Browsing by Author "Jägerroos, Vilma"

Abstract Background: To address the current COVID-19 and any future pandemic, we need a robust, real-time, and population-scale collection and analysis of data. Rapid and comprehensive knowledge on the trends in reported symptoms in populations provides an earlier window into the progression of the viral spread and helps to predict the needs and timing of professional healthcare. Objective: The objective of this study was to use a CE-marked medical online symptom checker service, ©Omaolo, and validate the data against the national demand for COVID- 19-related care to predict the pandemic progression in Finland. Methods: Our data comprised real-time ©Omaolo COVID-19 symptom checker responses (414,477 in total) and daily admission counts in nationwide inpatient and outpatient registers provided by the Finnish Institute for Health and Welfare (THL) from March 16th to June 15th, 2020 (the first wave of the pandemic in Finland). The symptom checker responses provide self-triage information input to a medically qualified algorithm that produces a personalised probability of having COVID-19, and provides graded recommendations for further actions. We trained linear regression and XGBoost models together with F-score and mutual information feature pre-selectors to predict the admissions once a week, one week in advance. Results: Our models reached a MAPE (mean absolute percentage error) between 24.2% and 36.4% in predicting the national daily patient admissions. The best result was achieved by combining both ©Omaolo and historical patient admission counts. Our best predictor was linear regression with mutual information as the feature pre-selector. Conclusions: Accurate short-term predictions of COVID-19 patient admissions can be made, and both the symptom check questionnaires and the daily admissions data contribute to the accuracy of the predictions. Thus, symptom checkers can be used to estimate the progression of the pandemic, which can be considered when predicting the healthcare burden in a future pandemic.