U.S. Imperialism (1898-1914) Spreading imperialism OR spreading democracy?
Class 21: Spreading Phenomena PartI
-
Upload
prosper-stone -
Category
Documents
-
view
225 -
download
1
description
Transcript of Class 21: Spreading Phenomena PartI
![Page 1: Class 21: Spreading Phenomena PartI](https://reader035.fdocuments.net/reader035/viewer/2022081505/5a4d1b0d7f8b9ab05998c2e7/html5/thumbnails/1.jpg)
Class 21: Spreading Phenomena PartI
Yong-Yeol Ahn and Alex Vespignani ; Network Science: Epidemic Modeling 2015
Prof. Boleslaw Szymanski
![Page 2: Class 21: Spreading Phenomena PartI](https://reader035.fdocuments.net/reader035/viewer/2022081505/5a4d1b0d7f8b9ab05998c2e7/html5/thumbnails/2.jpg)
Network Science: Epidemic Modeling 2015
Epidemic spreading – Why?
Why is the spreading process important?
![Page 3: Class 21: Spreading Phenomena PartI](https://reader035.fdocuments.net/reader035/viewer/2022081505/5a4d1b0d7f8b9ab05998c2e7/html5/thumbnails/3.jpg)
“Epidemic”
Epi + demosupon people
Biological:Airborne diseases (flu, SARS, …)• Venereal diseases (HIV, …) • Other infectious diseases including
some cancers (HPV, …)• Parasites (bedbugs, malaria, …)
Digital:• Computer viruses, worms• Mobile phone viruses
Conceptual/Intellectual:• Diffusion of innovations• Rumors• Memes• Business practices
http://en.wikipedia.org/wiki/Epidemic
![Page 4: Class 21: Spreading Phenomena PartI](https://reader035.fdocuments.net/reader035/viewer/2022081505/5a4d1b0d7f8b9ab05998c2e7/html5/thumbnails/4.jpg)
The Great Plague SARSHIV
Biological: Notable Epidemic Outbreaks
1918 Spanish flu
H1N1 flu
![Page 5: Class 21: Spreading Phenomena PartI](https://reader035.fdocuments.net/reader035/viewer/2022081505/5a4d1b0d7f8b9ab05998c2e7/html5/thumbnails/5.jpg)
Epidemic spreading – Why does it matter now?
High population density High mobility
perfect conditions for epidemic spreading.Airline figure: L. Hufnagel et al. PNAS 101, 15124 (2004)
![Page 6: Class 21: Spreading Phenomena PartI](https://reader035.fdocuments.net/reader035/viewer/2022081505/5a4d1b0d7f8b9ab05998c2e7/html5/thumbnails/6.jpg)
Network Science: Epidemic Modeling 2015
Separate, small population(hunter-gatherer society, wild animals)
Connected, highly populated areas(cities)
Large population can provide the “fuel”
Human societies have “crowd diseases”, which are the consequences of large, interconnected populations (Measles, tuberculosis, smallpox, influenza, common cold, …)
![Page 7: Class 21: Spreading Phenomena PartI](https://reader035.fdocuments.net/reader035/viewer/2022081505/5a4d1b0d7f8b9ab05998c2e7/html5/thumbnails/7.jpg)
14th Century – The Great Plague
4 years from France to Sweden
Limited by the speed of human travel
http://en.wikipedia.org/wiki/Black_Deathhttp://de.wikipedia.org/wiki/Schwarzer_Tod
![Page 8: Class 21: Spreading Phenomena PartI](https://reader035.fdocuments.net/reader035/viewer/2022081505/5a4d1b0d7f8b9ab05998c2e7/html5/thumbnails/8.jpg)
21st Century – SARS
Source: World Health Organization
![Page 9: Class 21: Spreading Phenomena PartI](https://reader035.fdocuments.net/reader035/viewer/2022081505/5a4d1b0d7f8b9ab05998c2e7/html5/thumbnails/9.jpg)
Hypponen M. Scientific American Nov. 70-77 (2006).
Computer Viruses, Worms, Mobile Phone Viruses
Code Red Worm paralyzed many countries’ Internet
http://www.caida.org/publications/visualizations/
![Page 10: Class 21: Spreading Phenomena PartI](https://reader035.fdocuments.net/reader035/viewer/2022081505/5a4d1b0d7f8b9ab05998c2e7/html5/thumbnails/10.jpg)
Early adopters
Innovators
Early majority
Late majority
Laggards
Broadcast
Contagion
Diffusion of Innovation – The Adoption Curve
Reference unknown
![Page 11: Class 21: Spreading Phenomena PartI](https://reader035.fdocuments.net/reader035/viewer/2022081505/5a4d1b0d7f8b9ab05998c2e7/html5/thumbnails/11.jpg)
Spreading of InfluenceInformation Spreading
![Page 12: Class 21: Spreading Phenomena PartI](https://reader035.fdocuments.net/reader035/viewer/2022081505/5a4d1b0d7f8b9ab05998c2e7/html5/thumbnails/12.jpg)
• Epidemic spreading always implies network structure!
Epidemic Spreading – Network
Spreading happens only when the carries of the diseases/virus/idea are connected to each other.
![Page 13: Class 21: Spreading Phenomena PartI](https://reader035.fdocuments.net/reader035/viewer/2022081505/5a4d1b0d7f8b9ab05998c2e7/html5/thumbnails/13.jpg)
Epidemic Spreading – Network
The transportation network Internet
![Page 14: Class 21: Spreading Phenomena PartI](https://reader035.fdocuments.net/reader035/viewer/2022081505/5a4d1b0d7f8b9ab05998c2e7/html5/thumbnails/14.jpg)
Epidemic Spreading – Network
The transportation network Internet
L. Hufnagel et al. PNAS 101, 15124 (2004) http://www.caida.org/publications/visualizations/
![Page 15: Class 21: Spreading Phenomena PartI](https://reader035.fdocuments.net/reader035/viewer/2022081505/5a4d1b0d7f8b9ab05998c2e7/html5/thumbnails/15.jpg)
Network Science: Epidemic Modeling 2015
Types of Spreading Phenomena and Networks
Phenomena Network Agent
Venereal disease Sexual network pathogens
Other infectious disease Contact network, transport network
pathogens
Rumor spreading Communication network Information, memes
Diffusion of innovation Communication network Ideas
Internet worms Internet Malwares (binary strings)
Mobile phone virus Social network / proximity network
Malwares (binary strings)
Bedbugs Hotel – traveler network Bedbugs
Malaria Mosquito – Human network Plasmodium
![Page 16: Class 21: Spreading Phenomena PartI](https://reader035.fdocuments.net/reader035/viewer/2022081505/5a4d1b0d7f8b9ab05998c2e7/html5/thumbnails/16.jpg)
Network Science: Epidemic Modeling 2015
Classical Models of Epidemics
Epidemic Modeling (classical models)
![Page 17: Class 21: Spreading Phenomena PartI](https://reader035.fdocuments.net/reader035/viewer/2022081505/5a4d1b0d7f8b9ab05998c2e7/html5/thumbnails/17.jpg)
Classical Epidemic Models – Basic States
Susceptible (healthy)
Infected (sick)
Removed(immune / dead)
S I RInfection
Recovery
Recovery
Removal
![Page 18: Class 21: Spreading Phenomena PartI](https://reader035.fdocuments.net/reader035/viewer/2022081505/5a4d1b0d7f8b9ab05998c2e7/html5/thumbnails/18.jpg)
SIS Model: Common Cold
Susceptible (healthy)
Infected (sick)
Removed(immune / dead)
S I RInfection
Recovery
Recovery
Removal
![Page 19: Class 21: Spreading Phenomena PartI](https://reader035.fdocuments.net/reader035/viewer/2022081505/5a4d1b0d7f8b9ab05998c2e7/html5/thumbnails/19.jpg)
Example 2: Flu, SARS, Plague, …
Susceptible (healthy)
Infected (sick)
Removed(immune / dead)
S I RInfection
Recovery
Recovery
Removal
![Page 20: Class 21: Spreading Phenomena PartI](https://reader035.fdocuments.net/reader035/viewer/2022081505/5a4d1b0d7f8b9ab05998c2e7/html5/thumbnails/20.jpg)
Simplest Model: SI
Susceptible (healthy)
Infected (sick)
Removed(immune / dead)
S I RInfection
Recovery
Recovery
Removal
![Page 21: Class 21: Spreading Phenomena PartI](https://reader035.fdocuments.net/reader035/viewer/2022081505/5a4d1b0d7f8b9ab05998c2e7/html5/thumbnails/21.jpg)
SI Model: Homogeneous Mixing (No network)
Each individual has β contacts with randomly chosen others individuals per unit time.
M. E. J. Newman, Networks: an introduction
with
![Page 22: Class 21: Spreading Phenomena PartI](https://reader035.fdocuments.net/reader035/viewer/2022081505/5a4d1b0d7f8b9ab05998c2e7/html5/thumbnails/22.jpg)
Susceptible + Infected more Infected
SI Model: Dynamics
SI
http://en.wikipedia.org/wiki/Logistic_functionhttp://mathworld.wolfram.com/LogisticEquation.htmlLogistic equation: a basic model of population growth.
![Page 23: Class 21: Spreading Phenomena PartI](https://reader035.fdocuments.net/reader035/viewer/2022081505/5a4d1b0d7f8b9ab05998c2e7/html5/thumbnails/23.jpg)
If i(t) is small,
exponential outbreak
As i(t) 1.
SI Model – Behavior
saturation
Time (t)
Frac
tion
Infe
cted
i(t)
SI model: the fraction infected increases until everyone is infected.
![Page 24: Class 21: Spreading Phenomena PartI](https://reader035.fdocuments.net/reader035/viewer/2022081505/5a4d1b0d7f8b9ab05998c2e7/html5/thumbnails/24.jpg)
SIS Model: Common Cold
Susceptible (healthy)
Infected (sick)
Removed(immune / dead)
S I RInfection
Recovery
Recovery
Removal
![Page 25: Class 21: Spreading Phenomena PartI](https://reader035.fdocuments.net/reader035/viewer/2022081505/5a4d1b0d7f8b9ab05998c2e7/html5/thumbnails/25.jpg)
SIS Model
SI IS
Stationary state:
Frac
tion
Infe
cted
i(t)
Time (t)
SIS model: fraction infected individuals saturates below 1.
![Page 26: Class 21: Spreading Phenomena PartI](https://reader035.fdocuments.net/reader035/viewer/2022081505/5a4d1b0d7f8b9ab05998c2e7/html5/thumbnails/26.jpg)
SIS Model: Epidemic Threshold and Basic Reproductive Number
SI IS “Epidemic threshold”
“Basic reproductive number”
On average, how many uninfected individuals will be infected by one infected individual?