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

Background

2005

2004

2003

2005

2002

8015

2505

10880

40857

17624

Ndjamena, Chad

Adamawa, Nigeria

Niamey, Niger

Kinshasa, DRC

Kinshasa, DRC

CasesLength (months)YearPlace

1 6 12+

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

Objectives

1. Measure the impact of vaccination interventions

2. Examine:• Timing of interventions in course of epidemic• Age range to vaccinate• Intervention vaccination coverage

3. Generalize to other settings

Overview of methodology

1. Estimate effective reproductive ratio• Chain-Binomial/MLE • Ferrari, et al, 2005, Math Biosc, 98(1), 14-26

2. Recreate epidemic & simulate interventions • Individual-based model• Niamey, Niger 2003-2004 as a case study

3. Generalize results• Standard epidemic model with vaccination

Niamey, Niger (2003-2004): 2.8

Kinshasa, DRC (2005-6): 1.9

Ndjamena, Chad (2005): 2.5

I

NI

NI

I

I

I

R= avg number secondary cases generated by one case in a partially immune population

1) Estimating the Effective Reproductive Ratio (R)

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%

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

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

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

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

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

city

citycity

commune

communecommune

CSI

CSICSI

quartier

quartierquartierat,i

NIβ+

NIβ+

NIβ+

NIβ=,P exp1

2) Proportion cases prevented by intervention coverage and time: 6 to 59m, Niamey, Niger

0

10

20

30

40

50

60

70

80

90

100

30 40 50 60 70 80 90 100

Intervention Coverage (%)

Pro

po

rtio

n o

f C

ases

Pre

ven

ted

(%

) 2 months 3 months

4 months + 6 months

2) Proportion cases prevented by intervention coverage and time: 6 to 15y, Niamey, Niger

0

10

20

30

40

50

60

70

80

90

100

30 40 50 60 70 80 90 100Intervention Coverage (%)

Pro

po

rtio

n o

f c

ases

pre

ven

ted

(%

)

2 months 3 months

4 months

3) Generalizing to different scenarios (ex.: 50% coverage, 10 days, 100 000 persons)

Pro

port

ion

redu

ctio

n in

num

ber

of c

ases

RDay of intervention

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

Acknowledgements

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