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Updated: Oct 3, 2025

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Impact of a Conformité Européenne (CE) Certification-Marked Medical Software Sensor on COVID-19 Pandemic Progression

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Summary

Online symptom checkers, like Omaolo, can accurately predict COVID-19 hospital admissions one week in advance. Combining symptom data with patient admission counts enhances pandemic progression predictions and healthcare burden assessments.

Keywords:
COVID-19COVID-19 forecastingadmission datadigital healthhealth carehealth datahealth technologyhealth technology assessmentmachine learningonline symptom checkerpandemicviral spread

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Area of Science:

  • Epidemiology
  • Public Health
  • Health Informatics

Background:

  • Robust data collection is crucial for managing COVID-19 and future pandemics.
  • Real-time symptom data offers early insights into viral spread and healthcare needs.

Purpose of the Study:

  • To validate the Omaolo (CE-marked medical online symptom checker) data against national COVID-19 care demand.
  • To predict pandemic progression in Finland using symptom checker data.

Main Methods:

  • Utilized 414,477 real-time Omaolo COVID-19 symptom checker responses (March-June 2020).
  • Integrated data with daily inpatient/outpatient admission counts from Finnish Institute for Health and Welfare.
  • Trained linear regression and XGBoost models with feature preselection to predict admissions one week ahead.

Main Results:

  • Models achieved a mean absolute percentage error between 24.2% and 36.4% in predicting national daily patient admissions.
  • The best prediction accuracy was obtained by combining Omaolo data with historical patient admission counts.
  • Linear regression with mutual information feature preselection yielded the optimal predictive model.

Conclusions:

  • Accurate short-term predictions of COVID-19 patient admissions are feasible using symptom checker data.
  • Both symptom checker questionnaires and daily admission data improve prediction accuracy.
  • Symptom checkers can effectively estimate pandemic progression and inform healthcare burden predictions for future pandemics.