1. Meningitis surveillance
Advanced modeling
Meningitis surveillance
Data modeling in Niger
Preliminary results
Ariel BERESNIAK MD, PhD
26th Sep 2007

2. Outlines
BACKGROUND
OBJECTIVE
METHODS
PRELIMINARY RESULTS
NEXT STEPS 2

3. BACKGROUND Surveillance meningitis data routinely collected in Niger
Incidence&mortality weekly time series from 1986 at district level
Current analyses
Descriptive epidemiological information
Seasonality
Non directly usable for epidemic alert & response strategy 3

4. Objectives
Generate new meaningful information from meningitis surveillance data
Improve alert&response strategy
Explore new approaches of risk assessment 4

5. Methodological strategy
Cumulative sum of weekly distribution
Principal component analyses
Cluster analyses
Bayesian network 5

6. Cumulative sum of weekly data
Cumulative data over 20 years
Aggregation of data per week rank
Comparisons of distribution parameters
Average
Variance
Maximum
Skewness
Kurtosis 6

7. Principal component analysis
1st Table :
Raws : districts
Columns : average, variance, maximum, skewness, kurtosis
2d Table
Raws : weeks
Columns : districts
Projection on 2 axes
Presentation on factorial map 7

8. Cluster analyses Same cumulative data distribution sets
Data clustering using 3 techniques
Centroids method
Non hierarchical descending method
Ascending hierarchical method 8

9. Bayesian network
Graphical models manipulating information in uncertain context
Representation of relation between variables
Relation (arrow) = significant statistical link
Calculate conditional probabilities 9

10. Preliminary results Cumulative sum of weekly distributions Cases
Deaths 10

11. Preliminary results
Principal component analyses 11

12. Preliminary results Weeks Districts Cluster analyses Group selection WJ
Districts B N U

13. Preliminary results Bayesian network D1 = 0  District not under alert
D1 = 1  District under alert D2
Arc from D1 from D2 if
causal relationship.
Valuation by conditional
probability. D1 13

14. Bayesian net graph 14

15. « Impacting » and « impacted » district probabilities15

16. Interest of Bayesian network
Original and relevant approach in epidemiological surveillance
Decision making tool for improving effectiveness of vaccination response
Risk assessment tool 16

17. Next steps Extend the pilot model to other contiguous countries
Perform regional analyses
Improve model accuray in adding other parameters
Climat
Strains
Socio-economical parameters
Etc.
Detect potential epidemic intensity cycles over time 17