1 / 26

Don Burke, M.D. Dean, Graduate School of Public Health

Simulated epidemics to evaluate H1N1 pandemic preparedness strategies / MIDAS / ASPR-BARDA 9 October 2009. Don Burke, M.D. Dean, Graduate School of Public Health. Don Burke, PI Ron Vorhees , Allegh County Epidemiologist Rick Zimmerman, Community Health Physician

aerona
Download Presentation

Don Burke, M.D. Dean, Graduate School of Public Health

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Simulated epidemics to evaluate H1N1 pandemic preparedness strategies / MIDAS / ASPR-BARDA9 October 2009 Don Burke, M.D. Dean, Graduate School of Public Health

  2. Don Burke, PI Ron Vorhees, Allegh County Epidemiologist Rick Zimmerman, Community Health Physician John Grefenstette, Computer Scientist Cho-Cho Lin, Economist Sandra Quinn, Behavioral Scientist Jim Stark, Epidemiol grad student Shanta Zimmer, InfDis Physician Shawn Brown, Computer Scientist Roni Rosenfeld, Computer Scientist Maggie Potter, Lawyer & Public Health Practice Bruce Lee, Int Med Physician & Operations Rsch

  3. Allegheny County Pennsylvania Model Parameters Washington DC Metro Region • Total Population = 7,414,562 • Workers = 3,714,125 • Firms = 204,691 • Students = 1,369,980 • Schools = 2,443 Total Population = 1,242,755 Workers = 601,022 Firms = 48,595 Students = 212,315 Schools = 484 Total Population = 11,863,395 Workers = 5,391,651 Firms = 312,473 Students = 2,176,168 Schools = 4,319

  4. Location of influenza transmission Communities Workplaces 33% 37% • 16% infections in schools • 21% infections in workplaces Schools (Applies to an epidemic R0 = 1.9) 30% Homes

  5. Allegheny County Visualization

  6. Pennsylvania Visualization

  7. Washington DC Metro Visualization

  8. No Adjuvant Scenario

  9. Attached are two visualizations.  They are both performed with the DC Metro model. 1)      Oct_AR15 corresponds to seeding the DC  area with 100 infections on Sept 17th and starting vaccination on Oct 1st.  The model was calibrated to produce a serological attack rate of 15%, and 2/3 of the infections are symptomatic.  We are using a 50% Vaccine coverage rate and prioritizing based on the ACIP recommendations (school age kids, heath-care workers, etc.). a.       The first plot is an unmitigated epidemic b.      The second is with the vaccination implemented c.       The third is with 2 week reactive school closures and vaccination d.      The fourth is with 2 week system wide school closures and vaccination e.      The fifth is with giving 50% of symptomatic AVs that reduce their infectivity by 16%, 2 week system wide school closures and vaccination. f.          2)      Oct_AR25 are the same runs, but with the model calibrated to produce a serological attack rate of 25%.

  10. Ira Longini, Betz Halloran, et al

  11. Vaccine supply

  12. Epidemic scenarios • Moderate transmissibility (R0=1.6) • Peak in October or November • Lower transmissibility (R0=1.3) • Peak in November or December

  13. Epidemic peak and vaccine supply 16 Vaccine production data taken from BARDA (September 24, 2009) and assumes 1 dose per person

  14. Impact of vaccination November peak, R0=1.3

  15. Impact of vaccinationDecember peak, R0=1.3

  16. Ventilator Capacity and Demand Visualization of the ventilator data runs.  Simulation without spatial demographics.  The clinical attack rate is 17% in the epidemic.  The ventilator data was produced with the assumptions at 2/3 symptomatics, 0.7% symptomatic are hospitalized and that 7.5% of hospitalized patients end up on ventilator. The key to the map is as follows, and is colored based on the capacity that is present in each county: Light Green = 0-10%Green      = 11-50%Dark Green  = 50-100%Red > 100%Purple no capacity, but have at least one patient.

  17. Remaining Key Problems / Sources of uncertainty • “Now-casting”: How can we estimate the past and current spatio-temporal patterns • of transmission? • What is the ratio of Symptomatic / Total infections ? • “Fore-casting”: Will there be a swine “Third Wave” this winter? • How strong is pandemic vs seasonal “virus interference”? • How strong is seasonality and will there be a future winter peak due to • seasonal forcing ? • Why are older persons being spared? Immunity or connectivity?

  18. “Now-Casting” • The greatest source of uncertainty now in our models is where we are on the epidemic curve: the “Now-casting” problem • In order to appropriately respond to the evolving situation with as much accuracy as possible, understanding the current status of the epidemic is critical.

  19. Week 34 August 29, 2009

  20. Potential Data Sources • CDC for State level data and County level data • Individual State websites • Individual State Epidemiologists (Pennsylvania) • PRISM / Insurance companies • Emerging Infection Program sites • BioSense • ? Others

  21. The most accurate and timely data available should be used as a tool to parameterize dynamic models in response to the immediate situation.This is necessary if MIDAS is to be as helpful as it can be.

  22. END

More Related