Presentation Topic : Modeling Human Vaccinating Behaviors On a Disease Diffusion Network PhD Student : Shang XIA Supervisor : Prof. Jiming LIU Department.

Slides:



Advertisements
Similar presentations
The Disease Dynamics research group at the Department of Applied Mathematics and Theoretical Physics (DAMTP) at the University of Cambridge is headed by.
Advertisements

Findings Department of Health and Human Services National Institutes of Health National Institute of General Medical Sciences Social Studies Physicist.
What is Epidemiology? (1)
‘Small World’ Networks (An Introduction) Presenter : Vishal Asthana
Lesson 7: Viruses.
Modeling Malware Spreading Dynamics Michele Garetto (Politecnico di Torino – Italy) Weibo Gong (University of Massachusetts – Amherst – MA) Don Towsley.
How can Modeling Help in Emerging Epidemics? John Grefenstette, PhD Public Health Dynamics Lab Health Policy & Management Pitt Public Health Dec 5, 2014.
Disease Dynamics in a Dynamic Social Network Claire Christensen 1, István Albert 3, Bryan Grenfell 2, and Réka Albert 1,2 Bryan Grenfell 2, and Réka Albert.
Population dynamics of infectious diseases Arjan Stegeman.
University of Buffalo The State University of New York Spatiotemporal Data Mining on Networks Taehyong Kim Computer Science and Engineering State University.
Health Aspect of Disaster Risk Assessment Dr AA Abubakar Department of Community Medicine Ahmadu Bello University Zaria Nigeria.
Harvard University Initiative for Global Health Global Health Challenges Social Analysis 76: Lecture 3.
H1N1: “Swine Flu”. Why you should care… Every year between 5 and 20% of the population gets the flu. The CDC estimates that the flu kills 36,000 people.
Avian Influenza - Pandemic Threat ? Reinhard Bornemann.
Pandemic Preparedness: Planning for Business Continuity, Productivity, and Resilience Rick Allen, PhD Peter Wald, MD, MPH September
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.
Prevention and control of communicable disease. Over the last century, infectious diseases have lost a lot of their threat to individuals’ health as well.
Presentation Topic : Vaccination Deployment in Protection against Influenza A (H1N1) Infection PhD Student : Shang XIA Supervisor : Prof. Jiming LIU Department.
Emergent Phenomena & Human Social Systems NIL KILICAY.
June 27, 2005 Predicting Human Papilloma Virus Prevalence and Vaccine Policy Effectiveness Courtney Corley Department of Computer Science University of.
Disease and Public Health Lecture 11 Medicine, Disease and Society in Britain,
Pandemic Influenza Preparedness Kentucky Department for Public Health Department for Public Health.
How does mass immunisation affect disease incidence? Niels G Becker (with help from Peter Caley ) National Centre for Epidemiology and Population Health.
Infectious Disease Epidemiology Sharyn Orton, Ph.D. American Red Cross, Rockville, MD Suggested reading: Modern Infectious Disease Epidemiology (1994)
Methods to Study and Control Diseases in Wild Populations Steve Bellan, MPH Department of Environmental Sci, Pol & Mgmt University of California at Berkeley.
Health promotion and health education programs. Assumptions of Health Promotion Relationship between Health education& Promotion Definition of Program.
Spreading of Epidemic Based on Human and Animal Mobility Pattern
Epidemiology modeling with Stella CSCI Stochastic vs. deterministic  Suppose there are 1000 individuals and each one has a 30% chance of being.
HEALTH, WELLNESS AND ILLNESS. W HAT I S H EALTH ?
Epidemiology.
Emerging Infectious Disease: A Computational Multi-agent Model.
Are global epidemics predictable ? V. Colizza School of Informatics, Indiana University, USA M. Barthélemy School of Informatics, Indiana University, USA.
Best Practice Guideline for the Workplace During Pandemic Influenza Occupational Health and Safety Employment Standards.
Your Friends Have More Friends Than You Do: Identifying Influential Mobile Users Through Random Walks Bo Han, Aravind Srinivasan University of Maryland.
Learning Goals Appreciate that events on the other side of the world affect us.
According to UNAIDS estimates, there are now 33.3 million people living with HIV, including 2.5 million children. During 2009 some 2.6 million people became.
Prediction Assisted Single-copy Routing in Underwater Delay Tolerant Networks Zheng Guo, Bing Wang and Jun-Hong Cui Computer Science & Engineering Department,
A Data Intensive High Performance Simulation & Visualization Framework for Disease Surveillance Arif Ghafoor, David Ebert, Madiha Sahar Ross Maciejewski,
EpiFast: A Fast Algorithm for Large Scale Realistic Epidemic Simulations on Distributed Memory Systems Keith R. Bisset, Jiangzhuo Chen, Xizhou Feng, V.S.
Epidemiology. Activity: 1.You will need to wear gloves for all class activities today. 2.Pick a random identification card up from the front desk (record.
Examining Dynamic Trust Relationships in Autonomy-Oriented Partner Finding Department of Computer Science, HKBU, HK International WIC Institute, BJUT,
Pandemic Influenza: A Primer for Organizational Preparation Pandemic Influenza: A Primer for Organizational Preparation Kristine Perkins, MPH Director,
The Vermont Department of Health Overview of Pandemic Influenza Regional Pandemic Planning Summits 2006 Guidance Support Prevention Protection.
E PIDEMIC SPREADING Speaker: Ao Weng Chon Advisor: Kwang-Cheng Chen 1.
1 EPIDEMIOLOGY 200B Methods II – Prediction and Validity Scott P. Layne, MD.
Mathematical Modeling of Bird Flu Propagation Urmi Ghosh-Dastidar New York City College of Technology City University of New York December 1, 2007.
RPI (2009) To: The Rensselaer Community From: Leslie Lawrence, M.D. Medical Director, Student Health Center Date: November 23, 2009 Re: H1N1 Update.
Diseases Unit 3. Disease Outbreak  A disease outbreak happens when a disease occurs in greater numbers than expected in a community, region or during.
Dynamic Random Graph Modelling and Applications in the UK 2001 Foot-and-Mouth Epidemic Christopher G. Small Joint work with Yasaman Hosseinkashi, Shoja.
Influenza epidemic spread simulation for Poland – A large scale, individual based model study.
Bio-Networking: Biology Inspired Approach for Development of Adaptive Network Applications 21 May 2005Ognen Paunovski Bio-Networking: Biology Inspired.
Infectious disease Definition
Coevolution of Epidemics, Social Networks, and Individual Behavior: A Case Study Joint work with Achla Marathe, and Madhav Marathe Jiangzhuo Chen Network.
S imulating SARS … Small-World Epidemiological Modeling and Public Health Policy Assessments Ji-Lung Hsieh ( 謝吉隆 ) Department of Computer Science, National.
Disease and Public Health Lecture 11 Medicine, Disease and Society in Britain,
1 Food and Agriculture Organization of the United Nation Regional Office for Asia and the Pacific Emergency Center for Transboundary Animal Diseases Special.
AUSTRALIA INDONESIA PARTNERSHIP FOR EMERGING INFECTIOUS DISEASES Basic Field Epidemiology Session 6 – How disease progresses.
The Vermont Department of Health Update on Pandemic Threat Cort Lohff, MD, MPH State Epidemiologist Guidance Support Prevention Protection.
Mining information from social media
What Is H1N1 (Swine Flu) Pandemic Influenza? Colorized image of H1N1 from a transmission electron micrograph. Source: CDC.
Rubella Surveillance and Control in Low-Resource Settings: Limitations, Biases, and Potential for Strengthening Amy Winter, PhD Candidate, Princeton University.
Copyright © 2008 Delmar. All rights reserved. Chapter 4 Epidemiology and Public Health Nursing.
Epidemic spreading on preferred degree adaptive networks Shivakumar Jolad, Wenjia Liu, R. K. P. Zia and Beate Schmittmann Department of Physics, Virginia.
Chapter 11: Nursing in Pandemics and Emergency Preparedness.
Hiroki Sayama NECSI Summer School 2008 Week 2: Complex Systems Modeling and Networks Network Models Hiroki Sayama
Epidemic Alerts EECS E6898: TOPICS – INFORMATION PROCESSING: From Data to Solutions Alexander Loh May 5, 2016.
Epidemiological Modeling to Guide Efficacy Study Design Evaluating Vaccines to Prevent Emerging Diseases An Vandebosch, PhD Joint Statistical meetings,
Department of Computer Science University of York
Susceptible, Infected, Recovered: the SIR Model of an Epidemic
Chapter 1 Health: The Foundation of Life
Presentation transcript:

Presentation Topic : Modeling Human Vaccinating Behaviors On a Disease Diffusion Network PhD Student : Shang XIA Supervisor : Prof. Jiming LIU Department of Computer Science August 31, th Postgraduate Research Symposium

Content: 1 2 Research Motivation & Objectives Disease Diffusion Dynamics 3 Individual Behaviors and Interactions 4 AOC Modeling for Local-Global Relationship Page 1/15 5 Conclusions

 In recent years, the emergence of vital epidemic which spreads all over the world have greatly endangered the public health and cause great social impacts. Swine Flu Spreading Map Research Background  SARS, ~ ,096 known infected cases and 774 deaths worldwide  Bird Flu, (H5N1) Recent Years 65 outbreaks in 2006; 55 in 2007; 11 in  Swine Flu, (H1N1) ~ Now 177,699 infected cases and 1,126 deaths ( Aug. 6 th ) Page 2/15

Swine Flu World Wide Spreading 04/24/200905/04/200905/26/ /16/200907/06/200907/30/2009 Data Source: [1] WHO, ECDC, CPC, HPA (UK), governments [2] BBC Website: Page 3/15

Research Motivation  Disease Diffusion on Human Social Contact Network. Human is the host of many severe infectious disease. Human’s traveling and interaction spread infection worldwide.  Disease Diffusion changes human’s behavioral pattern. Decision making for vaccination or not. Individual changes its social interactions.  Human’s contact pattern and demography features characterize disease diffusion dynamics. Human social contact is the medium of disease diffusion. Human demographical characteristics influence disease infection. Page 4/15

Research Objectives Local Individual Reaction Pattern Relationship ? Global Disease Diffusion Dynamics Human Interaction Network  Human Interaction Network Disease Infection Model  Disease Infection Model Human Behavioral Mechanism  Human Behavioral Mechanism Disease Diffusion Dynamics  Disease Diffusion Dynamics  Local-Global Relationship Problem Page 5/15 Complex Social Disease Diffusion System

Disease Infection Model SIV Percolation Model  Individual States in Network Susceptible Individual Infected Individual Vaccinated Individual  Epidemic Transmission in Network  Individual States Transition Neighbor Infections. Infected individuals die or recover. Vaccination escape potential infection. Random Selection for a neighbor susceptible. Selection Probability in terms of contact patterns. Page 6/15

Short Range Routine Mobile Long Range Chance Traveling Trajectory from home to workplace; Regular shopping in nearby supermarket; Visiting familiar friends or customers. Enjoying vacations abroad; Business trip to other regions; Social Contact Network Human Mobility Dynamics Human Social Contact Network (1) Scale Free Network Page 7/15

Human Social Contact Network (2)  Heterogeneity in demography difference  Heterogeneity in community structure Disease - Independent host parameters Ages, Gender, Occupation and so on. Community structure by contact pattern. Community structure by demography characteristics Disease - Dependent host parameters Current health status, Susceptibility, Disease transmission rate, etc.  Heterogeneity in contact pattern Degree Distribution : Neighbor Contacts Edge Weight : Contact Frequency Page 8/15

Individual Behavioral Mechanism (1)  Individual Vaccination Dilemma Herd Immunity Dilemma: T he individual incentive to vaccinate disappears at high coverage levels. So the individual incline to persuade other instead of itself to adopt the vaccination. Self-Interest Decision Page 9/15 Risk of Vaccination Risk of Infection Self-Trust Experience Group Psychology Irrational Predictions

Individual Behavioral Mechanism (2)  Individual Decision Making Dynamics Perceived Payoff for Vaccination or not  Perceived Payoff for Vaccination or not Records of History Decision Making  Records of History Decision Making Environment Estimation  Environment Estimation Individuals’ Biased Preference  Individuals’ Biased Preference Decision Making Mechanism Page 10/15

Local - Global Relationship Modeling Autonomy Oriented Computing “ AOC emphasize the modeling of autonomy in the entities of a complex system and the self-organization of them in achieving a specific goal ” -- By Liu (2005) AOC Modeling Framework Natural System Identification Artificial System Construction Performance Measurement Page 11/15

AOC Modeling  Natural System Identification  Artificial System Construction  Performance Measurement Disease Infection Model Individual Contact Network Individual Interaction Patterns Individual Behaviors Mechanism Vaccination Patterns Disease Diffusion Dynamics Multi-Agent System Autonomous Entities & Self-Organization Local-Global Relationship Page 12/15

Result Evaluation Criteria  Social Vaccination Dynamics  Disease Diffusion Dynamics The proportion of vaccinated individuals. The Efficacy of Vaccination. The proportion of vaccinated individuals which are infected by its infectious neighbors. The proportion of non-vaccinated individuals which are also infected. The proportion of infected individuals in the whole population. Page 13/15 The mass outbreak in a certain community.

Conclusion  Social Contact Network  Entities Infection Model  Decision Making  Vaccination Dynamics  Disease Diffusion Dynamics Local Behaviors Global Dynamics Page 14/15

Q & A Thank You Very Much!