Applications and Comparisons of Four Time Series Models in Epidemiological Surveillance Data

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.

Effect of tobacco control policies on information seeking for smoking cessation in the Netherlands: A Google Trends study

“…ARIMA modelling is especially suitable when using detailed time series data such as Google Trends data [37]. Our study shows that Google data, measured in RSV, could be a useful tool to measure the effect of tobacco control policies, based on its ability to represent large populations.…”

Using an Autoregressive Integrated Moving Average Model to Predict the Incidence of Hemorrhagic Fever with Renal Syndrome in Zibo, China, 2004–2014

“…An ARIMA (p, d, q) model comprises 3 types of parameters (15,23,24): the autoregressive parameters (p), number of differencing passes (d), and moving average parameters (q). The multiplicative seasonal ARIMA (p, d, q) × (P, D, Q)s model is an extension of the ARIMA method of time series in which a pattern repeats seasonally over time (6,22,23). Analogous to the simple ARIMA parameters, the seasonal parameters are: seasonal autoregressive (P), seasonal differencing (D), and seasonal moving average parameters (Q).…”

“…The ARIMA model procedure consists of 3 iterative steps (6,22,24): identification, estimation, and diagnostic checking. Prior to fitting the ARIMA model, an appropriate difference of the series is usually performed to make the series stationary.…”

“…Autoregressive integrated moving average (ARIMA) models, which take into account changing trends, periodic changes, and random disturbances in time series, are very useful in modeling the temporal dependence structure of a time series (15). In epidemiology, ARIMA models have been successfully applied to predict the incidence of infectious diseases, such as HIV (16), influenza (17,18), malaria incidence (19), and others (20)(21)(22).…”

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