Leveraging Google search data to track influenza outbreaks in Africa

[摘要] Background: Traditionally, public health agencies track seasonal influenza activity by collecting information from clinics, hospitals, and laboratories. The inherent slowness of the processes used to collect influenza activity data limits the ability of public health agencies to adapt to unexpected changes in influenza activity in near real-time. In recent years, new influenza surveillance methods that use nontraditional data sources, such as Google searches, have been proposed to successfully estimate influenza activity in near real-time. However, most of these methods have been designed for and implemented in high-income countries even though influenza disease burden remains high in low- to middle-income countries. Here, we seek to predict influenza activity in near real-time in Africa using machine learning models that combine Google searches with traditional epidemiological data. Methods: We extend the AutoRegression with Google search data (ARGO) model to track influenza activity in near-real-time in Africa. The ARGO model, which was originally designed to predict influenza activity in the United States, combines influenza-related Google searches with historical laboratory-confirmed influenza trends. We evaluate the predictive performance of the ARGO model and compare it with several benchmark models in Algeria, Ghana, Morocco, and South Africa. We also explore the advantages and limitations of using Google search data to monitor influenza activity. Results: In South Africa, Algeria, and Morocco, the ARGO model outperforms all benchmark models, suggesting that incorporating influenza-related Google search information in predictive models in these countries leads to improved predictions. In Ghana, however, the ARGO model and the autoregressive model of historical influenza activity have comparable performances. Conclusions: These results demonstrate that the quality of the ARGO predictions is higher in regions where influenza activity is seasonal, historical influenza activity is recorded consistently, and the volume of influenza-related Google search queries is enough to appear as non-zero in the Google Trends tool.

[学科分类] 电子与电气工程

[关键词] Real-time disease surveillance;Digital epidemiology;Google Flu Trends;Influenza monitoring;Seasonal influenza