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Idea Seminar Apr 15 2011
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Idea Seminar Apr 15 2011

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

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 Idea Seminar Apr 15 2011 Presentation Transcript

  • 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
  • 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
  • Agenda
    • Background View slide
    • Current Research View slide
    • The FluSurge Model
    • Methods
    • The FluPredict Model
    • SEIAR variation of Model
    • Results
    • Model Validation
    • Future Work
    • Model Enhancements
    • Benefits of the Model
    • Conclusion
    • In the Larger Picture
  • Pandemic Influenza
    • Infrequent but deadly and infectious strain of the influenza virus
    • Three occurrences in the 20th century
    • 1918:
    • Mortality of 50-100 million people worldwide
    • 1957, 1968:
    • Mortality of 1-6 million people worldwide [1]
    • 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
  • 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
  • Flusurge
    A model to estimate resource demand
    • Well recognized model for estimating demand in hospital settings
    • Used in Australia [4] and recommended in the Ontario Health Plan for Influenza Pandemic [5]
    • Estimates the number of hospitalizations and deaths of an influenza pandemic
    • 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
  • The FluSurge Model
    • Inputs:
    • Demographic Population
    • Three age categories: 0-19, 20-64, 65+
    • Population for region the hospital services
    • Attack Rate
    • 15%, 25%, 35%
    • Duration of the Pandemic
    • 6, 8, 12 weeks
    • Outputs:
    • Hospital Admissions
    • Number of Deaths
    • Associated with influenza
    • A spread of hospital admissions over the estimated duration
    Background |Current Research | Methods | Results | Future Work | Conclusion
    • Limitations/Research Gap:
    • No real-time prediction method
    • Number of hospitalizations modeled as triangular as opposed to a normal distribution
    • Not enough granularity in model parameters
    • Patient volume estimation based on historical data scaled to the inputted data
    The FluSurge Model
    Background |Current Research | Methods | Results | Future Work | Conclusion
  • Flupredict
    FluSurge with some additions
  • 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
  • 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.
    • Attack Rate
    • Multiplicative factor that affects the height of the pandemic curve (i.e. Affects the number of people being infected)
    • Pandemic Length
    • Affects the width of the pandemic curve (i.e. Affects the overall duration of the pandemic)
    • So, how do we generate the curves?
    • Use a normal distribution.
    • Relate these parameters to the input parameters of a normal distribution
    Two Important Parameters
    Background | Current Research |Methods| Results | Future Work | Conclusion
  • 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
  • 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
  • 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
  • Most likely Scenario- RMS Error
    Background | Current Research |Methods| Results | Future Work | Conclusion
  • Most likely Scenario- RMS Error
    Error = Difference between the curves
    Background | Current Research |Methods| Results | Future Work | Conclusion
  • 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
    • Use the most-likely scenario prediction to see how a pandemic of the predicted magnitude will affect the hospital
    • Each hospital has a pandemic response plan
    • Details how many beds to open up during the pandemic depending on various “triggers”
    • “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
    Impact on Hospital Resources
    Background | Current Research |Methods| Results | Future Work | Conclusion
  • Impact on WOHC – Resource Model
    Background | Current Research |Methods| Results | Future Work | Conclusion
  • Flupredict
    SEIAR Adaptation
  • SEIAR Model
    Compartmental, epidemiological model
    Characteristic of influenza
    Background | Current Research |Methods| Results | Future Work | Conclusion
  • Graphical Representation
    Background | Current Research |Methods| Results | Future Work | Conclusion
    • Custom-built differential equation solver in Excel
    • Runge-Kutta 4th order method
    • Given the parameters, we can arrive at patient distributions
    Governing Equations
    Background | Current Research |Methods| Results | Future Work | Conclusion
    • Parameters estimated using RMS error minimization
    • Literature was used to set bounds on the parameters
    • For a given set of parameters, a set of curves were generated and compared to the real-time data of the hospital
    Parameterization
    Background | Current Research |Methods| Results | Future Work | Conclusion
  • Model demo
    A brief
  • Results
    A summary of the
    • Input:
    • 3 weeks of data from WOHC
    • Data was smoothed out with a moving average smoothing technique before input into model
    • Result:
    • Able to predict a 11 week surge with the given data
    Model Validation - FluPredict
    Background | Current Research | Methods |Results| Future Work | Conclusion
  • Model Validation - FluPredict
    Background | Current Research | Methods |Results| Future Work | Conclusion
  • Model Validation - FluPredict
    Background | Current Research | Methods |Results| Future Work | Conclusion
    Prediction given 3 weeks of data input: 11 weeks with 10% attack rate
  • Model Validation – FluPredict
    Background | Current Research | Methods |Results| Future Work | Conclusion
    Prediction: 11 weeks with 10% attack rate
    • Constrained by the number of data points put into the model
    • Tendency to pinpoint local surges as opposed to global surges in the data
    • This can be remedied by applying a smoothing function to the data to eliminate unnecessary bumps in the data
    • NOTE: The prediction is only an estimate of what may happen
    Limitations to the Prediction
    Background | Current Research | Methods |Results| Future Work | Conclusion
  • Results from FluPredict
    Background | Current Research | Methods |Results| Future Work | Conclusion
  • Results from SEIAR model
    Background | Current Research | Methods |Results| Future Work | Conclusion
  • Future work
    Enhancements to the models
  • Model Enhancements
    • Prediction part of the model
    • Calculation of appropriate start date for pandemic influenza
    • Further validation of the model in different regions
    • Regional, provincial, national data
    • Investigate more robust prediction methods
    • What is the minimum # of points required to predict accurately?
    • Leverage data from multiple hospitals would be helpful
    Background | Current Research | Methods | Results |Future Work | Conclusion
    • Resource Allocation part of the model
    • Extend into modeling of impact of staff
    • Staff absenteeism/illness
    • Impact to key staff ratios
    • Increase granularity into non-ICU bed availabilities
    • Take into account current pandemic plans, ward structure for beds, existing patients within WOHC system
    Model Enhancements
    Background | Current Research | Methods | Results |Future Work | Conclusion
  • 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
    • This research is a combination of:
    • A Prediction Model
    • A Resource Allocation Model
    • Hope to augment current research conducted in this field
    • Continual refinement of CDC’s FluSurge model
    • Further refinement and data validation of our model is required
    • At this stage, the FluPredict framework has shown promising preliminary results
    Conclusion – In the Larger Picture
    Background | Current Research | Methods | Results | Future Work |Conclusion
  • Questions?
    Comments?