Impact of a CE-marked medical software sensor on SARS-CoV-2 disease pandemic progression estimation
| Autor: | K. M. Antila, L. Soininen, T. Lallukka, R. Kaikkonen, H. Nordlund, K. Vepsäläinen, Vesa Jormanainen, V. Jägerroos, L. Limingoja |
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| Rok vydání: | 2021 |
| Předmět: |
Estimation
medicine.medical_specialty business.industry Public health Public Health Environmental and Occupational Health Disease computer.software_genre medicine.disease Mean absolute percentage error Health care Pandemic Medical software medicine Medical emergency business computer Predictive modelling |
| Zdroj: | European Journal of Public Health. 31 |
| ISSN: | 1464-360X 1101-1262 |
| Popis: | Background To address the current and any future pandemic, we need a robust, real-time and population-scale collection of data. Rapid and comprehensive knowledge on the trends in reported symptoms in populations provides an earlier window into our progress against the viral spread, and helps predict the need and timing of professional healthcare. We thus used a CE-marked medical online symptom checker service and validated the data against national demand of care to estimate the progression of the pandemic 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 16th March-15th June, 2020 (the first wave of the pandemic in Finland). The questionnaires responses provide self-triage information input to a medically qualified algorithm that produces a personalized probability of having the 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 1 week ahead. Results Our models reached a MAPE (Mean Absolute Percentage Error) between 24.2% and 36.4% and MdAPE (Median Average Percentage Error) between 20.1% and 25.1% in predicting 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 pre-selector with slight advantage over XGBoost. 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, symptoms checkers can be used to estimate the progression of the pandemic, which can be considered when predicting the health care burden in a future pandemic. Key messages Digital surveillance of symptoms enables rapid and contactless tracing of epidemic progression. Prediction models can help support medical decision making in an epidemic to promote and protect public health and prevent health care system overload. |
| Databáze: | OpenAIRE |
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