Measles Vaccination in Epidemic Contexts

1. Measles Vaccination in Epidemic Contexts RF Grais, ACK Conlan, MJ Ferrari, C Dubray, A Djibo, F Fermon, M-E Burny, KP Alberti, I Jeanne, BS Hersh, PJ Guerin, ON Bjornstad, BT Grenfell June 1, 2006

2. Background Cases Place Year Length (months) 1 12+ 6 Kinshasa, DRC 17624 2002 Kinshasa, DRC 40857 2005 Niamey, Niger 10880 2003 Adamawa, Nigeria 2505 2004 Ndjamena, Chad 8015 2005

3. Rationale • Operational guidance for MSF • WHO guidelines (1999) • Spread so fast its always too late • Scarce resources best invested elsewhere • Based on literature review and mathematical models of epidemics in non-African settings

4. Objectives • Measure the impact of vaccination interventions • Examine: • Timing of interventions in course of epidemic • Age range to vaccinate • Intervention vaccination coverage • Generalize to other settings

5. Overview of methodology • Estimate effective reproductive ratio • Chain-Binomial/MLE • Ferrari, et al, 2005, Math Biosc, 98(1), 14-26 • Recreate epidemic & simulate interventions • Individual-based model • Niamey, Niger 2003-2004 as a case study • Generalize results • Standard epidemic model with vaccination

6. 1) Estimating the Effective Reproductive Ratio (R) • Niamey, Niger (2003-2004): 2.8 • Kinshasa, DRC (2005-6): 1.9 • Ndjamena, Chad (2005): 2.5 I I NI I NI I R= avg number secondary cases generated by one case in a partially immune population

7. 2) Recreating an epidemic, Niamey, Niger 2003-2004: Key Assumptions • Constant • 15 day delay between decision and delivery • 10 day intervention • Vaccine efficiency = 85% • Variable • 2 age ranges for vaccination (standard): • 6m to 59m • 6m to <15y • Interventions: • 2, 3 or 4 months after epidemic starts • vaccination coverage: 30% – 100%

8. 2) Model Overview: Niamey, Niger • Probability of infection: • age • immune status • vaccination status • location in the city • status of other children • contact decreases with distance • time

9. 2) Model Overview: Niamey, Niger • Probability of infection: • age • immune status • vaccination status • location in the city • status of other children • contact decreases with distance • time

10. 2) Model Overview: Niamey, Niger • Probability of infection: • age • immune status • vaccination status • location in the city • status of other children • contact decreases with distance • time

11. 2) Model Overview: Niamey, Niger • Probability of infection: • age • immune status • vaccination status • location in the city • status of other children • contact decreases with distance • time

12. 2) Model Overview: Niamey, Niger • Probability of infection: • age • immune status • vaccination status • location in the city • status of other children • contact decreases with distance • time

13. 2) Proportion cases prevented by intervention coverage and time: 6 to 59m, Niamey, Niger 100 2 months 3 months 90 4 months + 6 months 80 70 60 Proportion of Cases Prevented (%) 50 40 30 20 10 0 30 40 50 60 70 80 90 100 Intervention Coverage (%)

14. 2) Proportion cases prevented by intervention coverage and time: 6 to 15y, Niamey, Niger 100 90 80 70 60 Proportion of cases prevented(%) 50 40 2 months 3 months 30 4 months 20 10 0 30 40 50 60 70 80 90 100 Intervention Coverage (%)

15. 3) Generalizing to different scenarios (ex.: 50% coverage, 10 days, 100 000 persons) Proportion reduction in number of cases R Day of intervention

16. Conclusions • More time than we thought to intervene • 3 Key Factors • Timing • Age range for vaccination • Vaccination coverage objective • Benefit even when late • Up to 8% = 800 cases • Revision of WHO guidelines

17. Ministries of Health, Niger, Nigeria, Chad, DRC MSF-F and MSF-B in field and Paris WHO Survey teams Study participants Center for Infectious Disease Dynamics CERMES EPIET Acknowledgements