Tracking COVID-19 using online search

Lampos, Vasileios; Moura, Simon; Yom-Tov, Elad; Edelstein, Michael; Majumder, Maimuna; McKendry, Rachel A.; Cox, Ingemar J.

Online search data is routinely used to monitor the prevalence of infectious diseases, such as influenza. Previous work has focused on supervised learning solutions, where ground truth data, in the form of historical syndromic surveillance reports, can be used to train machine learning models. However, no sufficient data -- in terms of accuracy and time span -- exist to apply such approaches for monitoring the emerging COVID-19 infectious disease pandemic caused by a novel coronavirus (SARS-CoV-2). Therefore, unsupervised, or semi-supervised solutions should be sought. Recent outcomes have shown that it is possible to transfer an online search based model for influenza-like illness from a source to a target country without using ground truth data for the target location. The transferred model's accuracy depends on choosing search queries and their corresponding weights wisely, via a transfer learning methodology, for the target location. In this work, we draw a parallel to previous findings and attempt to develop an unsupervised model for COVID-19 by: (i) carefully choosing search queries that refer to related symptoms as identified by a survey from the National Health Service in the United Kingdom (UK), and (ii) weighting them based on their reported ratio of occurrence in people infected by COVID-19. Furthermore, understanding that online searches may be also driven by concern rather than infections, we devise a preliminary approach that attempts to minimise this part of the signal by incorporating a basic news media coverage metric in association with confirmed COVID-19 cases. Finally, we propose a transfer learning method for mapping supervised COVID-19 models from a country to another, in an effort to transfer knowledge from areas where the disease has a more extended progression.