Idea Seminar Apr 15 2011

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Pandemic Influenza Modelling Presentation given at the IDEA seminar at the Fields Institute

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  • Conclusion: reflect on meaning and significance of thesis workNeed to change
  • Cut to the model! End on the “Data Input” Tab
  • Removed the “Currently: examining ED admissions (with symptoms of ILI)
  • Change to diagrams instead.
  • Change to diagrams instead.
  • Idea Seminar Apr 15 2011

    1. 1. Pandemic Influenza Planning<br />FluPredict:A real-time prediction model for aid in hospital-level pandemic influenza planning<br />Jonathan Wang <br />Professor Michael Carter<br />April 15, 2011<br />
    2. 2. A little about me...<br />BASc in Engineering Science at University of Toronto<br />Specializing in Biomedical Engineering<br />Currently, MASc in Industrial Engineering at University of Toronto<br />Specializing in Healthcare Operations<br />CHL5425 with Professor Fisman on mathematical epidemiology last semester<br />
    3. 3. Agenda<br /><ul><li>Background
    4. 4. Current Research
    5. 5. The FluSurge Model
    6. 6. Methods
    7. 7. The FluPredict Model
    8. 8. SEIAR variation of Model
    9. 9. Results
    10. 10. Model Validation
    11. 11. Future Work
    12. 12. Model Enhancements
    13. 13. Benefits of the Model
    14. 14. Conclusion
    15. 15. In the Larger Picture</li></li></ul><li>Pandemic Influenza<br /><ul><li>Infrequent but deadly and infectious strain of the influenza virus
    16. 16. Three occurrences in the 20th century
    17. 17. 1918:
    18. 18. Mortality of 50-100 million people worldwide
    19. 19. 1957, 1968:
    20. 20. Mortality of 1-6 million people worldwide [1]
    21. 21. For the Pandemic H1N1 virus in 2009, there have been a total of 8,678 hospitalized cases in Canada with 428 deaths since the beginning of the pandemic [2]</li></ul>Background| Current Research | Methods | Results | Future Work | Conclusion<br />[1] Skowronski, Danuta; Kendall, Perry. Pandemic Influenza--A primer for Physicians. BC Medical Journal. June 2007, Vol. 49, 5, pp. 236-239.<br />[2] Health Canada.FluWatch. [Online] April 24, 2010. [Cited: July 26, 2010] http://www.phac-aspc.gc.ca/fluwatch/09-10/w16_10/index-eng.php<br />
    22. 22. Impact of Pandemic<br /> Therefore, it is necessary to adequately plan for a pandemic to allocate resources effectively in a hospital setting<br />Background| Current Research | Methods | Results | Future Work | Conclusion<br />
    23. 23. Flusurge<br />A model to estimate resource demand<br />
    24. 24. <ul><li>Well recognized model for estimating demand in hospital settings
    25. 25. Used in Australia [4] and recommended in the Ontario Health Plan for Influenza Pandemic [5]
    26. 26. Estimates the number of hospitalizations and deaths of an influenza pandemic
    27. 27. Compares the number of persons hospitalized, the number of persons requiring ICU care, and the number of persons requiring ventilator support during a pandemic with existing hospital capacity.</li></ul>The FluSurge Model [3]<br />Background |Current Research | Methods | Results | Future Work | Conclusion<br />[3] Zhang, Xinzhi, Meltzer, Martin I. and Wortley, Pascale M.FluSurge - A Tool to Estimate Demand for Hospital Services during the Next Pandemic Influenza. Medical Decision Making. Nov-Dec, 2006, Vol. 26, 617.<br />[4]Lum ME, McMillan AJ, Brook CW, et al. Impact of pandemic (H1N1) 2009 influenza on critical care capacity in Victoria. [28 September 2009] Med J Aust. eMJA rapid online publication.<br />[5] http://www.health.gov.on.ca/english/providers/program/emu/pan_flu/pan_flu_plan.html<br />
    28. 28. The FluSurge Model<br /><ul><li>Inputs:
    29. 29. Demographic Population
    30. 30. Three age categories: 0-19, 20-64, 65+
    31. 31. Population for region the hospital services
    32. 32. Attack Rate
    33. 33. 15%, 25%, 35%
    34. 34. Duration of the Pandemic
    35. 35. 6, 8, 12 weeks
    36. 36. Outputs:
    37. 37. Hospital Admissions
    38. 38. Number of Deaths
    39. 39. Associated with influenza
    40. 40. A spread of hospital admissions over the estimated duration</li></ul>Background |Current Research | Methods | Results | Future Work | Conclusion<br />
    41. 41. <ul><li>Limitations/Research Gap:
    42. 42. No real-time prediction method
    43. 43. Number of hospitalizations modeled as triangular as opposed to a normal distribution
    44. 44. Not enough granularity in model parameters
    45. 45. Patient volume estimation based on historical data scaled to the inputted data</li></ul>The FluSurge Model<br />Background |Current Research | Methods | Results | Future Work | Conclusion<br />
    46. 46. Flupredict<br />FluSurge with some additions<br />
    47. 47. Goal: Dynamically predict the impact of pandemic influenza to hospital resources based on daily data inputs<br />Outputs: Bed availabilities, ICU capacity and ventilator usage<br />Inputs: Age Demographics, CDC FluSurge assumptions, surveillance tool data<br />Introduction to FluPredict<br />Background | Current Research |Methods| Results | Future Work | Conclusion<br />
    48. 48. Difference Between Models<br />FluSurge[6] FluPredict<br />Background | Current Research |Methods| Results | Future Work | Conclusion<br />[6] Meltzer, M et al. The Economic Impact of Pandemic Influenza in the United States: Priorities for Intervention. Emerging Infectious Diseases. Oct, 1999, Vol. 5, 5.<br />
    49. 49. <ul><li>Attack Rate
    50. 50. Multiplicative factor that affects the height of the pandemic curve (i.e. Affects the number of people being infected)
    51. 51. Pandemic Length
    52. 52. Affects the width of the pandemic curve (i.e. Affects the overall duration of the pandemic)
    53. 53. So, how do we generate the curves?
    54. 54. Use a normal distribution.
    55. 55. Relate these parameters to the input parameters of a normal distribution</li></ul>Two Important Parameters<br />Background | Current Research |Methods| Results | Future Work | Conclusion<br />
    56. 56. Pandemic Length<br />For a given attack rate:<br />6 SD<br />3 SD<br />SD = Duration/6<br />Mean = Duration/2<br />99.7% of all values lie within 3 standard deviations of the mean for normal distribution.<br />Background | Current Research |Methods| Results | Future Work | Conclusion<br />
    57. 57. Generated theoretical pandemic influenza curves (with different durations [1-12 weeks] and attack rates [1%-50%])<br />Can generate 1800 scenarios. <br />But which scenario most accurately represents the data?<br />Varying Pandemic Length<br />Background | Current Research |Methods| Results | Future Work | Conclusion<br />
    58. 58. The most likely scenario is defined by the duration and attack rate<br />Q: How to determine these 2 variables?<br />A: Fit the incoming ED (ILI) admission data to “standard” pandemic hospitalization curves<br />Most likely Scenario<br />Background | Current Research |Methods| Results | Future Work | Conclusion<br />
    59. 59. Most likely Scenario- RMS Error<br />Background | Current Research |Methods| Results | Future Work | Conclusion<br />
    60. 60. Most likely Scenario- RMS Error<br />Error = Difference between the curves<br />Background | Current Research |Methods| Results | Future Work | Conclusion<br />
    61. 61. To calculate the RMS error value:<br />The RMS error is then compared against the RMS error for all other simulated curves<br />The minimum RMS error indicates that the simulated curve is the best fit to the input data<br />We then can attribute the attack rate and the duration of the best fit curve to the input data<br />Most likely Scenario- RMS Error<br />Background | Current Research |Methods| Results | Future Work | Conclusion<br />
    62. 62. <ul><li>Use the most-likely scenario prediction to see how a pandemic of the predicted magnitude will affect the hospital
    63. 63. Each hospital has a pandemic response plan
    64. 64. Details how many beds to open up during the pandemic depending on various “triggers”
    65. 65. “Triggers” = a pre-specified percentage of ED admissions after which the hospital will open a preset number of beds to increase capacity for incoming ILI patients</li></ul>Impact on Hospital Resources<br />Background | Current Research |Methods| Results | Future Work | Conclusion<br />
    66. 66. Impact on WOHC – Resource Model<br />Background | Current Research |Methods| Results | Future Work | Conclusion<br />
    67. 67. Flupredict<br />SEIAR Adaptation<br />
    68. 68. SEIAR Model<br />Compartmental, epidemiological model<br />Characteristic of influenza <br />Background | Current Research |Methods| Results | Future Work | Conclusion<br />
    69. 69. Graphical Representation<br />Background | Current Research |Methods| Results | Future Work | Conclusion<br />
    70. 70. <ul><li>Custom-built differential equation solver in Excel
    71. 71. Runge-Kutta 4th order method
    72. 72. Given the parameters, we can arrive at patient distributions </li></ul>Governing Equations<br />Background | Current Research |Methods| Results | Future Work | Conclusion<br />
    73. 73. <ul><li>Parameters estimated using RMS error minimization
    74. 74. Literature was used to set bounds on the parameters
    75. 75. For a given set of parameters, a set of curves were generated and compared to the real-time data of the hospital</li></ul>Parameterization<br />Background | Current Research |Methods| Results | Future Work | Conclusion<br />
    76. 76. Model demo<br />A brief<br />
    77. 77. Results<br />A summary of the <br />
    78. 78. <ul><li>Input:
    79. 79. 3 weeks of data from WOHC
    80. 80. Data was smoothed out with a moving average smoothing technique before input into model
    81. 81. Result:
    82. 82. Able to predict a 11 week surge with the given data</li></ul>Model Validation - FluPredict<br />Background | Current Research | Methods |Results| Future Work | Conclusion<br />
    83. 83. Model Validation - FluPredict<br />Background | Current Research | Methods |Results| Future Work | Conclusion<br />
    84. 84. Model Validation - FluPredict<br />Background | Current Research | Methods |Results| Future Work | Conclusion<br />Prediction given 3 weeks of data input: 11 weeks with 10% attack rate<br />
    85. 85. Model Validation – FluPredict<br />Background | Current Research | Methods |Results| Future Work | Conclusion<br />Prediction: 11 weeks with 10% attack rate<br />
    86. 86. <ul><li>Constrained by the number of data points put into the model
    87. 87. Tendency to pinpoint local surges as opposed to global surges in the data
    88. 88. This can be remedied by applying a smoothing function to the data to eliminate unnecessary bumps in the data
    89. 89. NOTE: The prediction is only an estimate of what may happen</li></ul>Limitations to the Prediction<br />Background | Current Research | Methods |Results| Future Work | Conclusion<br />
    90. 90. Results from FluPredict<br />Background | Current Research | Methods |Results| Future Work | Conclusion<br />
    91. 91. Results from SEIAR model<br />Background | Current Research | Methods |Results| Future Work | Conclusion<br />
    92. 92. Future work<br />Enhancements to the models<br />
    93. 93. Model Enhancements<br /><ul><li>Prediction part of the model
    94. 94. Calculation of appropriate start date for pandemic influenza
    95. 95. Further validation of the model in different regions
    96. 96. Regional, provincial, national data
    97. 97. Investigate more robust prediction methods
    98. 98. What is the minimum # of points required to predict accurately?
    99. 99. Leverage data from multiple hospitals would be helpful</li></ul>Background | Current Research | Methods | Results |Future Work | Conclusion<br />
    100. 100. <ul><li>Resource Allocation part of the model
    101. 101. Extend into modeling of impact of staff
    102. 102. Staff absenteeism/illness
    103. 103. Impact to key staff ratios
    104. 104. Increase granularity into non-ICU bed availabilities
    105. 105. Take into account current pandemic plans, ward structure for beds, existing patients within WOHC system</li></ul>Model Enhancements<br />Background | Current Research | Methods | Results |Future Work | Conclusion<br />
    106. 106. Leveraged hospital’s surveillance tool and pandemic preparedness plan<br />An estimate of the duration and attack rate (or epidemiological properties) of the pandemic based on real-time surveillance data<br />Ability to explore various scenarios to see the impact a pandemic will have on hospital’s resources<br />The planning of procedures easier if pandemic length is able to be estimated (elective surgeries)<br />Benefits to Hospitals<br />Background | Current Research | Methods | Results | Future Work |Conclusion<br />
    107. 107. <ul><li>This research is a combination of:
    108. 108. A Prediction Model
    109. 109. A Resource Allocation Model
    110. 110. Hope to augment current research conducted in this field
    111. 111. Continual refinement of CDC’s FluSurge model
    112. 112. Further refinement and data validation of our model is required
    113. 113. At this stage, the FluPredict framework has shown promising preliminary results</li></ul>Conclusion – In the Larger Picture<br />Background | Current Research | Methods | Results | Future Work |Conclusion<br />
    114. 114. Questions?<br />Comments? <br />

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