'Outbreak Gold Standard' selection to provide optimized threshold for infectious diseases early-alert based on China Infectious Disease Automated-alert and Response System.

The China Infectious Disease Automated-alert and Response System (CIDARS) was successfully implemented and became operational nationwide in 2008. The CIDARS plays an important role in and has been integrated into the routine outbreak monitoring efforts of the Center for Disease Control (CDC) at all levels in China. In the CIDARS, thresholds are determined using the "Mean+2SD? in the early stage which have limitations. This study compared the performance of optimized thresholds defined using the "Mean +2SD? method to the performance of 5 novel algorithms to select optimal "Outbreak Gold Standard (OGS)? and corresponding thresholds for outbreak detection. Data for infectious disease were organized by calendar week and year. The "Mean+2SD?, C1, C2, moving average (MA), seasonal model (SM), and cumulative sum (CUSUM) algorithms were applied. Outbreak signals for the predicted value (Px) were calculated using a percentile-based moving window. When the outbreak signals generated by an algorithm were in line with a Px generated outbreak signal for each week, this Px was then defined as the optimized threshold for that algorithm. In this study, six infectious diseases were selected and classified into TYPE A (chickenpox and mumps), TYPE B (influenza and rubella) and TYPE C [hand foot and mouth disease (HFMD) and scarlet fever]. Optimized thresholds for chickenpox (P55), mumps (P50), influenza (P40, P55, and P75), rubella (P45 and P75), HFMD (P65 and P70), and scarlet fever (P75 and P80) were identified. The C1, C2, CUSUM, SM, and MA algorithms were appropriate for TYPE A. All 6 algorithms were appropriate for TYPE B. C1 and CUSUM algorithms were appropriate for TYPE C. It is critical to incorporate more flexible algorithms as OGS into the CIDRAS and to identify the proper OGS and corresponding recommended optimized threshold by different infectious disease types.

'Outbreak Gold Standard'Selection to Provide Optimized Threshold for Infectious Diseases Early-alert Based on China Infectious Disease Automated-alert and Response System / 华中科技大学学报（医学）（英德文版）

The China Infectious Disease Automated-alert and Response System (CIDARS) was successfully implemented and became operational nationwide in 2008.The CIDARS plays an important role in and has been integrated into the routine outbreak monitoring efforts of the Center for Disease Control (CDC) at all levels in China.In the CIDARS,thresholds are determined using the'Mean+2SD'in the early stage which have limitations.This study compared the performance of optimized thresholds defined using the'Mean +2SD'method to the performance of 5 novel algorithms to select optimal 'Outbreak Gold Standard (OGS)'and corresponding thresholds for outbreak detection.Data for infectious disease were organized by calendar week and year.The'Mean+2SD',C1,C2,moving average (MA),seasonal model (SM),and cumulative sum (CUSUM) algorithms were applied.Outbreak signals for the predicted value (Px) were calculated using a percentile-based moving window.When the outbreak signals generated by an algorithm were in line with a Px generated outbreak signal for each week,this Px was then defined as the optimized threshold for that algorithm.In this study,six infectious diseases were selected and classified into TYPE A (chickenpox and mumps),TYPE B (influenza and rubella) and TYPE C [hand foot and mouth disease (HFMD) and scarlet fever].Optimized thresholds for chickenpox (P55),mumps (P50),influenza (P40,P55,and P75),rubella (P45 and P75),HFMD (P65 and P70),and scarlet fever (P75 and Ps0) were identified.The C1,C2,CUSUM,SM,and MA algorithms were appropriate for TYPE A.All 6 algorithms were appropriate for TYPE B.C1 and CUSUM algorithms were appropriate for TYPE C.It is critical to incorporate more flexible algorithms as OGS into the CIDRAS and to identify the proper OGS and corresponding recommended optimized threshold by different infectious disease types.