P(k |infected early)E(number infections|k)
=∑k
Pn(k)(k − 1)β
β + γ
=β
β + γ
∑k
kP(k)(k − 1)
〈K 〉
=β
β + γ
⟨K 2 − K
⟩〈K 〉
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R0 calculationFor SIR disease:
I The probability a newly infected individual has degree k isPn(k).
I The expected number of infections it causes given k is(k − 1) β
β+γ [it cannot reinfect the source of its infection].I So
R0 = E(number infections caused|infected early)
=∑k
P(k |infected early)E(number infections|k)
=∑k
Pn(k)(k − 1)β
β + γ
=β
β + γ
∑k
kP(k)(k − 1)
〈K 〉
=β
β + γ
⟨K 2 − K
⟩〈K 〉
22 / 49
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R0 calculationFor SIR disease:
I The probability a newly infected individual has degree k isPn(k).
I The expected number of infections it causes given k is(k − 1) β
β+γ [it cannot reinfect the source of its infection].I So
R0 = E(number infections caused|infected early)
=∑k
P(k |infected early)E(number infections|k)
=∑k
Pn(k)(k − 1)β
β + γ
=β
β + γ
∑k
kP(k)(k − 1)
〈K 〉
=β
β + γ
⟨K 2 − K
⟩〈K 〉
22 / 49
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R0 calculationFor SIR disease:
I The probability a newly infected individual has degree k isPn(k).
I The expected number of infections it causes given k is(k − 1) β
β+γ [it cannot reinfect the source of its infection].I So
R0 = E(number infections caused|infected early)
=∑k
P(k |infected early)E(number infections|k)
=∑k
Pn(k)(k − 1)β
β + γ
=β
β + γ
∑k
kP(k)(k − 1)
〈K 〉
=β
β + γ
⟨K 2 − K
⟩〈K 〉
22 / 49
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R0 calculationFor SIR disease:
I The probability a newly infected individual has degree k isPn(k).
I The expected number of infections it causes given k is(k − 1) β
β+γ [it cannot reinfect the source of its infection].I So
R0 = E(number infections caused|infected early)
=∑k
P(k |infected early)E(number infections|k)
=∑k
Pn(k)(k − 1)β
β + γ
=β
β + γ
∑k
kP(k)(k − 1)
〈K 〉
=β
β + γ
⟨K 2 − K
⟩〈K 〉
22 / 49
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R0 calculationFor SIR disease:
I The probability a newly infected individual has degree k isPn(k).
I The expected number of infections it causes given k is(k − 1) β
β+γ [it cannot reinfect the source of its infection].I So
R0 = E(number infections caused|infected early)
=∑k
P(k |infected early)E(number infections|k)
=∑k
Pn(k)(k − 1)β
β + γ
=β
β + γ
∑k
kP(k)(k − 1)
〈K 〉
=β
β + γ
⟨K 2 − K
⟩〈K 〉
22 / 49
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R0 calculationFor SIR disease:
I The probability a newly infected individual has degree k isPn(k).
I The expected number of infections it causes given k is(k − 1) β
β+γ [it cannot reinfect the source of its infection].I So
R0 = E(number infections caused|infected early)
=∑k
P(k |infected early)E(number infections|k)
=∑k
Pn(k)(k − 1)β
β + γ
=β
β + γ
∑k
kP(k)(k − 1)
〈K 〉
=β
β + γ
⟨K 2 − K
⟩〈K 〉
22 / 49
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Recall our key questions
For SIR:
I P, the probability of an epidemic.
I A, the “attack rate”: the fraction infected if an epidemichappens (better named the attack ratio).
I R0, the average number of infections caused by those infectedearly in the epidemic.
I I (t), the time course of the epidemic.
For SIS:
I PI I (∞), the equilibrium level of infection
I R0
I I (t)
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Changing the final size question
Instead of asking what proportion end up susceptible or recoveredask:
What is the probability a random node does not have atransmission path to it from one of the index nodes?
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Changing the final size question
Instead of asking what proportion end up susceptible or recoveredask:
What is the probability a random node does not have atransmission path to it from one of the index nodes?
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u
ΘΘ Θ
ΘΘ
Θ = P(v did not transmit to u)
Probability a random degree k test individual is susceptible at theend is
S
N=∑k
P(k)
(1− ρ)Θk
= (1− ρ)ψ(Θ)
whereψ(x) =
∑k
P(k)xk
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u
ΘΘ Θ
ΘΘ
Θ = P(v did not transmit to u)
Probability a random degree k test individual is susceptible at theend is
S
N=∑k
P(k)
(1− ρ)Θk
= (1− ρ)ψ(Θ)
whereψ(x) =
∑k
P(k)xk
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u
ΘΘ Θ
ΘΘ
Θ = P(v did not transmit to u)
Probability a random degree k test individual is susceptible at theend is
S
N=∑k
P(k)(1− ρ)Θk
= (1− ρ)ψ(Θ)
whereψ(x) =
∑k
P(k)xk
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u
ΘΘ Θ
ΘΘ
Θ = P(v did not transmit to u)
Probability a random degree k test individual is susceptible at theend is
S
N=∑k
P(k)(1− ρ)Θk = (1− ρ)ψ(Θ)
whereψ(x) =
∑k
P(k)xk
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Finding Θ
v
u
ΘΘ Θ
ΘΘ
ΘΘ
Probability a random degree k partner never infected is
φS =∑k
(1− ρ)Θk−1
= (1− ρ)ψ′(Θ)
〈K 〉
Given β and γ, partner does not transmit to u with probability
Θ = φS +
(1− β
β + γ
)(1−φS)
= 1− β
β + γ+
β
β + γ
(1− ρ)ψ′(Θ)
〈K 〉
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Finding Θ
v
u
ΘΘ Θ
ΘΘ
ΘΘ
Probability a random degree k partner never infected is
φS =∑k
Pn(k)(1− ρ)Θk−1
= (1− ρ)ψ′(Θ)
〈K 〉
Given β and γ, partner does not transmit to u with probability
Θ = φS +
(1− β
β + γ
)(1−φS)
= 1− β
β + γ+
β
β + γ
(1− ρ)ψ′(Θ)
〈K 〉
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Finding Θ
v
u
ΘΘ Θ
ΘΘ
ΘΘ
Probability a random degree k partner never infected is
φS =∑k
kP(k)
〈K 〉(1− ρ)Θk−1
= (1− ρ)ψ′(Θ)
〈K 〉
Given β and γ, partner does not transmit to u with probability
Θ = φS +
(1− β
β + γ
)(1−φS)
= 1− β
β + γ+
β
β + γ
(1− ρ)ψ′(Θ)
〈K 〉
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Finding Θ
v
u
ΘΘ Θ
ΘΘ
ΘΘ
Probability a random degree k partner never infected is
φS =∑k
kP(k)
〈K 〉(1− ρ)Θk−1 = (1− ρ)
ψ′(Θ)
〈K 〉
Given β and γ, partner does not transmit to u with probability
Θ = φS +
(1− β
β + γ
)(1−φS)
= 1− β
β + γ+
β
β + γ
(1− ρ)ψ′(Θ)
〈K 〉
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Finding Θ
v
u
ΘΘ Θ
ΘΘ
ΘΘ
Probability a random degree k partner never infected is
φS =∑k
kP(k)
〈K 〉(1− ρ)Θk−1 = (1− ρ)
ψ′(Θ)
〈K 〉
Given β and γ, partner does not transmit to u with probability
Θ = φS +
(1− β
β + γ
)(1−φS)
= 1− β
β + γ+
β
β + γ
(1− ρ)ψ′(Θ)
〈K 〉
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Finding Θ
v
u
ΘΘ Θ
ΘΘ
ΘΘ
Probability a random degree k partner never infected is
φS =∑k
kP(k)
〈K 〉(1− ρ)Θk−1 = (1− ρ)
ψ′(Θ)
〈K 〉
Given β and γ, partner does not transmit to u with probability
Θ = φS +
(1− β
β + γ
)(1−φS) = 1− β
β + γ+
β
β + γ
(1− ρ)ψ′(Θ)
〈K 〉
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Final Size
SoA = 1− (1− ρ)ψ(Θ)
where
Θ =γ
β + γ+
β
β + γ(1− ρ)
ψ′(Θ)
〈K 〉
A more rigorous definition would be that Θ is the probability that the given edge isn’t the final edge of a directedpath from an index node to u in the percolated network H.
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Final Size
SoA = 1− (1− ρ)ψ(Θ)
where
Θ =γ
β + γ+
β
β + γ(1− ρ)
ψ′(Θ)
〈K 〉A more rigorous definition would be that Θ is the probability that the given edge isn’t the final edge of a directedpath from an index node to u in the percolated network H.
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Recall our key questions
For SIR:
I P, the probability of an epidemic.
I A, the “attack rate”: the fraction infected if an epidemichappens (better named the attack ratio).
I R0, the average number of infections caused by those infectedearly in the epidemic.
I I (t), the time course of the epidemic.
For SIS:
I PI I (∞), the equilibrium level of infection
I R0
I I (t)
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Finding S(t) for SIR disease
u
θθ θ
θθ
θ(t) = P(v not yet transmitted to u)
Probability a random test individual still susceptible is
S(t)
N=∑k
P(k)
(1− ρ)θ(t)k
= (1− ρ)ψ(θ(t))
whereψ(x) =
∑k
P(k)xk
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Finding S(t) for SIR disease
u
θθ θ
θθ
θ(t) = P(v not yet transmitted to u)
Probability a random degree k test individual still susceptible is
S(t)
N=∑k
P(k)
(1− ρ)θ(t)k
= (1− ρ)ψ(θ(t))
whereψ(x) =
∑k
P(k)xk
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Finding S(t) for SIR disease
u
θθ θ
θθ
θ(t) = P(v not yet transmitted to u)
Probability a random degree k test individual still susceptible is
S(t)
N=∑k
P(k)(1− ρ)θ(t)k
= (1− ρ)ψ(θ(t))
whereψ(x) =
∑k
P(k)xk
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Finding S(t) for SIR disease
u
θθ θ
θθ
θ(t) = P(v not yet transmitted to u)
Probability a random degree k test individual still susceptible is
S(t)
N=∑k
P(k)(1− ρ)θ(t)k = (1− ρ)ψ(θ(t))
whereψ(x) =
∑k
P(k)xk
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How does θ evolve?
v
u
θ
θ
v
u
φS v
u
φI v
u
φR
v
u
1 − θ
I θ = φS + φI + φR .
I θ = −βφI .I Our goal is to find φI in terms of θ.
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How does θ evolve?
θ
v
u
φS v
u
φI v
u
φR
v
u
1 − θ
I θ = φS + φI + φR .
I θ = −βφI .I Our goal is to find φI in terms of θ.
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How does θ evolve?
θ
v
u
φS v
u
φI v
u
φR
v
u
1 − θ
βφI
γφI
I θ = φS + φI + φR .
I θ = −βφI .
I Our goal is to find φI in terms of θ.
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How does θ evolve?
θ
v
u
φS v
u
φI v
u
φR
v
u
1 − θ
βφI
γφI
I θ = φS + φI + φR .
I θ = −βφI .I Our goal is to find φI in terms of θ.
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Finding φR(t)
θ
v
u
φS v
u
φI v
u
φR
v
u
1 − θ
βφI
γφI
Because derivatives are proportional, φR = γβ (1− θ)
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Finding φS(t)
v
u
θθ θ
θθ
θθ
Probability a random degree k partner still susceptible is
φS(t) =∑k
(1− ρ)θ(t)k−1
= (1− ρ)ψ′(θ)
〈K 〉
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Finding φS(t)
v
u
θθ θ
θθ
θθ
Probability a random degree k partner still susceptible is
φS(t) =∑k
Pn(k)(1− ρ)θ(t)k−1
= (1− ρ)ψ′(θ)
〈K 〉
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Finding φS(t)
v
u
θθ θ
θθ
θθ
Probability a random degree k partner still susceptible is
φS(t) =∑k
kP(k)
〈K 〉(1− ρ)θ(t)k−1
= (1− ρ)ψ′(θ)
〈K 〉
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Finding φS(t)
v
u
θθ θ
θθ
θθ
Probability a random degree k partner still susceptible is
φS(t) =∑k
kP(k)
〈K 〉(1− ρ)θ(t)k−1 = (1− ρ)
ψ′(θ)
〈K 〉
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θ
v
u
(1 − ρ)ψ′(θ)〈K〉 v
u
φI v
u
γβ(1 − θ)
v
u
1 − θ
βφI
γφI
We have
φI = θ − φS − φR
= θ − (1− ρ)ψ′(θ)
〈K 〉− γ
β(1− θ)
θ = −βφI
= −βθ + β(1− ρ)ψ′(θ)
〈K 〉+ γ(1− θ)
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θ
v
u
(1 − ρ)ψ′(θ)〈K〉 v
u
φI v
u
γβ(1 − θ)
v
u
1 − θ
βφI
γφI
We have
φI = θ − φS − φR = θ − (1− ρ)ψ′(θ)
〈K 〉− γ
β(1− θ)
θ = −βφI
= −βθ + β(1− ρ)ψ′(θ)
〈K 〉+ γ(1− θ)
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θ
v
u
(1 − ρ)ψ′(θ)〈K〉 v
u
φI v
u
γβ(1 − θ)
v
u
1 − θ
βφI
γφI
We have
φI = θ − φS − φR = θ − (1− ρ)ψ′(θ)
〈K 〉− γ
β(1− θ)
θ = −βφI = −βθ + β(1− ρ)ψ′(θ)
〈K 〉+ γ(1− θ)
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Final System
We finally have
θ = −βθ + β(1− ρ)ψ′(θ)
〈K 〉+ γ(1− θ)
R = γI S = (1− ρ)Nψ(θ) I = N − S − R
Compare with
θ = −βθ + βθ2 (1− ρ)ψ′(θ)
〈K〉− θγ ln θ
R = γI , S = (1− ρ)Nψ(θ), I = N − S − R
More details in [1, 2, 3]
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0 5 10 15 20
t
0.00
0.05
0.10
0.15
0.20
Pro
port
ion infe
cted
Truncated Power Law
Bimodal
Poisson
Homogeneous
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A good exercise
Repeat this derivation for a model in which infections last for onetime step and transmission occurs with probability p.
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Epidemic probability
I To calculate epidemic probability, we consider a singleintroduced node, randomly chosen in the population.
I ρ = 0.
I ψ(x) =∑
k P(k)xk is the probability generating function forthe degree distribution.
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Epidemic probability
I To calculate epidemic probability, we consider a singleintroduced node, randomly chosen in the population.
I ρ = 0.
I ψ(x) =∑
k P(k)xk is the probability generating function forthe degree distribution.
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Epidemic probability
I To calculate epidemic probability, we consider a singleintroduced node, randomly chosen in the population.
I ρ = 0.
I ψ(x) =∑
k P(k)xk is the probability generating function forthe degree distribution.
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Calculating epidemic probability
u
Ω(D)Ω(D)Ω(D)
Ω(D)
Ω(D)
Ω(D) = P(u does not transmit to a neighbor|D) + P(u transmits, but neighbor doesn’t lead to an epidemic)
Probability a random degree k index case whose infection durationis D does not start an epidemic is
1−P =
∫ ∞0
γe−γD∑k
P(k)
Ω(D)k
dD =
∫ ∞0
γe−γDψ(Ω(D))dD
whereψ(x) =
∑k
P(k)xk
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Calculating epidemic probability
u
Ω(D)Ω(D)Ω(D)
Ω(D)
Ω(D)
Ω(D) = P(u does not transmit to a neighbor|D) + P(u transmits, but neighbor doesn’t lead to an epidemic)
Probability a random degree k index case whose infection durationis D does not start an epidemic is
1−P =
∫ ∞0
γe−γD∑k
P(k)
Ω(D)k
dD =
∫ ∞0
γe−γDψ(Ω(D))dD
whereψ(x) =
∑k
P(k)xk
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Calculating epidemic probability
u
Ω(D)Ω(D)Ω(D)
Ω(D)
Ω(D)
Ω(D) = P(u does not transmit to a neighbor|D) + P(u transmits, but neighbor doesn’t lead to an epidemic)
Probability a random degree k index case whose infection durationis D does not start an epidemic is
1−P =
∫ ∞0
γe−γD
∑k
P(k)Ω(D)k
dD =
∫ ∞0
γe−γDψ(Ω(D))dD
whereψ(x) =
∑k
P(k)xk
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Calculating epidemic probability
u
Ω(D)Ω(D)Ω(D)
Ω(D)
Ω(D)
Ω(D) = P(u does not transmit to a neighbor|D) + P(u transmits, but neighbor doesn’t lead to an epidemic)
Probability a random degree k index case whose infection durationis D does not start an epidemic is
1−P =
∫ ∞0
γe−γD∑k
P(k)Ω(D)k dD
=
∫ ∞0
γe−γDψ(Ω(D))dD
whereψ(x) =
∑k
P(k)xk
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Calculating epidemic probability
u
Ω(D)Ω(D)Ω(D)
Ω(D)
Ω(D)
Ω(D) = P(u does not transmit to a neighbor|D) + P(u transmits, but neighbor doesn’t lead to an epidemic)
Probability a random degree k index case whose infection durationis D does not start an epidemic is
1−P =
∫ ∞0
γe−γD∑k
P(k)Ω(D)k dD =
∫ ∞0
γe−γDψ(Ω(D))dD
whereψ(x) =
∑k
P(k)xk
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Finding Ω
v
u
Ω(D)Ω(D)Ω(D)
Ω(D)Ω(D)
Ω(D)
Ω(D)
p(D)
Probability a random partner of the index case having degree kwhose infection duration is D does not start an epidemic is
Ω(D) =
[1− p(D)] +
∫ ∞0
γe−γD∑k
Pn(k)
p(D)Ω(D)k−1
dD
p(D) = 1− e−βD is the probability of transmitting given infection duration of D
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Finding Ω
v
u
Ω(D)Ω(D)Ω(D)
Ω(D)Ω(D)
Ω(D)
Ω(D)
p(D)
Probability a random partner of the index case having degree kwhose infection duration is D does not start an epidemic is
Ω(D) =
[1− p(D)] +
∫ ∞0
γe−γD∑k
Pn(k)
p(D)Ω(D)k−1
dD
p(D) = 1− e−βD is the probability of transmitting given infection duration of D
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Finding Ω
v
u
Ω(D)Ω(D)Ω(D)
Ω(D)Ω(D)
Ω(D)
Ω(D)
p(D)
Probability a random partner of the index case having degree kwhose infection duration is D does not start an epidemic is
Ω(D) =
[1− p(D)] +
∫ ∞0
γe−γD
∑k
Pn(k)p(D)Ω(D)k−1
dD
p(D) = 1− e−βD is the probability of transmitting given infection duration of D
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Finding Ω
v
u
Ω(D)Ω(D)Ω(D)
Ω(D)Ω(D)
Ω(D)
Ω(D)
p(D)
Probability a random partner of the index case having degree kwhose infection duration is D does not start an epidemic is
Ω(D) = [1− p(D)] +
∫ ∞0
γe−γD∑k
Pn(k)p(D)Ω(D)k−1 dD
p(D) = 1− e−βD is the probability of transmitting given infection duration of D
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Finding Ω
v
u
Ω(D)Ω(D)Ω(D)
Ω(D)Ω(D)
Ω(D)
Ω(D)
p(D)
Probability a random partner of the index case having degree kwhose infection duration is D does not start an epidemic is
Ω(D) = [1− p(D)] + p(D)
∫ ∞0
γe−γD∑k
Pn(k)Ω(D)k−1 dD
p(D) = 1− e−βD is the probability of transmitting given infection duration of D
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Finding Ω
v
u
Ω(D)Ω(D)Ω(D)
Ω(D)Ω(D)
Ω(D)
Ω(D)
p(D)
Probability a random partner of the index case having degree kwhose infection duration is D does not start an epidemic is
Ω(D) = [1− p(D)] + p(D)
∫ ∞0
γe−γD∑k
kP(k)
〈K 〉Ω(D)k−1 dD
p(D) = 1− e−βD is the probability of transmitting given infection duration of D
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Finding Ω
v
u
Ω(D)Ω(D)Ω(D)
Ω(D)Ω(D)
Ω(D)
Ω(D)
p(D)
Probability a random partner of the index case having degree kwhose infection duration is D does not start an epidemic is
Ω(D) = [1− p(D)] + p(D)
∫ ∞0
γe−γD∑
k kP(k)Ω(D)k−1
〈K 〉dD
p(D) = 1− e−βD is the probability of transmitting given infection duration of D
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Finding Ω
v
u
Ω(D)Ω(D)Ω(D)
Ω(D)Ω(D)
Ω(D)
Ω(D)
p(D)
Probability a random partner of the index case having degree kwhose infection duration is D does not start an epidemic is
Ω(D) = [1− p(D)] + p(D)
∫ ∞0
γe−γDψ′(Ω(D))
〈K 〉dD
p(D) = 1− e−βD is the probability of transmitting given infection duration of D
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Calculating epidemic probability
We arrive at
1− P =
∫ ∞0
γe−γDψ(Ω(D))dD
Ω(D) = e−βD +(
1− e−βD)∫ ∞
0γe−γD
ψ′(Ω(D))
〈K 〉dD
In general we can only solve this numerically, but it isstraightforward. We start with a guess that Ω(D) = 1, plug it inand iterate.In fact the nth iteration will give the probability that the diseasespreads at least n generations.
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SIR and percolation
SIS disease
References
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SIS disease
It is very difficult to write down an analytic model of SIS disease innetworks that accounts for partnership duration.
I The difficulty results from the fact that a node can infect itsneighbors, thus changing the exposure it receives after itrecovers.
I This effect is real: a person who has recovered from, say,MRSA but passed it on to his/her family is at higher risk ofreaquiring MRSA. Ignoring this weakens our ability to drawconclusions.
I This has policy implications: how much will it reduce MRSAtransmission if we clear the disease from a hospital or a prison?
I So for SIS disease simulation is likely to play a major role.
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SIS disease
It is very difficult to write down an analytic model of SIS disease innetworks that accounts for partnership duration.
I The difficulty results from the fact that a node can infect itsneighbors, thus changing the exposure it receives after itrecovers.
I This effect is real: a person who has recovered from, say,MRSA but passed it on to his/her family is at higher risk ofreaquiring MRSA. Ignoring this weakens our ability to drawconclusions.
I This has policy implications: how much will it reduce MRSAtransmission if we clear the disease from a hospital or a prison?
I So for SIS disease simulation is likely to play a major role.
42 / 49
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SIS disease
It is very difficult to write down an analytic model of SIS disease innetworks that accounts for partnership duration.
I The difficulty results from the fact that a node can infect itsneighbors, thus changing the exposure it receives after itrecovers.
I This effect is real: a person who has recovered from, say,MRSA but passed it on to his/her family is at higher risk ofreaquiring MRSA. Ignoring this weakens our ability to drawconclusions.
I This has policy implications: how much will it reduce MRSAtransmission if we clear the disease from a hospital or a prison?
I So for SIS disease simulation is likely to play a major role.
42 / 49
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SIS disease
It is very difficult to write down an analytic model of SIS disease innetworks that accounts for partnership duration.
I The difficulty results from the fact that a node can infect itsneighbors, thus changing the exposure it receives after itrecovers.
I This effect is real: a person who has recovered from, say,MRSA but passed it on to his/her family is at higher risk ofreaquiring MRSA. Ignoring this weakens our ability to drawconclusions.
I This has policy implications: how much will it reduce MRSAtransmission if we clear the disease from a hospital or a prison?
I So for SIS disease simulation is likely to play a major role.
42 / 49
![Page 120: Part 5 Percolation approaches to disease spreadtjhladish.github.io/sismid/SISMID_Lecture5.pdf · Percolation We are going to explore a relationship between SIR disease and percolation.](https://reader036.fdocuments.in/reader036/viewer/2022062605/5fd26bec6ae80e5b09540034/html5/thumbnails/120.jpg)
SIS disease
It is very difficult to write down an analytic model of SIS disease innetworks that accounts for partnership duration.
I The difficulty results from the fact that a node can infect itsneighbors, thus changing the exposure it receives after itrecovers.
I This effect is real: a person who has recovered from, say,MRSA but passed it on to his/her family is at higher risk ofreaquiring MRSA. Ignoring this weakens our ability to drawconclusions.
I This has policy implications: how much will it reduce MRSAtransmission if we clear the disease from a hospital or a prison?
I So for SIS disease simulation is likely to play a major role.
42 / 49
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Percolation-like results and SIS
I It is possible to use percolation-like results to for rigorousconclusions about SIS disease.
I As a general rule, these rigorous results do not generalize if wedo not assume constant infection and transmission rates.
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Percolation-like results and SIS
I It is possible to use percolation-like results to for rigorousconclusions about SIS disease.
I As a general rule, these rigorous results do not generalize if wedo not assume constant infection and transmission rates.
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A percolation-like approach
t
BobbieAlex Charlie
I Find transmission events as Poisson process
I Find recovery events as Poisson process
I Trace out from initial infection
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A percolation-like approach
t
BobbieAlex Charlie
I Find transmission events as Poisson process
I Find recovery events as Poisson process
I Trace out from initial infection
44 / 49
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A percolation-like approach
t
BobbieAlex Charlie
I Find transmission events as Poisson process
I Find recovery events as Poisson process
I Trace out from initial infection
44 / 49
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A percolation-like approach
t
BobbieAlex Charlie
I Find transmission events as Poisson process
I Find recovery events as Poisson process
I Trace out from initial infection
44 / 49
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Now invert the picture
t
BobbieAlex Charlie
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Now invert the picture
t
BobbieAlex Charlie
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Now invert the picture
t
BobbieAlex Charlie
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Some conclusions
I An infection of u at time 0 leads to an infection of v at time tiff there is a path that doesn’t go through a recovery event.
I That reversed path also works. So any node infected at time twould cause infection of the initial node at time t in thereversed process.
I So the expected number of nodes infected at time t startingfrom infection of u at time 0 is equal to the probability u isinfected at time t if we infect a random individual at time 0.
I The equilibrium size of an SIS epidemic with Poissoniantransmission and recovery equals the probability that anepidemic occurs.
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Some conclusions
I An infection of u at time 0 leads to an infection of v at time tiff there is a path that doesn’t go through a recovery event.
I That reversed path also works. So any node infected at time twould cause infection of the initial node at time t in thereversed process.
I So the expected number of nodes infected at time t startingfrom infection of u at time 0 is equal to the probability u isinfected at time t if we infect a random individual at time 0.
I The equilibrium size of an SIS epidemic with Poissoniantransmission and recovery equals the probability that anepidemic occurs.
46 / 49
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Some conclusions
I An infection of u at time 0 leads to an infection of v at time tiff there is a path that doesn’t go through a recovery event.
I That reversed path also works. So any node infected at time twould cause infection of the initial node at time t in thereversed process.
I So the expected number of nodes infected at time t startingfrom infection of u at time 0 is equal to the probability u isinfected at time t if we infect a random individual at time 0.
I The equilibrium size of an SIS epidemic with Poissoniantransmission and recovery equals the probability that anepidemic occurs.
46 / 49
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Some conclusions
I An infection of u at time 0 leads to an infection of v at time tiff there is a path that doesn’t go through a recovery event.
I That reversed path also works. So any node infected at time twould cause infection of the initial node at time t in thereversed process.
I So the expected number of nodes infected at time t startingfrom infection of u at time 0 is equal to the probability u isinfected at time t if we infect a random individual at time 0.
I The equilibrium size of an SIS epidemic with Poissoniantransmission and recovery equals the probability that anepidemic occurs.
46 / 49
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SIS size vs Probability
1000 simulations starting with a single randomly chosen node in aConfiguration model network with P(1) = P(5) = 0.5.
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SIS size vs Probability
1000 simulations starting with a single randomly chosen node in aConfiguration model network with P(1) = P(5) = 0.5.
0 5 10 15 20 25 30
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
β = 0.5, γ = 1273 did not die out
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SIS size vs Probability
1000 simulations starting with a single randomly chosen node in aConfiguration model network with P(1) = P(5) = 0.5.
0 5 10 15 20 25 30
0.0
0.1
0.2
0.3
0.4
0.5
β = 0.75, γ = 1448 did not die out
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![Page 137: Part 5 Percolation approaches to disease spreadtjhladish.github.io/sismid/SISMID_Lecture5.pdf · Percolation We are going to explore a relationship between SIR disease and percolation.](https://reader036.fdocuments.in/reader036/viewer/2022062605/5fd26bec6ae80e5b09540034/html5/thumbnails/137.jpg)
SIS size vs Probability
1000 simulations starting with a single randomly chosen node in aConfiguration model network with P(1) = P(5) = 0.5.
0 5 10 15 20 25 30
0.0
0.1
0.2
0.3
0.4
0.5
0.6
β = 1, γ = 1537 did not die out
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![Page 138: Part 5 Percolation approaches to disease spreadtjhladish.github.io/sismid/SISMID_Lecture5.pdf · Percolation We are going to explore a relationship between SIR disease and percolation.](https://reader036.fdocuments.in/reader036/viewer/2022062605/5fd26bec6ae80e5b09540034/html5/thumbnails/138.jpg)
SIR and percolation
SIS disease
References
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![Page 139: Part 5 Percolation approaches to disease spreadtjhladish.github.io/sismid/SISMID_Lecture5.pdf · Percolation We are going to explore a relationship between SIR disease and percolation.](https://reader036.fdocuments.in/reader036/viewer/2022062605/5fd26bec6ae80e5b09540034/html5/thumbnails/139.jpg)
References I
[1] Joel C. Miller, Anja C. Slim, and Erik M. Volz.Edge-based compartmental modelling for infectious disease spread.Journal of the Royal Society Interface, 9(70):890–906, 2012.
[2] Joel C. Miller.Epidemics on networks with large initial conditions or changing structure.PLoS ONE, 9(7):e101421, 2014.
[3] Istvan Z Kiss, Joel C Miller, and Peter L Simon.Mathematics of epidemics on networks: from exact to approximate models.IAM. Springer, 2017.
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