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

1. Pandemic Influenza Planning FluPredict:A real-time prediction model for aid in hospital-level pandemic influenza planning Jonathan Wang Professor Michael Carter April 15, 2011

2. A little about me... BASc in Engineering Science at University of Toronto Specializing in Biomedical Engineering Currently, MASc in Industrial Engineering at University of Toronto Specializing in Healthcare Operations CHL5425 with Professor Fisman on mathematical epidemiology last semester

4. Current Research

5. The FluSurge Model

6. Methods

7. The FluPredict Model

8. SEIAR variation of Model

9. Results

10. Model Validation

11. Future Work

12. Model Enhancements

13. Benefits of the Model

14. Conclusion

16. Three occurrences in the 20th century

17. 1918:

18. Mortality of 50-100 million people worldwide

19. 1957, 1968:

20. Mortality of 1-6 million people worldwide [1]

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]Background| Current Research | Methods | Results | Future Work | Conclusion [1] Skowronski, Danuta; Kendall, Perry. Pandemic Influenza--A primer for Physicians. BC Medical Journal. June 2007, Vol. 49, 5, pp. 236-239. [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

22. Impact of Pandemic Therefore, it is necessary to adequately plan for a pandemic to allocate resources effectively in a hospital setting Background| Current Research | Methods | Results | Future Work | Conclusion

23. Flusurge A model to estimate resource demand

25. Used in Australia [4] and recommended in the Ontario Health Plan for Influenza Pandemic [5]

26. Estimates the number of hospitalizations and deaths of an influenza pandemic

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.The FluSurge Model [3] Background |Current Research | Methods | Results | Future Work | Conclusion [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. [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. [5] http://www.health.gov.on.ca/english/providers/program/emu/pan_flu/pan_flu_plan.html

29. Demographic Population

30. Three age categories: 0-19, 20-64, 65+

31. Population for region the hospital services

32. Attack Rate

33. 15%, 25%, 35%

34. Duration of the Pandemic

35. 6, 8, 12 weeks

36. Outputs:

37. Hospital Admissions

38. Number of Deaths

39. Associated with influenza

40. A spread of hospital admissions over the estimated durationBackground |Current Research | Methods | Results | Future Work | Conclusion

42. No real-time prediction method

43. Number of hospitalizations modeled as triangular as opposed to a normal distribution

44. Not enough granularity in model parameters

45. Patient volume estimation based on historical data scaled to the inputted dataThe FluSurge Model Background |Current Research | Methods | Results | Future Work | Conclusion

46. Flupredict FluSurge with some additions

47. Goal: Dynamically predict the impact of pandemic influenza to hospital resources based on daily data inputs Outputs: Bed availabilities, ICU capacity and ventilator usage Inputs: Age Demographics, CDC FluSurge assumptions, surveillance tool data Introduction to FluPredict Background | Current Research |Methods| Results | Future Work | Conclusion

48. Difference Between Models FluSurge[6] FluPredict Background | Current Research |Methods| Results | Future Work | Conclusion [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.

50. Multiplicative factor that affects the height of the pandemic curve (i.e. Affects the number of people being infected)

51. Pandemic Length

52. Affects the width of the pandemic curve (i.e. Affects the overall duration of the pandemic)

53. So, how do we generate the curves?

54. Use a normal distribution.

55. Relate these parameters to the input parameters of a normal distributionTwo Important Parameters Background | Current Research |Methods| Results | Future Work | Conclusion

56. Pandemic Length For a given attack rate: 6 SD 3 SD SD = Duration/6 Mean = Duration/2 99.7% of all values lie within 3 standard deviations of the mean for normal distribution. Background | Current Research |Methods| Results | Future Work | Conclusion

57. Generated theoretical pandemic influenza curves (with different durations [1-12 weeks] and attack rates [1%-50%]) Can generate 1800 scenarios. But which scenario most accurately represents the data? Varying Pandemic Length Background | Current Research |Methods| Results | Future Work | Conclusion

58. The most likely scenario is defined by the duration and attack rate Q: How to determine these 2 variables? A: Fit the incoming ED (ILI) admission data to “standard” pandemic hospitalization curves Most likely Scenario Background | Current Research |Methods| Results | Future Work | Conclusion

59. Most likely Scenario- RMS Error Background | Current Research |Methods| Results | Future Work | Conclusion

60. Most likely Scenario- RMS Error Error = Difference between the curves Background | Current Research |Methods| Results | Future Work | Conclusion

61. To calculate the RMS error value: The RMS error is then compared against the RMS error for all other simulated curves The minimum RMS error indicates that the simulated curve is the best fit to the input data We then can attribute the attack rate and the duration of the best fit curve to the input data Most likely Scenario- RMS Error Background | Current Research |Methods| Results | Future Work | Conclusion

63. Each hospital has a pandemic response plan

64. Details how many beds to open up during the pandemic depending on various “triggers”

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 patientsImpact on Hospital Resources Background | Current Research |Methods| Results | Future Work | Conclusion

66. Impact on WOHC – Resource Model Background | Current Research |Methods| Results | Future Work | Conclusion

67. Flupredict SEIAR Adaptation

68. SEIAR Model Compartmental, epidemiological model Characteristic of influenza Background | Current Research |Methods| Results | Future Work | Conclusion

69. Graphical Representation Background | Current Research |Methods| Results | Future Work | Conclusion

71. Runge-Kutta 4th order method

72. Given the parameters, we can arrive at patient distributions Governing Equations Background | Current Research |Methods| Results | Future Work | Conclusion

74. Literature was used to set bounds on the parameters

75. For a given set of parameters, a set of curves were generated and compared to the real-time data of the hospitalParameterization Background | Current Research |Methods| Results | Future Work | Conclusion

76. Model demo A brief

77. Results A summary of the

79. 3 weeks of data from WOHC

80. Data was smoothed out with a moving average smoothing technique before input into model

81. Result:

82. Able to predict a 11 week surge with the given dataModel Validation - FluPredict Background | Current Research | Methods |Results| Future Work | Conclusion

83. Model Validation - FluPredict Background | Current Research | Methods |Results| Future Work | Conclusion

84. Model Validation - FluPredict Background | Current Research | Methods |Results| Future Work | Conclusion Prediction given 3 weeks of data input: 11 weeks with 10% attack rate

85. Model Validation – FluPredict Background | Current Research | Methods |Results| Future Work | Conclusion Prediction: 11 weeks with 10% attack rate

87. Tendency to pinpoint local surges as opposed to global surges in the data

88. This can be remedied by applying a smoothing function to the data to eliminate unnecessary bumps in the data

89. NOTE: The prediction is only an estimate of what may happenLimitations to the Prediction Background | Current Research | Methods |Results| Future Work | Conclusion

90. Results from FluPredict Background | Current Research | Methods |Results| Future Work | Conclusion

91. Results from SEIAR model Background | Current Research | Methods |Results| Future Work | Conclusion

92. Future work Enhancements to the models

94. Calculation of appropriate start date for pandemic influenza

95. Further validation of the model in different regions

96. Regional, provincial, national data

97. Investigate more robust prediction methods

98. What is the minimum # of points required to predict accurately?

99. Leverage data from multiple hospitals would be helpfulBackground | Current Research | Methods | Results |Future Work | Conclusion

101. Extend into modeling of impact of staff

102. Staff absenteeism/illness

103. Impact to key staff ratios

104. Increase granularity into non-ICU bed availabilities

105. Take into account current pandemic plans, ward structure for beds, existing patients within WOHC systemModel Enhancements Background | Current Research | Methods | Results |Future Work | Conclusion

106. Leveraged hospital’s surveillance tool and pandemic preparedness plan An estimate of the duration and attack rate (or epidemiological properties) of the pandemic based on real-time surveillance data Ability to explore various scenarios to see the impact a pandemic will have on hospital’s resources The planning of procedures easier if pandemic length is able to be estimated (elective surgeries) Benefits to Hospitals Background | Current Research | Methods | Results | Future Work |Conclusion

108. A Prediction Model

109. A Resource Allocation Model

110. Hope to augment current research conducted in this field

111. Continual refinement of CDC’s FluSurge model

112. Further refinement and data validation of our model is required

113. At this stage, the FluPredict framework has shown promising preliminary resultsConclusion – In the Larger Picture Background | Current Research | Methods | Results | Future Work |Conclusion

114. Questions? Comments?