Epidemic models over networks

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Epidemic models over networks

Argimiro Arratia & R. Ferrer-i-Cancho

Universitat Polit`ecnica de Catalunya

Complex and Social Networks (2020-2021)

Master in Innovation and Research in Informatics (MIRI)

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Official website: www.cs.upc.edu/~csn/

Contact:

I Ramon Ferrer-i-Cancho, rferrericancho@cs.upc.edu, http://www.cs.upc.edu/~rferrericancho/

I Argimiro Arratia, argimiro@cs.upc.edu,

http://www.cs.upc.edu/~argimiro/

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Epidemic models

Epidemic models attempt to capture the dynamics in the spreading of a disease (or idea, computer virus, product adoption).

Central questions they try to answer are:

I How do contagions spread in populations?

I Will a disease become an epidemic?

I Who are the best people to vaccinate?

I Will a given YouTube video go viral?

I What individuals should we market to for maximizing product

penetration?

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In today’s lecture

Classic epidemic models (full mixing) The SI model

The SIR model The SIS model

Epidemic models over networks Homogeneous models

A general network model for SIS

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Classic epidemic models (full mixing) Epidemic models over networks

The SI model The SIR model The SIS model

Full mixing in classic epidemiological models

Full mixing assumption

In classic epidemiology, it is assumed that every individual has an equal chance of coming into contact with every other individual in the population

half of the lecture)!

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Full mixing in classic epidemiological models

Full mixing assumption

In classic epidemiology, it is assumed that every individual has an equal chance of coming into contact with every other individual in the population

Dropping this assumption by making use of an underlying contact

network leads to the more realistic models over networks (second

half of the lecture)!

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The SI model ( fully mixing susceptible – infected )

Notation (following [Newman, 2010])

I Let S (t) be the number of individuals who are susceptible to sickness at time t

I Let X (t) be the number of individuals who are infected at time t 1

I Total population size is n

I Contact with infected individuals causes a susceptible person to become infected

I An infected never recovers and stays infected and infectious to others

1 Well, really S and X are random variables and we want to capture number

of infected and susceptible in expectation.

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The SI model

In the SI model, individuals can be in one of two states:

I infective (I), or

I susceptible (S) S - I

The parameters of the SI model are

I β infection rate: probability of contagion after contact per

unit time

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The SI model

Dynamics

dX

dt = β SX

n and dS

dt = −β SX n where

I S /n is the probability of meeting a susceptible person at random per unit time

I XS /n is the average number of susceptible people that infected nodes meet per unit time

I βXS /n is the average number of susceptible people that

become infected from all infecteds per unit time

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The SI model

Logistic growth equation and curve

Define s = S /n and x = X /n, since S + X = n or equivalently s + x = 1, we get:

dx

dt = β(1 − x )x

The solution to the differential equation (known as the “logistic growth equation”) leads to the logistic growth curve

x (t) = x 0 e βt

1 − x 0 + x 0 e βt

where x (0) = x 0

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The SI model

Logistic growth equation and curve

x (t) = x 0 e βt 1 − x 0 + x 0 e βt

2 4 6 8 10

0.0 0.2 0.4 0.6 0.8 1.0

t

Fr action of inf ected x

Logistic growth curve

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Solving the logistic growth equation I

dx

dt = β(1 − x )x

⇐⇒

Z x x 0

1

(1 − x )x dx = Z t

0

βdt

⇐⇒

Z x

x 0

1

(1 − x ) dx + Z x

x 0

1

x dx = βt − β0

⇐⇒

Z x

x 0

1

(1 − x ) dx + Z x

x 0

1

x dx = βt

⇐⇒ ln 1 − x 0 1 − x + ln x

x 0 = βt

⇐⇒ ln (1 − x 0 )x

(1 − x )x 0 = βt

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Solving the logistic growth equation II

ln (1 − x 0 )x (1 − x )x 0 = βt

⇐⇒ (1 − x 0 )x (1 − x )x 0 = e βt

⇐⇒ x

1 − x = x 0 e βt 1 − x 0

⇐⇒ 1 − x

x = 1 − x 0

x 0 e βt

⇐⇒ 1

x = 1 − x 0

x 0 e βt + 1 = 1 − x 0 + x 0 e βt x 0 e βt

⇐⇒ x = x 0 e βt

1 − x 0 + x 0 e βt

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The SIR model

Allowing recovery and immunity

In the SIR model, individuals can be in one of two states:

I infective (I), or

I susceptible (S), or

I recovered (R) S - I - R

The parameters of the SIR model are

I β infection rate: probability of contagion after contact per unit time

I γ recovery rate: probability of recovery from infection per unit

time

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The SIR model

Dynamics

ds

dt = −βsx dx

dt = βsx − γx dr dt = γx

The solution to this system (with s + x + r = 1) is not analytically

tractable, but solutions look like the following:

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The SIR model I

A threshold phenomenon

Now we are interested in considering the fraction of the population that will get sick (i.e. size of the epidemic), basically captured by r (t) as t → ∞

Substituting dt = γx dr from the third equation into ds = −βsxdt and solving for s (assuming r 0 = 0), we obtain that

s(t) = s 0 e β γ r and so

dr

dt = γ(1 − r − s 0 e β γ r )

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The SIR model II

A threshold phenomenon

As t → ∞, we get that r(t) stabilizes and so dr dt = 0, thus:

r = 1 − s 0 e β γ r

Assume that s 0 ≈ 1, since typically we start with a small nr. of

infected individuals and we are considering large populations, and

so r = 1 − e β γ r

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The SIR model III

A threshold phenomenon

0.0 0.2 0.4 0.6 0.8 1.0

0.00.20.40.60.81.0

r

y=1−e(−0.5r) y=1−e(−1r) y=1−e(−1.5r) y=r

0 1 2 3 4

0.00.20.40.60.81.0

β γ

epidemic size in the limit

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The SIR model IV

A threshold phenomenon

I if β γ 6 1 then no epidemic occurs

I if β γ > 1 then epidemic occurs

I β = γ is the epidemic transition

0 1 2 3 4

0.00.20.40.60.81.0

β γ

epidemic size in the limit

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The SIR model

The basic reproduction number R 0

Basic reproduction number R 0

R 0 is the average number of additional people that a newly infected person passes the disease onto before they recover 2 .

I R 0 > 1 means each infected person infects more than 1 person and hence the epidemic grows exponentially (at least at the early stages)

I R 0 < 1 makes the epidemic shrink

I R 0 = 1 marks the epidemic threshold between the growing and shrinking regime

In the SIR model, R 0 = β γ

2 It is defined for the early stages of the epidemic and so one can assume

that most people are in the susceptible state.

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The SIS model

People can cure but do not become immune

In the SIS model, individuals can be in one of two states:

I infective (I), or

I susceptible (S) S  - I

The parameters of the SI model are

I β infection rate: probability of contagion after contact per unit time

I γ recovery rate: probability of recovery from infection per unit

time

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The SIS model

Dynamics

ds

dt = γx − βsx dx

dt = βsx − γx

Using s + x = 1, we can solve the system analytically obtaining x (t) = x 0 (β − γ)e (β−γ)t

β − γ + βx 0 e (β−γ)t

Intuition: The SIS models the flu

while the SIR models the mumps

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The SIS model

Examples

β = 0.8, γ = 0.4

0 5 10 15 20

0.00.10.20.30.40.5

Time t

Proportion of infected x(t)

I logistic growth curve

I steady state at x = β−γ β

β = 0.4, γ = 0.8

0 5 10 15 20

0.0000.0020.0040.0060.0080.010

Time t

Proportion of infected x(t)

I exponential decay

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The SIS model

The basic reproduction number R 0

I The point β = γ marks the epidemic transition

I In the SIS model, R 0 = β γ

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In today’s lecture

Classic epidemic models (full mixing) The SI model

The SIR model The SIS model

Epidemic models over networks Homogeneous models

A general network model for SIS

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Homogeneous network models

All nodes have degree very close to hki (e.g. Erd¨ os-R´ enyi networks or regular lattices)

We can re-write the equation of the epidemic models taking into

account that individuals have approximately hki possibilities of

contagion from neighbors

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Homogeneous SI model

Equations of dynamics dx

dt = βhkix(1 − x) ds

dt = −βhkis(1 − s) Solution

x (t) = x 0 e βhkit 1 − x 0 + x 0 e βhkit Observations

I Same behavior as in the non-networked model

I Growth of infecteds depends on hki as well as β

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Homogeneous SIR model

Equations of dynamics

ds

dt = −βhkisx dx

dt = βhkisx − γx dr dt = γx Epidemic threshold

I if β γ 6 hki 1 then no epidemic occurs

I if β γ > hki 1 then epidemic occurs Observations

I Same behavior as in the non-networked model

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Homogeneous SIS model

Equations of dynamics ds

dt = γx − βhkisx dx

dt = βhkisx − γx Solution

x (t) = x 0

(βhki − γ)e (βhki−γ)t βhki − γ + βhkix 0 e (βhki−γ)t Observations

I Same behavior as in the non-networked model

I Epidemic threshold at βhki − γ = 1

I Equivalent to β γ 6 hki 1 , same as SIR

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A general network model for SIS [Chakrabarti et al., 2008]

Now we need to consider that infection can be through existing connections

I A is the adjacency matrix of the underlying contact network, and A ij is the entry corresponding to the potential edge between nodes i and j

I Assume A is symmetric (contagion goes in both ways) and has dimension n × n (n is the population size)

I s i (t) is the probability of node i being susceptible to disease at time t

I x i (t) is the probability of node i being infected at time t

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Model dynamics I

From [Chakrabarti et al., 2008]

“During each time interval ∆t, an infected node i tries to infect its

neighbors with probability β. At the same time, i may be cured

with probability γ.”

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Model dynamics II

Notation

I Let x i (t) be the probability that node i is infected at time t

I Let ζ i (t) be the probability that a node i will not receive infections from its neighbors in the next time step

ζ i (t) = Y

j :i −j

j fails to pass infection

z }| {

x j (t − 1)(1 − β) +

j is not infected

z }| {

(1 − x j (t − 1))

= Y

j :i −j

1 − x j (t − 1)β

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Model dynamics III

Then, the probability that a node i is uninfected is:

1−x i (t) =

neighbors fail to infect

z}|{

ζ i (t) (

node is healthy

z }| {

(1 − x i (t − 1)) +

node is infected and cures

z }| { γx i (t − 1) ) Finally, the fraction of infecteds is computed as:

x (t) = X

i

x i (t)

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Threshold phenomenon I

Theorem

The epidemic threshold of the SIS model over arbitrary networks is

1

λ 1 , where λ 1 is the largest eigenvalue of the underlying contact network, that is:

I If β γ > λ 1

1 then epidemic occurs

I If β γ < λ 1

1 then no epidemic occurs

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Threshold phenomenon II

ζ i (t) = Y

j :i −j

1 − x j (t − 1)β

> 1 − β X

j :i −j

x j (t − 1)

= 1 − β X

j

A ij x j (t − 1)

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Threshold phenomenon III

x i (t) = 1 − (1 − (1 − γ)x i (t − 1))ζ i (t) 6 1 − (1 − (1 − γ)x i (t − 1))(1 − β X

j

A ij x j (t − 1))

= 1 − (1 − (1 − γ)x i )(1 − β X

j

A ij x j )

= 1 −

1 − (1 − γ)x i − β X

j

A ij x j + (1 − γ)x i β X

j

A ij x j

= (1 − γ)x i + β X

j

A ij x j − (1 − γ)x i β X

j

A ij x j 6 (1 − γ)x i + β X

j

A ij x j

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Threshold phenomenon IV

In matrix notation:

x(t) 6 ((1 − γ)I + βA)x(t − 1) Define S = βA + (1 − γ)I, then

x(t) 6 Sx(t − 1) 6 S 2 x(t − 2) 6 ... 6 S t x(0) Assuming that x(0) = P

r a r v r , where v r are the eigenvectors of S x(t) 6 S t X

r

a r v r = X

r

r ) t a r v r

From linear algebra we know that λ 1 > 0 (matrix S is symmetric

and real) and also λ 1 > λ 2 > .. > λ r . For t → ∞, the sum is

dominated by the first eigenvalue and so

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Threshold phenomenon V

x(t) 6 (λ 1 ) t a 1 v 1

If λ 1 < 1, then the epidemic must vanish (the other direction also holds, check [Chakrabarti et al., 2008]).

Finally, the relation between the eigenvalues of S = (1 − γ)I + βA matrix and the ones of A matrix is, for all r :

λ S r = 1 − γ + βλ A r

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Threshold phenomenon VI

So the final threshold (w.r.t. leading eigenvalue of A)

λ S 1 < 1

⇐⇒ 1 − γ + βλ A 1 < 1

⇐⇒ 1 + βλ A 1 < 1 + γ

⇐⇒ βλ A 1 < γ

⇐⇒ β

γ < 1

λ A 1

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References I

Chakrabarti, D., Wang, Y., Wang, C., Leskovec, J., and Faloutsos, C. (2008).

Epidemic thresholds in real networks.

ACM Transactions on Information and System Security (TISSEC), 10(4):1.

Newman, M. (2010).

Networks: An Introduction.

Oxford University Press, USA, 2010 edition.

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