Background Healthcare professionals (HCPs) search medical information during their clinical work using Internet sources. In Finland, Physician's Databases (PD) serve as an Internet medical portal aimed at HCPs. Influenza epidemics appear seasonal outbreaks causing public health concern. Oseltamivir can be used to treat influenza. Little is known about HCPs’ queries on oseltamivir and influenza from dedicated online medical portals and whether queries could be used as an additional source of information for disease surveillance when detecting influenza epidemics. Methods We compared HCPs’ queries on oseltamivir and influenza from PD to influenza diagnoses from the primary healthcare register in Finland 2011‐2016. The Moving Epidemic Method (MEM) calculated the starts of influenza epidemics. Laboratory reports of influenza A and influenza B were assessed. Paired differences compared queries, diagnoses, and laboratory reports by using starting weeks. Kendall's correlation test assessed the season‐to‐season similarity. Results We found that PD and the primary healthcare register showed visually similar patterns annually. Paired differences in the mean showed that influenza epidemics based on queries on oseltamivir started earlier than epidemics based on diagnoses by −0.80 weeks (95% CI: −1.0, 0.0) with high correlation ( τ = 0.943). Queries on influenza preceded queries on oseltamivir by −0.80 weeks (95% CI: −1.2, 0.0) and diagnoses by −1.60 weeks (95% CI: −1.8, −1.0). Conclusions HCPs’ queries on oseltamivir and influenza from Internet medical databases correlated with register diagnoses of influenza. Therefore, they should be considered as a supplementary source of information for disease surveillance when detecting influenza epidemics.

Background The COVID-19 pandemic has prevailed over a year, and log and register data on coronavirus have been utilized to establish models for detecting the pandemic. However, many sources contain 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 about how modeling log data on smell/taste disorders and coronavirus from the dedicated internet databases used by citizens and health care professionals (HCPs) could enhance disease surveillance. Our material and method provide a novel approach to analyze web-based information seeking to detect infectious disease outbreaks. 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 our negative binomial regression modeling (ie, whether the inclusion of the case count could improve the model). Methods We collected weekly log data on searches related to COVID-19 (smell/taste disorders, coronavirus) between December 30, 2019, and November 30, 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), a database widely used among HCPs. 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 modeling to assess whether the case numbers could explain some of the dynamics of searches when plotting database logs. Results We found that coronavirus searches drastically increased in HL (0 to 744,113) and PD (4 to 5375) prior to the first wave of COVID-19 cases between December 2019 and March 2020. Searches for smell disorders in HL doubled from the end of December 2019 to the end of March 2020 (2148 to 4195), and searches for taste disorders in HL increased from mid-May to the end of November (0 to 1980). Case numbers were significantly associated with smell disorders (P<.001) and taste disorders (P<.001) in HL, and with coronavirus searches (P<.001) in PD. We could not identify any other associations between case numbers and searches in either database. Conclusions Novel infodemiological approaches could be used in analyzing database logs. Modeling log data from web-based sources 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.

Introduction Health care professionals working in primary and specialized care typically search for medical information from Internet sources. In Finland, Physician’s Databases are online portals aimed at professionals seeking medical information. As dosage errors may occur when prescribing medication to children, professionals’ need for reliable medical information has increased in public health care centers and hospitals. Influenza continues to be a public health threat, with young children at risk of developing severe illness and easily transmitting the virus. Oseltamivir is used to treat children with influenza. The objective of this study was to compare searches for children’s oseltamivir and influenza diagnoses in primary and specialized care, and to determine if the searches could aid detection of influenza outbreaks. Methods We compared searches in Physician’s Databases for children’s oral suspension of oseltamivir (6 mg/mL) for influenza diagnoses of children under 7 years and laboratory findings of influenza A and B from the National Infectious Disease Register. Searches and diagnoses were assessed in primary and specialized care across Finland by season from 2012–2016. The Moving Epidemic Method (MEM) calculated seasonal starts and ends, and paired differences in the mean compared two indicators. Correlation was tested to compare seasons. Results We found that searches and diagnoses in primary and specialized care showed visually similar patterns annually. The MEM-calculated starting weeks in searches appeared mainly in the same week. Oseltamivir searches in primary care preceded diagnoses by −1.0 weeks (95% CI: −3.0, −0.3; p = 0.132) with very high correlation (τ = 0.913). Specialized care oseltamivir searches and diagnoses correlated moderately (τ = 0.667). Conclusion Health care professionals’ searches for children’s oseltamivir in online databases linked with the registers of children’s influenza diagnoses in primary and specialized care. Therefore, database searches should be considered as supplementary information in disease surveillance when detecting influenza epidemics.

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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