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

Outlines BACKGROUND OBJECTIVE METHODS PRELIMINARY RESULTS NEXT STEPS 2

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

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

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

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

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

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

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

Preliminary results Cumulative sum of weekly distributions Cases Deaths 10

Preliminary results Principal component analyses 11

Preliminary results Weeks Districts Cluster analyses Group selection W J Districts B N U

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

Bayesian net graph 14

« Impacting » and « impacted » district probabilities 15

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

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