Database searches for smell/taste disorders and coronavirus and their relation to COVID-19 cases in Finland 2020: A time series analysis of citizens’ and healthcare professionals’ Internet information seeking using log and register data in assessing model improvement (Preprint)

BACKGROUND Background: The COVID-19 pandemic has prevailed over a year, and log and register data on coronavirus have been tried to model from Internet sources to detect the pandemic. However, many sources comprise unreliable health information on COVID-19 and its symptoms, and platforms cannot characterize the users performing searches. Prior studies have assessed symptom searches from general search engines (Google/Google Trends). Little is known how modelling log data on smell/taste disorders and coronavirus from the dedicated Internet databases used by citizens and healthcare professionals could enhance disease surveillance. Our material and method provide a novel approach to analyze Internet information seeking to detect infectious disease outbreaks. OBJECTIVE Objective: The aim of this study was 1) to assess whether citizens’ and professionals’ searches for smell/taste disorders and coronavirus relate to epidemiological data on COVID-19 cases, and 2) to test negative binomial models whether the inclusion of the case count could improve the model. METHODS Methods: We collected weekly log data on searches related to COVID-19 (smell/taste disorders, coronavirus) during 30/12/2019–30/11/2020 (49 weeks). Two major medical Internet databases in Finland were used: Health Library (HL), a free portal aimed at citizens, and Physician’s Database (PD), widely used among healthcare professionals. Log data from databases were combined with register data on the numbers of COVID-19 cases reported in the Finnish National Infectious Diseases Register. We used negative binomial regression modelling to assess if the case numbers could explain some of the dynamics of searches when plotting Internet searches. RESULTS Results: We found that coronavirus searches drastically increased in HL (0 to 744,113) and in PD (4 to 5,375) prior to the first wave of COVID-19 cases during December 2019 and March 2020. Searches for smell disorders in HL doubled from end of December 2019 to end of March 2020 (2,148 to 4,195), and searches for taste disorders in HL increased from mid-May to end of November (0 to 1,980). Case numbers were significantly associated with smell disorders in HL (P < .001), and with coronavirus searches (P < .001) in PD. We could not identify any other associations between case numbers and searches in either database. CONCLUSIONS Conclusions: Modelling log data from Internet databases was seen to improve the model only occasionally. However, search behaviors among citizens and professionals could be used as a supplementary source of information for infectious disease surveillance. Further research is needed to apply statistical models to log data of the dedicated medical databases. CLINICALTRIAL None