1 / 22

Modeling the Ebola Outbreak in West Africa, 2014

Modeling the Ebola Outbreak in West Africa, 2014. Sept 5 th Update Bryan Lewis PhD, MPH ( blewis@vbi.vt.edu ) Caitlin Rivers MPH, Eric Lofgren PhD, James Schlitt , Katie Dunphy , Stephen Eubank PhD, Madhav Marathe PhD, and Chris Barrett PhD. Currently Used Data.

gracie
Download Presentation

Modeling the Ebola Outbreak in West Africa, 2014

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. Modeling the Ebola Outbreak in West Africa, 2014 Sept 5th Update Bryan Lewis PhD, MPH (blewis@vbi.vt.edu) Caitlin Rivers MPH, Eric Lofgren PhD, James Schlitt, Katie Dunphy, Stephen Eubank PhD, MadhavMarathe PhD, and Chris Barrett PhD

  2. Currently Used Data • Data from WHO, MoH Liberia, and MoH Sierra Leone, available here: • https://github.com/cmrivers/ebola • Sierra Leone case counts censored up to 4/30/14. • Time series was filled in with missing dates, and case counts were interpolated.

  3. Liberia Forecasts Forecast performance Model Parameters 'alpha':1/12, 'beta_I':0.17950, 'beta_H':0.062036, 'beta_F':0.489256, 'gamma_h':0.308899, 'gamma_d':0.075121, 'gamma_I':0.050000, 'gamma_f':0.496443, 'delta_1':.5, 'delta_2':.5, 'dx':0.510845 rI: 0.95 rH: 0.65 rF: 0.61 R0 total: 2.22

  4. Forecasting Resource Demand • Accounting for prevalent cases in the model • Can include their modeled state: community, hospital, or burial • Help with logisitical planning

  5. Exhausting Health Care System • Model adjusted to have limited capacity “better” health compartment (sized: 300, 500, 1000, 2000 beds) added to existing “degraded” health compartment (previous fit) • Those in new health compartment assumed to be • Well isolated and the dead are buried properly (ie once in the health system, very limited transmission to community 90% less than original fit) • More beds have a measurable impact in total cases at 2 months, but does not halt transmission alone

  6. Next Steps • Agent-based modeling: • Initial version of Sierra Leone constructed • Need more work on mixing estimates • Initial look at sublocation modeling required a re-adjustment • Gathering data to assist in logistical questions • Further refinement of compartmental model to look at health-care system questions • Impact of increased / decreased effectiveness

  7. Supporting material describing model structure, and additional results Appendix

  8. Epi Notes • Case identified in Senegal • Guinean student, sought care in Dakar, identified and quarantined though did not report exposure to Ebola, thus HCWs were exposed. BBC • Liberian HCWs survival credited to Zmapp • Dr. SengaOmeonga and physician assistant KyndaKobbah were discharged from a Liberian treatment center on Saturday after recovering from the virus, according to the World Health Organization. CNN

  9. Epi Notes • Guinea riot in Nzerekore (2nd city) on Aug 29 • Market area “disinfected,” angry residents attack HCW and hospital, “Ebola is a lie” BBC • India quarantines 6 “high-risk” Ebola suspects on Monday in New Delhi • Among 181 passengers who arrived in India from the affected western African countries HealthMap

  10. Further evidence of endemic Ebola • 1985 manuscript finds ~13% sero-prevalence of Ebola in remote Liberia • Paired control study: Half from epilepsy patients and half from healthy volunteers • Geographic and social group sub-analysis shows all affected ~equally

  11. Twitter Tracking Most common images: Risk map, lab work (britain), joke cartoon, EBV rally

  12. Legrand et al. Model Description Legrand, J, R F Grais, P Y Boelle, A J Valleron, and A Flahault. “Understanding the Dynamics of Ebola Epidemics” Epidemiology and Infection 135 (4). 2007. Cambridge University Press: 610–21. doi:10.1017/S0950268806007217.

  13. Compartmental Model • Extension of model proposed by Legrand et al. Legrand, J, R F Grais, P Y Boelle, A J Valleron, and A Flahault. “Understanding the Dynamics of Ebola Epidemics” Epidemiology and Infection 135 (4). 2007. Cambridge University Press: 610–21. doi:10.1017/S0950268806007217.

  14. Legrand et al. Approach • Behavioral changes to reduce transmissibilities at specified days • Stochastic implementation fit to two historical outbreaks • Kikwit, DRC, 1995 • Gulu, Uganda, 2000 • Finds two different “types” of outbreaks • Community vs. Funeral driven outbreaks

  15. Parameters of two historical outbreaks

  16. NDSSL Extensions to Legrand Model • Multiple stages of behavioral change possible during this prolonged outbreak • Optimization of fit through automated method • Experiment: • Explore “degree” of fit using the two different outbreak types for each country in current outbreak

  17. Optimized Fit Process • Parameters to explored selected • Diag_rate, beta_I, beta_H, beta_F, gamma_I, gamma_D, gamma_F, gamma_H • Initial values based on two historical outbreak • Optimization routine • Runs model with various permutations of parameters • Output compared to observed case count • Algorithm chooses combinations that minimize the difference between observed case counts and model outputs, selects “best” one

  18. Fitted Model Caveats • Assumptions: • Behavioral changes effect each transmission route similarly • Mixing occurs differently for each of the three compartments but uniformly within • These models are likely “overfitted” • Many combos of parameters will fit the same curve • Guided by knowledge of the outbreak and additional data sources to keep parameters plausible • Structure of the model is supported

  19. Sierra Leone Forecasts rI:0.85 rH:0.74 rF:0.31 R0 total: 1.90 Model Parameters 'alpha':1/10 'beta_I':0.164121 'beta_H':0.048990 'beta_F':.16 'gamma_h':0.296 'gamma_d':0.044827 'gamma_I':0.055 'gamma_f':0.25 'delta_1':.55 delta_2':.55 'dx':0.58

  20. All Countries Forecasts rI:0.85 rH:0.74 rF:0.31 Overal:1.90

  21. Exhausting Health Care System • Model adjusted to have limited capacity “better” health compartment (sized: 300, 500, 1000, 2000 beds) added to existing “degraded” health compartment (previous fit) • Those in new health compartment assumed to be • Well isolated and the dead are buried properly (ie once in the health system, very limited transmission to community 90% less than original fit) • More beds have a measurable impact in total cases at 2 months, but does not halt transmission alone

  22. Long-term Operational Estimates • Based on forced bend through extreme reduction in transmission coefficients, no evidence to support bends at these points • Long term projections are unstable

More Related