Utilizing Google Search Data With Deep Learning, Machine Learning and Time Series Modeling to Forecast Influenza-Like Illnesses in South Africa

Abstract

Influenza-like illnesses (ILI) result in deaths and hospitalizations across the globe. Traditional surveillance systems rely on data from general medical practitioners. The process is resource-intensive and plagued with delay. Although recent studies have shown the potential utility of free and fast alternatives like web and social media data, the reliability cannot be generalized due to differences in technological culture. Meanwhile, there is a scarcity of studies exploring these free online data for (sub-Saharan) African countries. In this paper, we utilize Google trends (GT) data for ILI forecasting in South Africa. We study models based on deep learning (Long short-term memory (LSTM) and feedforward neural networks (FNN)), machine learning (Multiple linear regression (MLR), elastic net (EN), support vector machine (SVM)), and statistical time series (seasonal autoregressive integrated moving average (SARIMA)) algorithms. The FNN and SVM models using GT data alone, produce forecasts close in accuracy to those fitted to actual ILI data. The algorithms rank differently across various performance measures. Generally, the deep learning techniques perform better than the other algorithms in our study. However, tuning the former is quite intricate. Combining GT and historical ILI data enhances the models. The non-deep-learning algorithms benefit more from this enhancement. Furthermore, we observe that search volume increases proportional to and timeously with reported infection rates, suggesting that South Africans search Google in the week they feel flu symptoms. Thus, monitoring Google search trends is a reliable proxy for monitoring flu spread in South Africa.

Keywords

• Deep learning
• flu surveillance
• Google Trends
• ILI reporting
• influenza forecasting
• machine learning