Dataset Information

Symptom-Based Predictive Model of COVID-19 Disease in Children.

ABSTRACT:

Background

Testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is neither always accessible nor easy to perform in children. We aimed to propose a machine learning model to assess the need for a SARS-CoV-2 test in children (<16 years old), depending on their clinical symptoms.

Methods

Epidemiological and clinical data were obtained from the REDCap^® registry. Overall, 4434 SARS-CoV-2 tests were performed in symptomatic children between 1 November 2020 and 31 March 2021, 784 were positive (17.68%). We pre-processed the data to be suitable for a machine learning (ML) algorithm, balancing the positive-negative rate and preparing subsets of data by age. We trained several models and chose those with the best performance for each subset.

Results

The use of ML demonstrated an AUROC of 0.65 to predict a COVID-19 diagnosis in children. The absence of high-grade fever was the major predictor of COVID-19 in younger children, whereas loss of taste or smell was the most determinant symptom in older children.

Conclusions

Although the accuracy of the models was lower than expected, they can be used to provide a diagnosis when epidemiological data on the risk of exposure to COVID-19 is unknown.

SUBMITTER: Antonanzas JM

PROVIDER: S-EPMC8779426 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature

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Publications

Symptom-Based Predictive Model of COVID-19 Disease in Children.

Antoñanzas Jesús M JM Perramon Aida A López Cayetana C Boneta Mireia M Aguilera Cristina C Capdevila Ramon R Gatell Anna A Serrano Pepe P Poblet Miriam M Canadell Dolors D Vilà Mònica M Catasús Georgina G Valldepérez Cinta C Català Martí M Soler-Palacín Pere P Prats Clara C Soriano-Arandes Antoni A The Copedi-Cat Research Group

Viruses 20211230 1

<h4>Background</h4>Testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is neither always accessible nor easy to perform in children. We aimed to propose a machine learning model to assess the need for a SARS-CoV-2 test in children (<16 years old), depending on their clinical symptoms.<h4>Methods</h4>Epidemiological and clinical data were obtained from the REDCap<sup>®</sup> registry. Overall, 4434 SARS-CoV-2 tests were performed in symptomatic children between 1 No ...[more]

PMID: 35062267

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Project description:BackgroundPrevious studies have suggested associations between trends of web searches and COVID-19 traditional metrics. It remains unclear whether models incorporating trends of digital searches lead to better predictions.ObjectiveThe aim of this study is to investigate the relationship between Google Trends searches of symptoms associated with COVID-19 and confirmed COVID-19 cases and deaths. We aim to develop predictive models to forecast the COVID-19 epidemic based on a combination of Google Trends searches of symptoms and conventional COVID-19 metrics.MethodsAn open-access web application was developed to evaluate Google Trends and traditional COVID-19 metrics via an interactive framework based on principal component analysis (PCA) and time series modeling. The application facilitates the analysis of symptom search behavior associated with COVID-19 disease in 188 countries. In this study, we selected the data of nine countries as case studies to represent all continents. PCA was used to perform data dimensionality reduction, and three different time series models (error, trend, seasonality; autoregressive integrated moving average; and feed-forward neural network autoregression) were used to predict COVID-19 metrics in the upcoming 14 days. The models were compared in terms of prediction ability using the root mean square error (RMSE) of the first principal component (PC1). The predictive abilities of models generated with both Google Trends data and conventional COVID-19 metrics were compared with those fitted with conventional COVID-19 metrics only.ResultsThe degree of correlation and the best time lag varied as a function of the selected country and topic searched; in general, the optimal time lag was within 15 days. Overall, predictions of PC1 based on both search terms and COVID-19 traditional metrics performed better than those not including Google searches (median 1.56, IQR 0.90-2.49 versus median 1.87, IQR 1.09-2.95, respectively), but the improvement in prediction varied as a function of the selected country and time frame. The best model varied as a function of country, time range, and period of time selected. Models based on a 7-day moving average led to considerably smaller RMSE values as opposed to those calculated with raw data (median 0.90, IQR 0.50-1.53 versus median 2.27, IQR 1.62-3.74, respectively).ConclusionsThe inclusion of digital online searches in statistical models may improve the nowcasting and forecasting of the COVID-19 epidemic and could be used as one of the surveillance systems of COVID-19 disease. We provide a free web application operating with nearly real-time data that anyone can use to make predictions of outbreaks, improve estimates of the dynamics of ongoing epidemics, and predict future or rebound waves.

Dataset Information

Symptom-Based Predictive Model of COVID-19 Disease in Children.

Background

Methods

Results

Conclusions

Publications

Symptom-Based Predictive Model of COVID-19 Disease in Children.

Similar Datasets

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