S imulating SARS … Small-World Epidemiological Modeling and Public Health Policy Assessments Ji-Lung Hsieh ( 謝吉隆 ) Department of Computer Science, National Chiao Tung University Chung-Yuan Huang ( 黃崇源 ) Department of Computer Science and Information Engineering, Chang Gung University Chuen-Tsai Sun ( 孫春在 ) Department of Computer Science, National Chiao Tung University Yi-Ming Arthur Chen ( 陳宜民 ) Institute of Public Health, National Yang-Ming University

2/25 When SARS outbreak began Confirm viral structure. Develop vaccines and antidotes. Establish faster inspection methods. Revise public health policies and prevention strategies. Taking Body TemperatureMask Wearing for General Public

3/25 Public Health Policies Balancing the social costs and resource expenditures required for controlling epidemic outbreaks Mask Policy—General Public vs. Healthcare Workers Taking Body Temperature A/B Class Home Quarantines Reducing Public Contact, Controlling Hospital Access Vaccines, Antidotes, … Improper implementation & inappropriate timing Secondary impacts as disease concealment Social discrimination against SARS patients and health care workers Panic buying of masks (ex. N95 mask).

4/25 A Suitable Epidemic Simulation Platform Simulating epidemic transmission dynamics and associated public health policies ; Assisting with understanding the properties and efficacies of various public health policies; Constructing an effective, low-cost, and executable suite of epidemic prevention strategies; Reducing the difficulties and costs associated with learning epidemiological issues.

5/25 Epidemic Simulations Factors that influence the epidemic transmission dynamics Epidemiological Progress Incubation, Infectious, Recovered, and Immune Days, … Individual Diversity Super-spreader, Inoculator, Immune, Weak Individual… Social Networks Interpersonal Relationships and Simple Daily Contact High Local Clustering and Small-World Phenomena Mobile Individual Problems Short- and Long-Distance Movement Daily visits to fixed and/or multiple locations Public Health Policies and Strategies Factors of Individual Diversity Epidemiological Factors Factors of Social Networks

6/25 Cellular Automata with Social Mirror Identity Model Epidemic Disease Agent Population Mirror Identity Concept Social Contact Network

7/25 Simulation Framework Epidemic Disease Data from WHO, CDC, and individual national health authorities Time points of imported cases and public health policies Interaction rules; population, network, and epidemic parameters CASMIM Input Initialize Output

8/25 CASMIM Simulation System CASMIM

9/25 Statistical Analyses for Simulating SARS Reliability Test Chi-square test Validity Test Correlation coefficient (CC)  [-1, 1] Coefficient of efficiency (CE)  [0, 1] Mean square error (MSE)  [0,  ] Mean absolute error (MAE)  [0,  ]

10/25 Singapore SARS Outbreak 4/28 taking body temperature at transportation gates 3/24 stop visits to hospital & home quarantine 3/27 stop class of junior and elementary school 3/30 restriction on air passenger

11/25 Taipei SARS Outbreak 6/1 taking body temperature 3/26 wearing mask by healthcare workers 3/28 home quarantine 3/30 wearing mask by general public 4/10 taking passenger temperature at airport

12/25 Toronto SARS outbreak 3/26 Stop visits to hospital & Home quarantine

13/25 Taking Body Temperature

14/25 Wearing Masks for General Public

15/25 Wearing Masks for Health Workers

16/25 Assessing Public Health Suites 3/24 Executing public health suites

Conclusions (cont.) A novel and complete small-world epidemic model Simulating epidemic transmission dynamics and associated public health policies ; Assisting with understanding the properties and efficacies of various public health policies; Constructing an effective, low-cost, and executable suite of epidemic prevention strategies. Reducing the difficulties and costs associated with learning epidemiological concepts.