Forecasting influenza epidemics by integrating internet search queries and traditional surveillance data with the support vector machine regression model in Liaoning, from 2011 to 2015

Abstract

BackgroundInfluenza epidemics pose significant social and economic challenges in China. Internet search query data have been identified as a valuable source for the detection of emerging influenza epidemics. However, the selection of the search queries and the adoption of prediction methods are crucial challenges when it comes to improving predictions. The purpose of this study was to explore the application of the Support Vector Machine (SVM) regression model in merging search engine query data and traditional influenza data.MethodsThe official monthly reported number of influenza cases in Liaoning province in China was acquired from the China National Scientific Data Center for Public Health from January 2011 to December 2015. Based on Baidu Index, a publicly available search engine database, search queries potentially related to influenza over the corresponding period were identified. An SVM regression model was built to be used for predictions, and the choice of three parameters (C, γ, ε) in the SVM regression model was determined by leave-one-out cross-validation (LOOCV) during the model construction process. The model’s performance was evaluated by the evaluation metrics including Root Mean Square Error, Root Mean Square Percentage Error and Mean Absolute Percentage Error.ResultsIn total, 17 search queries related to influenza were generated through the initial query selection approach and were adopted to construct the SVM regression model, including nine queries in the same month, three queries at a lag of one month, one query at a lag of two months and four queries at a lag of three months. The SVM model performed well when with the parameters (C = 2, γ = 0.005, ɛ = 0.0001), based on the ensemble data integrating the influenza surveillance data and Baidu search query data.ConclusionsThe results demonstrated the feasibility of using internet search engine query data as the complementary data source for influenza surveillance and the efficiency of SVM regression model in tracking the influenza epidemics in Liaoning.

Seasonality and trend prediction of scarlet fever incidence in mainland China from 2004 to 2018 using a hybrid SARIMA-NARX model

“…At present, many efforts have been made to construct modeling approaches to track and understand the temporal characteristics of infectious diseases, and furthermore to predict outbreaks (He et al, 2017). A multitude of standard mathematical techniques like the autoregressive integrated moving average (ARIMA) model (Song et al, 2016), support vector machine (Liang et al, 2018), multivariate time series method (Zhang et al, 2016a), generalized regression model (Zhang et al, 2016b), error-trend-seasonal technique (Wang et al, 2018), seasonal decomposition model and exponential smoothing model (Al-Sakkaf & Jones, 2014), have been regarded as a serviceable policy-supportive tool for the incidence time series forecasting of contagious diseases. Of these approaches, the ARIMA method assuming time series to be stationary is the most popular approach for time series estimation.…”

Applying Machine Learning Models with An Ensemble Approach for Accurate Real-Time Influenza Forecasting in Taiwan: Development and Validation Study

“…In order to resolve the timeliness problem of the influenza-like illness surveillance data, researchers have attempted to explore the use of social media data (such as Twitter and Facebook) or internet search data (such as Google search and Google Flu Trends) to develop forecasting models because these data can be collected in almost real-time [ 11 , 22 , 26 , 36 , 41 , 44 ]. However, the method of data collection, quality of social media data, and accuracy of the models still posed problems [ 22 , 26 , 36 , 41 ]. In our framework, we did not include social media data because of the following reasons.…”

“…Lasso regression, random forest, extreme gradient boosting, and support vector regression have been widely implemented to aggregate these data from Google search, Google trend, Wikipedia, and social media (such as Twitter and Baidu) in influenza forecasting [ 24 - 27 ]. The performance of elastic net and support vector regression was considered to be comparable in a study [ 26 ] which used the Baidu index as a predictor and predicted the number of influenza cases in China by support vector regression, and in a study [ 28 ] in France which used electronic health record data with historical epidemiology information for influenza-like illness incidence rate predictions.…”

<p>Estimating the Prevalence and Mortality of Coronavirus Disease 2019 (COVID-19) in the USA, the UK, Russia, and India</p>

“…3,[5][6][7] Also, stakeholders can apply the time series from the past and present outbreaks to forecast prevalence rates and then identifying how to limit the spread of the virus, and ultimately introducing the most effective vaccination policies. 5 Numerous modeling techniques (such as machine learning method, 8 general linear model, 9 spatiotemporal approach, 5 artificial neural networks (ANNs), 10 grey GM (1,1) model, 11 autoregressive integrated moving average (ARIMA), 12 support vector machine (SVM) regression model, 13 multivariate time series analysis, 14 and susceptible-exposed-infectious-recovered (SEIR) model) 15 that serve as helpful policy-supportive tools have been utilized to model and estimate the epidemic patterns and even the outbreak of infectious diseases. Among which, the most commonly adopted methods for various predictive objectives are either the linear models (such as general linear model, GM (1,1), and ARIMA) or the nonlinear models (such as SVM and ANNs).…”