A Simulation-Based Study on the Comparison of Statistical and Time Series Forecasting Methods for Early Detection of Infectious Disease Outbreaks.

Eunjoo Yang,Lkhagvadorj Munkhdalai,Keun Ryu,Jusim Kim,Hyun Park,Yeon Choi,Ibrahim Musa

doi:10.3390/ijerph15050966

Abstract

Early detection of infectious disease outbreaks is one of the important and significant issues in syndromic surveillance systems. It helps to provide a rapid epidemiological response and reduce morbidity and mortality. In order to upgrade the current system at the Korea Centers for Disease Control and Prevention (KCDC), a comparative study of state-of-the-art techniques is required. We compared four different temporal outbreak detection algorithms: the CUmulative SUM (CUSUM), the Early Aberration Reporting System (EARS), the autoregressive integrated moving average (ARIMA), and the Holt-Winters algorithm. The comparison was performed based on not only 42 different time series generated taking into account trends, seasonality, and randomly occurring outbreaks, but also real-world daily and weekly data related to diarrhea infection. The algorithms were evaluated using different metrics. These were namely, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), F1 score, symmetric mean absolute percent error (sMAPE), root-mean-square error (RMSE), and mean absolute deviation (MAD). Although the comparison results showed better performance for the EARS C3 method with respect to the other algorithms, despite the characteristics of the underlying time series data, Holt–Winters showed better performance when the baseline frequency and the dispersion parameter values were both less than 1.5 and 2, respectively.

Highlights

A number of emergency department-based syndromic surveillance systems and early warning systems for early detection of adverse disease events have been implemented since the year 2000.Syndromic surveillance is defined as the ongoing systematic collection, analysis, and interpretation of “syndrome”-specific data for early detection of public health aberrations [1]
Since the real-world data of the syndromic diarrhea did not include an attribute that defined the state of the outbreak, the performances of the algorithms in this case were evaluated based on symmetric mean absolute percent error (sMAPE), root-mean-square error (RMSE), and mean absolute deviation (MAD) as evaluation metrics
Since the default parameters assume a time series without trends and seasonality, in this paper, trend and seasonality were added into a generalized linear model resulting in a new model named “glm with trend” which requires an additional parameter, namely, “trans = ‘rossi’”, which indicates a version of the CUmulative SUM (CUSUM) algorithm that deals with trends, and seasonality [6]

Summary

Introduction

A number of emergency department-based syndromic surveillance systems and early warning systems for early detection of adverse disease events have been implemented since the year 2000. Syndromic surveillance is defined as the ongoing systematic collection, analysis, and interpretation of “syndrome”-specific data for early detection of public health aberrations [1]. The Korea Centers for Disease Control and Prevention (KCDC) has implemented an emergency department-based syndromic surveillance system. The system was designed to identify illness clusters before diagnoses are confirmed and reported to public health agencies. The system is connected to a considerable number of emergency departments from 17 provinces and cities in Korea and has been used to monitor the daily status of five different syndromes. A number of statistical methodologies have been proposed for

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