Abstract
Public health surveillance systems provide valuable data for reliable predication of future epidemic events. This paper describes a study that used nine types of infectious disease data collected through a national public health surveillance system in mainland China to evaluate and compare the performances of four time series methods, namely, two decomposition methods (regression and exponential smoothing), autoregressive integrated moving average (ARIMA) and support vector machine (SVM). The data obtained from 2005 to 2011 and in 2012 were used as modeling and forecasting samples, respectively. The performances were evaluated based on three metrics: mean absolute error (MAE), mean absolute percentage error (MAPE), and mean square error (MSE). The accuracy of the statistical models in forecasting future epidemic disease proved their effectiveness in epidemiological surveillance. Although the comparisons found that no single method is completely superior to the others, the present study indeed highlighted that the SVMs outperforms the ARIMA model and decomposition methods in most cases.
Highlights
Public health surveillance is an important way to continuously collect, analyze, interpret and disseminate health data essential to prevention and control [1]
Autoregressive integrated moving average (ARIMA) models have been widely used for epidemic time series forecasting including the hemorrhagic fever with renal syndrome [6,7], dengue fever [8,9], and tuberculosis [10]
The mean absolute percentage error (MAPE) for all infectious disease are controlled within 30% except hemorrhagic fever (55%), and typhoid fever (51%)
Summary
Public health surveillance is an important way to continuously collect, analyze, interpret and disseminate health data essential to prevention and control [1]. Public health surveillance systems are designed to facilitate the detection of abnormal behavior of infectious diseases and other adverse health events. To achieve this goal, different statistical methods have been used to forecast infectious disease incidence. The time series models try to predict epidemiological behaviors by modeling historical surveillance data. Many researchers have applied different time series models to forecasting epidemic incidence in previous studies. Exponential smoothing [2] and generalized regression [3] methods were used to forecast in-hospital infection and incidence of cryptosporidiosis respectively. Models based on artificial neural networks were used to predict the incidence of hepatitis A [11,12] and typhoid fever [13]
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