Overall point: knowledge of Rela4ve Humidity 2weeks ago improves predic4on

Transcription

1 Overall point: knowledge of Rela4ve Humidity 2weeks ago improves predic4on Knowing the RH two weeks ago improves accuracy in predic8ng an epidemic by ~25% 1 Coupled with a two week forecast, this indicates an improved ability to an9cipate a roll- off in epidemic 4 weeks in advance Using RH Without RH 1 It turns out other variables (air temp, winds, NE winds) also help, but less than relative humidity

2 Unique Datasets/Software Created Thorpex-Tigge UKMO NCAR CMC NCEP NCDC ECMWF MeteoFrance CMA KMA JMA IDD/LDM HTTP FTP Archive Centre Current Data Provider CPTEC Unidata IDD/LDM Internet Data Distribution / Local Data Manager BoM Commodity internet application to send and receive data

3 Weekly WHO telecons weather variables impacting meningitis incidence (RH) Employing Thorpex-Tigge ensemble operationally Providing weekly 1- and 2-wk ahead forecast updates on meningitis belt status to WHO/countries Comparison with 2011 for the same week April telecon 2-week ahead RH forecast (epidemiological week 17) for the ECMWF ensemble mean and the climatological state for the same week

4 Benefits of using humidity to forecast in-season epidemics: Preliminary Results Model trained for Chad showed benefits to using both most recent incidence reports and weather information to provide 2- and 3-week-ahead forecasts However, model trained on Benin-Togo showed no benefit in using weather information for 2-week forecasts; but only weather information (no incidence reports) selected for 3- week forecasts Requiring Sensitivity = 80% forced models to have low Positive Predictive Value (PPV< 25%)! => Too many times (>75% of time) vaccinating districts when they didn t need to be vaccinated! => Set forecast thresholds to high PPV?!

5 Chad: 3wk forecast-incidence compared

6 End-of-season date (risk less than climatological risk) Day of Year

7 Can humidity forecasts benefit inseason epidemic forecasts? Goal: Compare weekly forecast models with and without humidity to see benefits Steps: Fit logistic regression model for crossing threshold of >1/100,000 using cross-validation Solve for maximum forecast model probability so that Sensitivity = 0.8 Look at contingency table to compare Data: Country data (Benin-Togo, Chad) Incidence reports a) 2-5wks previous and b) 3-5wks Nearest-grid relative humidity, 2wks previous Time since last vaccine

8 Benin-Togo contingency table forecast 2 weeks-ahead Forec. yes True_pos, TP False_pos, FP Pos_pred, TP/(TP+FP) Forec. no False_neg, FN True_neg, TN Neg_pred, TN/(FN+TN) Sensitivity, TP/(TP+FN) Specificity, TN/(FP+TN) Humidity alone Forecast yes % Forecast no % 83% 73% Combined model (humidity did not benefit forecast) Forecast yes % Forecast no % 83% 84%

9 Benin-Togo contingency table forecast 3 weeks-ahead Forec. yes True_pos, TP False_pos, FP Pos_pred, TP/(TP+FP) Forec. no False_neg, FN True_neg, TN Neg_pred, TN/(FN+TN) Sensitivity, TP/(TP+FN) Specificity, TN/(FP+TN) Combined model (previous incid. didn t add benefit) => Humidity alone Forecast yes % Forecast no % 83% 73%

10 Chad contingency table forecast 2 & 3 wk combined models Forec. yes True_pos, TP False_pos, FP Pos_pred, TP/(TP+FP) Forec. no False_neg, FN True_neg, TN Neg_pred, TN/(FN+TN) Sensitivity, TP/(TP+FN) Specificity, TN/(FP+TN) 2 week forecast (sens_thresh 49%) Forecast yes % Forecast no % 80% 97% 3 week forecast (sens_thresh 24%) Forecast yes % Forecast no % 80% 96%

11 Developing a Decision Support System Need to allow regional view of humidity to support regional decisions Use compartmental (differential-equation) based model to predict cases and end-of-season Allow users to look at district-level predictions Incorporate current epidemiological data to improve prediction Can this start to create an archive of cases for future research?"

12 Capabilities Demonstrated -- Africa Decision Information System

13 Capabilities Demonstrated -- Africa Decision Information System (cont)

14 Capabilities Demonstrated -- Africa Decision Information System (cont)

15 Capabilities Demonstrated -- Africa Decision Information System (cont)