Robust Outbreak Surveillance

Transcription

1 Robust Outbreak Surveillance Marianne Frisén Statistical Research Unit University of Gothenburg Sweden Marianne Frisén Open U 21 1

2 Outline Aims Method and theoretical background Computer program Application to influenza in Sweden Comparisons Multivariate outbreak detection Marianne Frisén Open U 21 2

3 Examples of outbreak definitions Spatial clusters Higher incidence than usual LDI Increasing incidence Week Marianne Frisén Open U 21 3

4 Detection of onset of outbreak Marianne Frisén Open U 21 4

5 Robust method for detection of rise or decline Specify states C (nonepidemic) and D (epidemic) in terms of monotonicity Maximize the likelihood under the restrictions Signal when the ratio between the maximal likelihoods exceeds a limit x s : max max ( x D) k Marianne Frisén Open U 21 5 f f ( x s s C)

6 Andersson, E., Bock, D. and Frisén, M. (28) Modeling influenza incidence for the purpose of on-line monitoring. Statistical Methods in Medical Research, 17, Bock, D., Andersson, E. and Frisén, M. (28) Statistical Surveillance of Epidemics: Peak Detection of Influenza in Sweden. Biometrical Journal, 5, Frisén, M., Andersson, E. and Pettersson, K. (21) Semiparametric estimation of outbreak regression. Statistics 44, Frisén, M. and Andersson, E. (29) Semiparametric Surveillance of Monotonic Changes. Sequential Analysis, 28, Frisén, M., Andersson, E. and Schiöler, L. (29) Robust outbreak surveillance of epidemics in Sweden. Statistics in Medicine, 28, Marianne Frisén Open U 21 6

7 Computer programs Download and instructions Marianne Frisén Open U 21 7

8 No need for baseline estimation The alarm limit is determined by the same way as for other methods Marianne Frisén Open U 21 8

9 Surveillance Repeated observations Repeated decisions No fix hypothesis Time important Marianne Frisén Open U 21 9

10 Alarm criteria values for influenza Predicive value = P(outbreak alarm) At alarm signals (limit 5).99 At warning (limit 1).95 Median time to false alarm alarm 8 weeks - warning 9 Significance level (not recommended) alarm.2 - warning.4 Marianne Frisén Open U 21 1

11 MRL λ=1 λ=1 λ= Alarm Limit Marianne Frisén Open U 21 11

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15 Outbreak statistic Marianne Frisén Open U 21 15

16 E+24 1E+23 1E+22 1E+21 1E+2 1E+19 1E+18 1E+17 1E+16 1E+15 1E+14 1E+13 1E+12 1E+11 1E+1 1E+9 1E+8 1E E+24 1E+23 1E+22 1E+21 1E+2 1E+19 1E+18 1E+17 1E+16 1E+15 1E+14 1E+13 1E+12 1E+11 1E+1 1E+9 1E+8 1E E+24 1E+23 1E+22 1E+21 1E+2 1E+19 1E+18 1E+17 1E+16 1E+15 1E+14 1E+13 1E+12 1E+11 1E+1 1E+9 1E+8 1E E+24 1E+23 1E+22 1E+21 1E+2 1E+19 1E+18 1E+17 1E+16 1E+15 1E+14 1E+13 1E+12 1E+11 1E+1 1E+9 1E+8 1E LDI Larm LDI Larm LDI Larm LDI Larm E+24 1E+23 1E+22 1E+21 1E+2 1E+19 1E+18 1E+17 1E+16 1E+15 1E+14 1E+13 1E+12 1E+11 1E+1 1E+9 1E+8 1E E+24 1E+23 1E+22 1E+21 1E+2 1E+19 1E+18 1E+17 1E+16 1E+15 1E+14 1E+13 1E+12 1E+11 1E+1 1E+9 1E+8 1E E+24 1E+23 1E+22 1E+21 1E+2 1E+19 1E+18 1E+17 1E+16 1E+15 1E+14 1E+13 1E+12 1E+11 1E+1 1E+9 1E+8 1E E+24 1E+23 1E+22 1E+21 1E+2 1E+19 1E+18 1E+17 1E+16 1E+15 1E+14 1E+13 1E+12 1E+11 1E+1 1E+9 1E+8 1E LDI Larm LDI Larm LDI Larm LDI Larm Accumulation of information _2 2_3 3_4 4_5 5_6 6_7 Outbreak statistic Observations Marianne Frisén Open U 21 16

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18 Subjective judgment vs automatic - Less efficient - Large variation between judges + expert knowledge Combination! Marianne Frisén Open U 21 18

19 Parametric vs non-parametric method Exactly known baseline The parametric method is best Estimation with the efficiency available for Swedish influenza The nonparametric method is best Marianne Frisén Open U 21 19

20 Tularemia Sweden Rolfhamre P, Ekdahl K. An evaluation and comparison of three commonly used statistical models for automatic detection of outbreaks in epidemiological data of communicable diseases. Epidemiology and Infection 26; 134: and OutbreakP Method Sensitivity % PPV % Swiftness OutbreakP SPOTv EW CuSum CuSum Marianne Frisén Open U 21 2

21 Possible reason for good results The Outbreak method evaluates evidence of increase no uncertain baseline The Outbreak method accumulates information in an optimal way Marianne Frisén Open U 21 21

22 Multivariate version Combination of different data sources Example: geographical information Marianne Frisén Open U 21 22

23 Inference approaches to spatial (multivariate) surveillance Summarizing statistics for each time Parallel surveillance for each location Vector accumulation Joint solution Marianne Frisén Open U 21 23

24 Relation between the times of outbreak Information on the relation between the change times should be used The properties of a method depends on this relation Frisén, M., Andersson, E. and Schiöler, L. (21) Evaluation of Multivariate Surveillance. To appear in Journal of Applied Statistics Marianne Frisén Open U 21 24

25 Google Flu 1-2 weeks before CDC Marianne Frisén Open U 21 25

26 Influenza in Sweden First at the metropolitan areas with large airports. one week later at the rest. Marianne Frisén Open U 21 26

27 Sufficient reduction at a time lag For processes which each belong to the one parameter exponential family there exist a sufficient reduction to a univariate statistic Järpe, E. (2). On univariate and spatial surveillance. Ph.D Thesis. Department of Statistics, Göteborg University, Göteborg. Frisén, M., Andersson, E. & Schiöler, L. (21) Sufficient reduction in multivariate surveillance. To appear in Communications in Statistics. Theory and methods. Marianne Frisén Open U 21 27

28 Sufficient reduction is more efficient than a summarizing statistic for each time or parallel use of the Outbreak method for each location Marianne Frisén Open U 21 28

29 Sufficient reduction for time lag Most efficient for correct time lag Robust for small error of time lag Marianne Frisén Open U 21 29

30 Influenza in Sweden The sufficient reduction based on a time lag of one week gave an improvement for 27% of the last 11 seasons. The same for 73%. Marianne Frisén Open U 21 3

31 Computer-generated outbreak signals Methods can be further developed to efficiently use the available data to adapt better to the needs Routine use needed to detect new threats to give information for planning Complement to expert evaluation Marianne Frisén Open U 21 31

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