Brad Greening Rutgers University Duration of Infectivity and Disease in Dynamic Networks Bobby Zandstra Florida Gulf Coast University Long- vs. Short-term.

Slides:



Advertisements
Similar presentations
Probability in Propagation
Advertisements

Trust relationships in sensor networks Ruben Torres October 2004.
Language requirements for adult migrants Results of a survey Some observations and reflections Linguistic integration of adult migrants Council of Europe.
Maximizing the Spread of Influence through a Social Network
Containing DoS Attacks in Broadcast Authentication in Sensor Networks (Ronghua Wang, Wenliang Du, Peng Ning) Containing DoS Attacks in Broadcast Authentication.
Population dynamics of infectious diseases Arjan Stegeman.
Forwarding Redundancy in Opportunistic Mobile Networks: Investigation and Elimination Wei Gao 1, Qinghua Li 2 and Guohong Cao 3 1 The University of Tennessee,
Presentation Topic : Modeling Human Vaccinating Behaviors On a Disease Diffusion Network PhD Student : Shang XIA Supervisor : Prof. Jiming LIU Department.
Epidemiology and Public Health Introduction, Part II.
By: Roma Mohibullah Shahrukh Qureshi
Are You moved by Your Social Network Application? Abderrahmen Mtibaa, Augustin Chaintreau, Jason LeBrun, Earl Oliver, Anna-Kaisa Pietilainen, Christophe.
Joe Dinius ECE Oct  Introduction  Review of the Literature  Hypotheses  Model Description  Parameters  Results  Sensitivity Analysis.
Nik Addleman and Jen Fox.   Susceptible, Infected and Recovered S' = - ßSI I' = ßSI - γ I R' = γ I  Assumptions  S and I contact leads to infection.
The Impact of Household Capital Models on Targeted Epidemiological Control Strategies for Diseases with Age-Based Etiologies Nina H. Fefferman EENR/DIMACS.
On the Construction of Energy- Efficient Broadcast Tree with Hitch-hiking in Wireless Networks Source: 2004 International Performance Computing and Communications.
Preventing Smallpox Epidemics Using a Computational Model By Chintan Hossain and Hiren Patel.
Prénom Nom Document Analysis: Artificial Neural Networks Prof. Rolf Ingold, University of Fribourg Master course, spring semester 2008.
Dynamic Network Security Deployment under Partial Information George Theodorakopoulos (EPFL) John S. Baras (UMD) Jean-Yves Le Boudec (EPFL) September 24,
Chapter 9 Capital Budgeting Decision Models  Short-term versus Long-term Decisions  Payback Period  Discounted Payback Period  Net Present Value (NPV)
Modeling the SARS epidemic in Hong Kong Dr. Liu Hongjie, Prof. Wong Tze Wai Department of Community & Family Medicine The Chinese University of Hong Kong.
John Nelson Huffman Mentor: Dr. Nina H. Fefferman.
Multicast Communication Multicast is the delivery of a message to a group of receivers simultaneously in a single transmission from the source – The source.
PEPA is based at the IFS and CEMMAP © Institute for Fiscal Studies Identifying social effects from policy experiments Arun Advani (UCL & IFS) and Bansi.
A Measurement-driven Analysis of Information Propagation in the Flickr Social Network WWW09 报告人: 徐波.
Chapter 8 – Capital Budgeting Decision Models  Learning Objectives  Differentiate between short term and long term capital budgeting models  Apply the.
Modeling the population dynamics of HIV/AIDS Brandy L. Rapatski James A. Yorke Frederick Suppe.
A Provocation: Social insects as an experimental model of network epidemiology Michael Otterstatter (CA)
Course Overview & Introduction to Social Network Analysis How to analyse social networks?
Fish Infectious Disease Model Case Study BSC417/517.
Contagion in Networks Networked Life NETS 112 Fall 2013 Prof. Michael Kearns.
Data Analysis in YouTube. Introduction Social network + a video sharing media – Potential environment to propagate an influence. Friendship network and.
UbiStore: Ubiquitous and Opportunistic Backup Architecture. Feiselia Tan, Sebastien Ardon, Max Ott Presented by: Zainab Aljazzaf.
Propagation Delay and Receiver Collision Analysis in WDMA Protocols I.E. Pountourakis, P.A. Baziana and G. Panagiotopoulos School of Electrical and Computer.
Nina H. Fefferman, Ph.D. Rutgers Univ. Balancing Workforce Productivity Against Disease Risks for Environmental and Infectious.
CODE RED WORM PROPAGATION MODELING AND ANALYSIS Cliff Changchun Zou, Weibo Gong, Don Towsley.
9.3: Sample Means.
Selfishness, Altruism and Message Spreading in Mobile Social Networks September 2012 In-Seok Kang
Network theory 101 Temporal effects What we are interested in What kind of relevant temporal /topological structures are there? Why? How does.
Dual-Region Location Management for Mobile Ad Hoc Networks Yinan Li, Ing-ray Chen, Ding-chau Wang Presented by Youyou Cao.
Mitigation strategies on scale-free networks against cascading failures Jianwei Wang Adviser: Frank,Yeong-Sung Lin Present by Chris Chang.
Choosing Freeways MS&E 220 Project David Andrew Harju Shan Liu Xi Wang Huangxuan Ying.
1 Iterative Integer Programming Formulation for Robust Resource Allocation in Dynamic Real-Time Systems Sethavidh Gertphol and Viktor K. Prasanna University.
On the Economic Viability of Network Architectures Roch Guerin, Kartik Hosanagar (University of Pennsylvania) Andrew Odlyzko, Zhi-Li Zhang (University.
 Probability in Propagation. Transmission Rates  Models discussed so far assume a 100% transmission rate to susceptible individuals (e.g. Firefighter.
 Tree in Sensor Network Patrick Y.H. Cheung, and Nicholas F. Maxemchuk, Fellow, IEEE 3 rd New York Metro Area Networking Workshop (NYMAN 2003)
Introduction to Statistical Models for longitudinal network data Stochastic actor-based models Kayo Fujimoto, Ph.D.
Introduction to the Study of Sociology. Primary Question What is sociology and why is it important and beneficial?
C. Savarese, J. Beutel, J. Rabaey; UC BerkeleyICASSP Locationing in Distributed Ad-hoc Wireless Sensor Networks Chris Savarese, Jan Beutel, Jan Rabaey.
Derivatives Usage in Risk Management by Non-Financial Firms: Evidence from Greece By Spyridon K. Kapitsinas PhD Center of Financial Studies, Department.
1 Finding Spread Blockers in Dynamic Networks (SNAKDD08)Habiba, Yintao Yu, Tanya Y., Berger-Wolf, Jared Saia Speaker: Hsu, Yu-wen Advisor: Dr. Koh, Jia-Ling.
Chapter 9: Introduction to the t statistic. The t Statistic The t statistic allows researchers to use sample data to test hypotheses about an unknown.
Brad Greening Rutgers University Duration of Infectivity and Disease in Dynamic Networks Bobby Zandstra Florida Gulf Coast University Long- vs. Short-term.
Biao Wang 1, Ge Chen 1, Luoyi Fu 1, Li Song 1, Xinbing Wang 1, Xue Liu 2 1 Shanghai Jiao Tong University 2 McGill University
Chapter 1: An Introduction to the Foundations of Sociology Theoretical Paradigms.
Improving the Residential Location Model for the New York Metropolitan Region Haiyun Lin City College of New York Project Advisors: Prof. Cynthia Chen,
Epidemic spreading on preferred degree adaptive networks Shivakumar Jolad, Wenjia Liu, R. K. P. Zia and Beate Schmittmann Department of Physics, Virginia.
Nina H. Fefferman, Ph.D. DIMACS Rutgers Univ. Does Securing Infrastructure Against Workforce-Depletion Depend on Whether the.
Contagion in Networks Networked Life NETS 112 Fall 2015 Prof. Michael Kearns.
Authors: Jiang Xie, Ian F. Akyildiz
Health Information Exchanges
Networks and Communication Systems Department
Networked Life NETS 112 Fall 2018 Prof. Michael Kearns
Figure 1. Random processes produce apparent cycles
Networked Life NETS 112 Fall 2017 Prof. Michael Kearns
Networked Life NETS 112 Fall 2014 Prof. Michael Kearns
Networked Life NETS 112 Fall 2016 Prof. Michael Kearns
Relationships.
Diffusion in Networks Dr. Henry Hexmoor Department of Computer Science Southern Illinois University Carbondale 1/17/2019.
Relationships.
Networked Life NETS 112 Fall 2019 Prof. Michael Kearns
Presentation transcript:

Brad Greening Rutgers University Duration of Infectivity and Disease in Dynamic Networks Bobby Zandstra Florida Gulf Coast University Long- vs. Short-term Friendships and the Spread of Disease Mentor: Prof. Nina Fefferman Presentation Date: July 17, 2008

Key Terms Social Network SEIS Model

Previous Research Performed Model disease spread within dynamic networks where associations shift according to preference lists based on three different measures of network centrality. Network Centrality metrics by which preference lists are formed: Betweenness Closeness Degree (Popularity)

We may also measure the “success” of the society modeled by our network by using “population-wide” versions of these network centrality measures: Degree (Popularity): Closeness: Betweenness: Previous Research Performed

Questions Addressed Long- vs. Short-term friendships and the spread of disease By keeping long term friendships and minimizing short-term contacts, are you less prone to getting a disease? Varying the percentages of long- vs. short-term social contacts on patterns of disease spread in a population over time. Varying the percentage of each duration of friendship among social contacts over time will affect disease dynamics.

Assumptions ComputerTest Node50 Outdegree5 Neighbors Changed0.2, 0.4, 0.6, 0.8, 1.0 Time (Iterations)200 Introduction of Disease50 Transmission %0.8 Repetitions300 Mod Exchange1,5,10,25, 50

Questions Addressed Duration of infectivity and disease in dynamic networks What happens if we make the following adjustments to the dynamic workings of the network: If we include a fixed structure such as a “family”? If individuals make “smart” decisions concerning what friends they pick up? If individuals aren’t “social” once they become sick?

Popularity Success of a Society containing Families

Closeness Success of a Society containing Families

Betweenness Success of a Society containing Families

Popularity Success of a Society containing Families and in which the sick are “unsociable”

Closeness Success of a Society containing Families and in which the sick are “unsociable”

Betweenness Success of a Society containing Families and in which the sick are “unsociable”

Popularity Success of a Society containing Families and in which individuals choose friends “smartly”

Closeness Success of a Society containing Families and in which individuals choose friends “smartly”

Betweenness Success of a Society containing Families and in which individuals choose friends “smartly”

Popularity Success of a Society containing Families, Unsociable Sick, & Smart Friend Choice

Closeness Success of a Society containing Families, Unsociable Sick, & Smart Friend Choice

Betweenness Success of a Society containing Families, Unsociable Sick, & Smart Friend Choice

Upcoming Goals Determine causation for the various reactions in the success rates of the society given the different parameters. Determine impact that these parameters have on the disease spread process, i.e. the variation in number of secondary infections, the rate of disease propagation, duration of the disease in the network, etc.