Scott 校外口試

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Scott 校外口試

  1. 1. 小世界流行病學建模與公共衛生政策評估 利用社會分身點概念與區域資訊 建構社會網路式流行病學電腦模擬 Small-World Epidemiological Modeling and Public Health Policy Assessment Using the Social Mirror Identities Concept and Local Information for Network-based Epidemic Simulations 交通大學 資訊工程所 博士學位校外口試 學生:黃崇源 指導老師:孫春在教授 2005.11.11
  2. 2. Outline Related Works SIR Models, Simple Networks, SmallWorld Networks, … CASMIM Epidemic Simulations Factors of Individual Diversity Epidemiological Factors Factors of Social Networks
  3. 3. Related Works SIR Models, Simple Networks, Small-World Networks, … CASMIM Epidemic Simulations Factors of Individual Diversity Epidemiological Factors Factors of Social Networks
  4. 4. If Avian Flu Strikes Taiwan … <ul><li>Confirm viral structure. </li></ul><ul><li>Develop vaccines and antidotes. </li></ul><ul><li>Establish faster inspection methods. </li></ul><ul><li>Revise public health policies and prevention strategies. </li></ul>TOO SLOW!
  5. 5. A Suitable Epidemic Simulations <ul><li>Simulating epidemic transmission dynamics and associated public health policies ; </li></ul><ul><li>Assisting with understanding the properties and efficacies of various public health policies; </li></ul><ul><li>Constructing an effective , low-cost , and executable suite of epidemic prevention strategies; </li></ul><ul><li>Reducing the difficulties and costs associated with learning epidemiological concepts. </li></ul>
  6. 6. Epidemic Problems and Simulations <ul><li>Factors that influence the epidemic transmission dynamics </li></ul><ul><ul><li>Epidemiological Progress </li></ul></ul><ul><ul><ul><li>Incubation, Infectious, Recovered, and Immune Days, … </li></ul></ul></ul><ul><ul><li>Individual Diversity </li></ul></ul><ul><ul><ul><li>Super-spreader, Inoculator, Immune, Weak Individual… </li></ul></ul></ul><ul><ul><li>Social Networks </li></ul></ul><ul><ul><ul><li>Interpersonal Relationships and Simple Daily Contact </li></ul></ul></ul><ul><ul><ul><li>High Clustering and Small-World Phenomena </li></ul></ul></ul><ul><ul><li>Mobile Individual Problems </li></ul></ul><ul><ul><ul><li>Short- and Long-Distance Movement </li></ul></ul></ul><ul><ul><ul><li>Daily visits to fixed and/or multiple locations </li></ul></ul></ul><ul><ul><li>Public Health Policies and Strategies </li></ul></ul>Factors of Individual Diversity Epidemiological Factors Factors of Social Networks Complex Systems Susceptible ( S ) Incubation ( N ) Infected ( I ) Dead ( D ) Recovered ( R ) Immune ( M )
  7. 7. <ul><li>Some factors cannot be handled by traditional epidemic simulations. </li></ul><ul><li>Traditional Epidemic Simulations </li></ul><ul><ul><li>Population-based </li></ul></ul><ul><ul><ul><li>SIR Models </li></ul></ul></ul><ul><ul><li>Agent-based? </li></ul></ul><ul><ul><li>Network-based </li></ul></ul><ul><ul><ul><li>Simple Network Models </li></ul></ul></ul><ul><ul><ul><li>Small-World Network Models </li></ul></ul></ul>Epidemic Simulations (cont.) Factors of Individual Diversity Epidemiological Factors Factors of Social Networks
  8. 8. Related Works SIR Models, Simple Networks, Small-World Networks, … CASMIM Epidemic Simulations Factors of Individual Diversity Epidemiological Factors Factors of Social Networks
  9. 9. Population-based Epidemic Simulations <ul><li>SIR Models </li></ul><ul><ul><li>Estimate epidemic transmission dynamics and trends. </li></ul></ul><ul><ul><li>Proposed by Kermack and McKendrick in 1927. </li></ul></ul><ul><ul><li>S usceptible, I nfected, R emoved </li></ul></ul><ul><ul><li>Well-mixed hypothesis </li></ul></ul>
  10. 10. Population-based Epidemic Simulations <ul><li>Disadvantages </li></ul><ul><ul><li>Oversimplified </li></ul></ul><ul><ul><ul><li>Emphasizing epidemiological factors. </li></ul></ul></ul><ul><ul><li>Lack of features </li></ul></ul><ul><ul><ul><li>Social Networks </li></ul></ul></ul><ul><ul><ul><li>Individual Diversity </li></ul></ul></ul><ul><ul><ul><li>Mobile Individual Problems </li></ul></ul></ul><ul><ul><li>Insufficient for analyzing public health policies. </li></ul></ul>Factors of Individual Diversity Epidemiological Factors Factors of Social Networks
  11. 11. Simple Network-based Epidemic Simulations <ul><li>Node  Individual with attributes </li></ul><ul><li>Edge  Interpersonal relationships </li></ul><ul><li>Regular Networks </li></ul><ul><ul><li>One-dimensional ring-shaped lattices </li></ul></ul><ul><ul><li>Cellular automata with Moore neighborhood </li></ul></ul><ul><ul><li>High local clustering property </li></ul></ul><ul><li>Random Networks </li></ul><ul><ul><li>Proposed by Erdös and Renyi in 1959. </li></ul></ul><ul><ul><li>Low degree of separation property </li></ul></ul>
  12. 12. Simple Network-based Epidemic Simulations <ul><li>Disadvantages </li></ul><ul><ul><li>Limited Utility </li></ul></ul><ul><ul><li>Lack of important features </li></ul></ul><ul><ul><ul><li>Low Degree of Separation </li></ul></ul></ul><ul><ul><ul><li>High Local Clustering </li></ul></ul></ul><ul><ul><ul><li>Mobile Individual Problems </li></ul></ul></ul><ul><ul><li>Insufficient </li></ul></ul><ul><ul><ul><li>Simulating real epidemic transmission dynamics </li></ul></ul></ul><ul><ul><ul><li>Analyzing public health policies. </li></ul></ul></ul>Factors of Individual Diversity Epidemiological Factors Factors of Social Networks
  13. 13. <ul><li>Six degrees of separation (Milgram 1967) </li></ul><ul><ul><li>Human frequently interact with each other and form groups. </li></ul></ul><ul><ul><li>Everybody in the world remains separated by six people. </li></ul></ul><ul><ul><li>Verified by Watts and Strogatz in 1998. </li></ul></ul><ul><ul><li>Small-world networks are ubiquitous in the real world. </li></ul></ul><ul><ul><ul><li>High Local Clustering and Low Degree of Separation </li></ul></ul></ul><ul><ul><ul><li>Average degree of separation increases logarithmically . </li></ul></ul></ul><ul><ul><li>Strongly influence epidemic transmission dynamics. </li></ul></ul><ul><ul><li>Considered an abstract social network model for epidemic simulations. </li></ul></ul>Small-World Network Epidemic Simulations
  14. 14. Small-World Network Epidemic Simulations <ul><li>Disadvantages </li></ul><ul><ul><li>Partial explanations for the mobile individual problems. </li></ul></ul><ul><ul><ul><li>Short link  Short-Distance Contact </li></ul></ul></ul><ul><ul><ul><li>Long Link  Long-Distance Contact </li></ul></ul></ul><ul><ul><li>Too abstract , insufficient for analyzing public health policies. </li></ul></ul>Factors of Individual Diversity Epidemiological Factors Factors of Social Networks
  15. 15. Related Works SIR Models, Simple Networks, Small-World Networks, … CASMIM Epidemic Simulations Factors of Individual Diversity Epidemiological Factors Factors of Social Networks
  16. 16. CASMIM Social Mirror Identity Concept Sensitivity Analysis of Social Contact Network Simulating SARS and associated with Public Health Policies Sensitivity Analysis of Individual Diversity
  17. 17. <ul><li>Mobile Individual Problems </li></ul><ul><ul><li>Hard to control the movement of individuals </li></ul></ul><ul><ul><ul><li>Method, timing, direction, distance, ... </li></ul></ul></ul><ul><ul><li>Lifestyle in modern societies </li></ul></ul><ul><ul><ul><li>Short- and long-distance movement </li></ul></ul></ul><ul><ul><ul><li>Daily visits to fixed and/or multiple activity locations </li></ul></ul></ul><ul><ul><ul><li>High local clustering and Low degree of separation </li></ul></ul></ul><ul><li>Social Mirror Identity Concept </li></ul><ul><ul><li>Solution to the mobile individual problems </li></ul></ul><ul><ul><li>More complete and direct imitation of social phenomena </li></ul></ul>Social Mirror Identity Concept
  18. 18. CASMIM Social Mirror Identity Concept Sensitivity Analysis of Social Contact Network Simulating SARS and associated with Public Health Policies Sensitivity Analysis of Individual Diversity
  19. 19. Cellular Automata with Social Mirror Identity Model Epidemiological Factors Factors of Individual Diversity Factors of Social Networks Epidemic Disease Agent Population Mirror Identity Concept Social Contact Network
  20. 20. Parameters of CASMIM Social Contact Network Mirror Identity Concept Agent Population Epidemic CASMIM
  21. 21. <ul><li>Epidemiological Progress </li></ul><ul><li>Social Mobility </li></ul><ul><ul><li>Families </li></ul></ul><ul><ul><li>Dormitories </li></ul></ul><ul><ul><li>Hospitals, … </li></ul></ul><ul><li>Public Health Policies </li></ul><ul><ul><li>Mask policy—general public vs. healthcare workers </li></ul></ul><ul><ul><li>Taking body temperature </li></ul></ul><ul><ul><li>A/B class home quarantine </li></ul></ul><ul><ul><li>Controlling hospital access </li></ul></ul><ul><ul><li>Reducing public contact, … </li></ul></ul>Features of CASMIM Social Contact Network Mirror Identity Concept Agent Population Epidemic Andy Bob Cindy CASMIM
  22. 22. 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 Input Initialize Output CASMIM
  23. 23. CASMIM Simulation System CASMIM
  24. 24. CASMIM Simulation System (cont.) CASMIM
  25. 25. CASMIM Social Mirror Identity Concept Sensitivity Analysis of Social Contact Network Simulating SARS and associated with Public Health Policies Sensitivity Analysis of Individual Diversity
  26. 26. Sensitivity Analysis of Social Contact Network <ul><li>Whether CASMIM is </li></ul><ul><ul><li>A small-world network </li></ul></ul><ul><ul><ul><li>High local clustering and low degree of separation , </li></ul></ul></ul><ul><ul><li>A robust simulation model in which small-world property are not affected as long as social network parameters are set within reasonable ranges. </li></ul></ul><ul><ul><ul><li>Height  and width  of cellular automata </li></ul></ul></ul><ul><ul><ul><li>Total agent population  </li></ul></ul></ul><ul><ul><ul><li>Average number of an agent’s mirror identities  </li></ul></ul></ul>Social Contact Network Mirror Identity Concept Agent Population Epidemic CASMIM
  27. 27. Sensitivity Analysis Experiment 1 <ul><li>Focused on the relationship between total agent population and degree of separation . </li></ul><ul><li>Experiment Settings </li></ul><ul><ul><li>Maintain a fixed average number of agent mirror identities </li></ul></ul><ul><ul><li>Change the total agent population, from 2,000 to 200,000. </li></ul></ul><ul><li>Experimental Conclusions </li></ul><ul><ul><li>The lack of fluctuation in CASMIM’s small-world property is an indication of robustness for the total agent population parameter, even when its value changes. </li></ul></ul>A slow and logarithmic growth relationship between the total agent population and average degree of separation CASMIM
  28. 28. Sensitivity Analysis Experiment 2 <ul><li>Focused on the relationship between average number of agent’s mirror identities and degree of separation . </li></ul><ul><li>Experiment Settings </li></ul><ul><ul><li>Maintain a fixed population of 10,000 agents. </li></ul></ul><ul><ul><li>Manipulated the number of agent mirror identities at a rate of 2 n , with n = 0, 1, 2, 3, or 4. </li></ul></ul><ul><li>Experimental Conclusions </li></ul><ul><ul><li>The average number of agent mirror identities is a robust parameter; as long as it remains within a reasonable range, small-world property is not influenced by a change in value. </li></ul></ul>Small-World Networks CASMIM
  29. 29. CASMIM Social Mirror Identity Concept Sensitivity Analysis of Social Contact Network Simulating SARS and associated with Public Health Policies Sensitivity Analysis of Individual Diversity
  30. 30. Sensitivity Analysis of Individual Diversity <ul><li>Build a better artificial society </li></ul><ul><ul><li>CASMIM is similar to human social networks. </li></ul></ul><ul><ul><li>Applying local information mechanisms is an approach to approximate to real world. </li></ul></ul><ul><ul><ul><li>Individual characteristics, landscape properties, … </li></ul></ul></ul><ul><li>Experimental focus </li></ul><ul><ul><li>Influence of individual diversity and distribution mechanisms on network-based epidemic simulation. </li></ul></ul><ul><ul><li>Determine the appropriate values of agents and social mirror identities attributes for CASMIM. </li></ul></ul>Social Contact Network Mirror Identity Concept Agent Population Epidemic CASMIM
  31. 31. <ul><li>Distinguishes certain individuals from others. </li></ul><ul><ul><li>Divergence is expressed as attribute information </li></ul></ul><ul><ul><ul><li>Individual and social mirror identities </li></ul></ul></ul><ul><ul><li>Infection strength and resistance level to disease </li></ul></ul><ul><li>Distribution mechanism </li></ul><ul><ul><li>Designed for setting the most appropriate local information </li></ul></ul><ul><ul><li>Random numbers vs. constant mode </li></ul></ul><ul><ul><ul><li>Normal and uniform distributions </li></ul></ul></ul><ul><ul><li>Pre-designed pattern </li></ul></ul><ul><ul><ul><li>Specific region, concentrated location, entire environment, … </li></ul></ul></ul>Local Information & Distribution Mechanisms 上面圖表顯示,在新加坡的 SARS 超級傳染事件鏈中, 個體 1 、 6 、 35 、 127 是所謂的超級傳染者。
  32. 32. Sensitivity Analysis of Neighborhood Type <ul><li>Identify the influence of neighborhood type of agents’ social mirror identities on epidemic simulations. </li></ul><ul><ul><li>Moore and von Neumann neighborhood type </li></ul></ul><ul><li>Experiment Settings </li></ul><ul><ul><li>Normal distribution (Mean = 8, Standard Deviation = 2) </li></ul></ul><ul><ul><li>Uniform distribution (4, 8, 12) </li></ul></ul><ul><ul><li>Constant value (8) </li></ul></ul>CASMIM CASMIM
  33. 33. Conclusions of Experiment 1 <ul><li>As long as small-world property exist and the average degree of separation does not change, variation in the detailed neighbor numbers exerts only a slight influence on the epidemic transmission dynamics in CASMIM. </li></ul><ul><ul><li>Neighborhood type is a insensitive parameter </li></ul></ul><ul><ul><li>Small-world property are viewed as key factors </li></ul></ul><ul><li>When using CASMIM to simulating epidemic </li></ul><ul><ul><li>Simple arrangements can be applied for purposes of efficiency and convenience. (e.g., von Neumann type) </li></ul></ul>
  34. 34. Sensitivity Analysis of Weak Individuals <ul><li>Identify the influence of weak individuals, who constitute a certain percentage of the total population. </li></ul><ul><ul><li>Individuals have different levels of resistance to diseases. </li></ul></ul><ul><ul><li>Weak individuals have double chance becoming infected. </li></ul></ul><ul><li>Experiment Settings </li></ul><ul><ul><li>Six simulation with different percentages of weak individuals: 0%, 1%, 5%, 10%, 30%, 50% </li></ul></ul>CASMIM
  35. 35. Conclusions of Experiment 2 <ul><li>The percentage and resistance of weak individuals exerted a significant influence on the epidemic transmission dynamics in CASMIM when small-world property exist and the average degree of separation does not change. </li></ul><ul><ul><li>Weak individual proportions and resistance are two pivotal factors. </li></ul></ul><ul><ul><li>These results underscore the importance of being precise when setting individual Infection strength and resistance level for network-based epidemic simulations. </li></ul></ul>
  36. 36. Sensitivity Analysis of Distribution Patterns <ul><li>Identify the influence of the same number of weak individuals on CASMIM under different distribution setting. </li></ul><ul><li>Experiment Settings </li></ul><ul><ul><li>Set the radius r of the area in which the weak individuals were distributed: 0(0.1), 0.2, 0.4, 0.6, 0.8, and 1.0 </li></ul></ul>CASMIM
  37. 37. Conclusions of Experiment 3 <ul><li>Distribution patterns is a insensitive parameter . </li></ul><ul><li>Example </li></ul><ul><ul><li>Opinion leaders who are capable of conveying information to large number of individuals and therefore affect their behavior, </li></ul></ul><ul><ul><li>A much smaller number of indecisive individuals who regularly alter their opinions according to suggestions made by friends and relatives. </li></ul></ul><ul><ul><li>Reliable epidemic simulations require data on the percentages of opinion leaders and indecisive individuals, but on their distribution patterns . </li></ul></ul>
  38. 38. Analysis Conclusions of Individual Diversity <ul><li>Useful sensitivity analysis experiments for determining individual attributes of network-based epidemic simulation </li></ul><ul><ul><li>Insignificance </li></ul></ul><ul><ul><ul><li>Neighborhood type of agents’ social mirror identities </li></ul></ul></ul><ul><ul><ul><li>Weak individual distribution patterns </li></ul></ul></ul><ul><ul><li>Significance </li></ul></ul><ul><ul><ul><li>Weak individual percentages and resistance </li></ul></ul></ul><ul><ul><li>Emphasis </li></ul></ul><ul><ul><ul><li>Global variables related to network properties </li></ul></ul></ul>Social Contact Network Mirror Identity Concept Agent Population Epidemic
  39. 39. <ul><li>Simulating epidemic dynamics; </li></ul><ul><li>Assisting the properties and efficacies </li></ul><ul><li>of various public health policies; </li></ul><ul><li>Constructing an effective, low-cost, and executable suite of epidemic prevention strategies. </li></ul>CASMIM Social Mirror Identity Concept Sensitivity Analysis of Social Contact Network Simulating SARS and associated with Public Health Policies Sensitivity Analysis of Individual Diversity Simulating SARS Assessing Public Health Policies Assessing Public Health Suites
  40. 40. Statistical Analyses for Simulating SARS <ul><li>Reliability Test </li></ul><ul><ul><li>Chi-square test </li></ul></ul><ul><li>Validity Test </li></ul><ul><ul><li>Correlation coefficient (CC)  [-1, 1] </li></ul></ul><ul><ul><li>Coefficient of efficiency (CE)  [0, 1] </li></ul></ul><ul><ul><li>Mean square error (MSE)  [0,  ] </li></ul></ul><ul><ul><li>Mean absolute error (MAE)  [0,  ] </li></ul></ul>
  41. 41. Singapore SARS Outbreak
  42. 42. Taipei SARS Outbreak (1)(2) (3) (4) (5) (1) 和平醫院事件 (2) 仁濟醫院事件 (3) 臺大醫院事件 (4) 關渡醫院事件 (5) 陽明醫院事件
  43. 43. Toronto SARS outbreak
  44. 44. Taking Body Temperature
  45. 45. Wearing Masks for General Public
  46. 46. Wearing Masks for Health Workers
  47. 47. Assessing Public Health Suites 3/24 實施防疫策略
  48. 48. Conclusion Related Works SIR Models, Simple Networks, Small-World Networks, … CASMIM Epidemic Simulations Factors of Individual Diversity Epidemiological Factors Factor of Social Networks
  49. 49. Conclusions <ul><li>A novel and complete small-world epidemic model </li></ul><ul><ul><li>Social mirror identity concept </li></ul></ul><ul><ul><ul><li>Short- and long-distance movement </li></ul></ul></ul><ul><ul><ul><li>Daily visits to fixed and/or multiple activity locations </li></ul></ul></ul><ul><ul><li>Cellular automata with social mirror identity model </li></ul></ul><ul><ul><ul><li>High local clustering and small-world properties </li></ul></ul></ul><ul><ul><ul><li>Represent daily-contact social networks </li></ul></ul></ul><ul><ul><ul><li>Solve mobile individual problems. </li></ul></ul></ul>Factors of Individual Diversity Epidemiological Factors Factor of Social Networks Social Contact Network Mirror Identity Concept Agent Population Epidemic
  50. 50. Conclusions (cont.) <ul><li>A novel and complete small-world epidemic model </li></ul><ul><ul><li>Simulating epidemic transmission dynamics and associated public health policies ; </li></ul></ul><ul><ul><li>Assisting with understanding the properties and efficacies of various public health policies; </li></ul></ul><ul><ul><li>Constructing an effective , low-cost , and executable suite of epidemic prevention strategies. </li></ul></ul><ul><ul><li>Reducing the difficulties and costs associated with learning epidemiological concepts. </li></ul></ul>Simulating SARS Assessing Public Health Policies Constructing Public Health Suites
  51. 51. Q&A <ul><li>Thank you for your attention </li></ul>

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