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A multi-layer model for the early detection of COVID-19
Erez Shmueli1,2, Ronen Mansuri1, Matan Porcilan1
1Department of Industrial Engineering, Tel Aviv University, Tel Aviv 69978, Israel.
Journal of the Royal Society, Interface
|August 3, 2021
Summary
This study introduces a machine-learning model for COVID-19 detection using multiple data types. The model accurately predicts test outcomes, aiding in efficient testing and transmission control.
Area of Science:
- Computational epidemiology
- Machine learning in healthcare
- Infectious disease modeling
Background:
- Current COVID-19 screening relies on symptoms and exposure, which can be limiting.
- Accurate and early detection is crucial for controlling viral transmission.
Purpose of the Study:
- To develop and evaluate a machine-learning model for COVID-19 detection.
- To assess the model's predictive performance using diverse data sources.
- To explore the utility of pre-symptomatic data for COVID-19 prediction.
Main Methods:
- A machine-learning model was developed incorporating sociodemographic, spatio-temporal, medical, and self-reported data.
- The model was evaluated on a large dataset of 140,682 individuals undergoing 264,516 COVID-19 PCR tests.
- Performance was measured using the Area Under the Curve (AUC) metric.
Main Results:
- The multi-layer model achieved an AUC of 81.6% for overall prediction.
- The model demonstrated strong performance even in asymptomatic individuals (AUC 72.8%).
- Predicting test outcomes using pre-symptomatic data yielded a high AUC of 79.5%.
Conclusions:
- A novel machine-learning approach effectively predicts COVID-19 test outcomes.
- The model's ability to predict outcomes early, even without symptoms, is valuable.
- This predictive capability can enhance public health strategies for testing and transmission interruption.
