Providing early indication of regional anomalies in COVID19 case counts in England using search engine queries

TL;DR

This study demonstrates that analyzing search engine queries for COVID-19 symptoms can provide early regional indicators of outbreaks in England, with searches for fever and cough predicting case surges 16-17 days in advance.

Contribution

The paper introduces a method using search query data to predict regional COVID-19 case counts and anomalies, offering a tool for early detection and response planning.

Findings

Searches for 'fever' and 'cough' correlated with future case counts

Search patterns predicted case surges with a 16-17 day lead time

The method achieved an AUC of 0.64 for case prediction

Abstract

COVID19 was first reported in England at the end of January 2020, and by mid-June over 150,000 cases were reported. We assume that, similarly to influenza-like illnesses, people who suffer from COVID19 may query for their symptoms prior to accessing the medical system (or in lieu of it). Therefore, we analyzed searches to Bing from users in England, identifying cases where unexpected rises in relevant symptom searches occurred at specific areas of the country. Our analysis shows that searches for "fever" and "cough" were the most correlated with future case counts, with searches preceding case counts by 16-17 days. Unexpected rises in search patterns were predictive of future case counts multiplying by 2.5 or more within a week, reaching an Area Under Curve (AUC) of 0.64. Similar rises in mortality were predicted with an AUC of approximately 0.61 at a lead time of 3 weeks. Thus, our metric provided Public Health England with an indication which could be used to plan the response to COVID19 and could possibly be utilized to detect regional anomalies of other pathogens.