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Using a simple open-source automated machine learning algorithm to forecast COVID-19 spread: A modelling study
Shahir Asfahan1, Maya Gopalakrishnan1, Naveen Dutt1
1All India Institute of Medical Sciences, Rajasthan, Jodhpur, India.
Advances in Respiratory Medicine
|November 10, 2020
Summary
Machine learning accurately predicted COVID-19 spread in South Korea, with an average 7.42% error. This forecasting tool can aid healthcare resource allocation during pandemics.
Area of Science:
- Epidemiology
- Machine Learning
- Public Health
Background:
- Machine learning models are increasingly used for disease prediction.
- Automated machine learning offers a simplified approach to complex data analysis.
- Understanding COVID-19 dynamics is crucial for pandemic management.
Purpose of the Study:
- To apply an automated machine learning algorithm to predict COVID-19 spread in South Korea.
- To assess the effectiveness of machine learning in forecasting infectious disease dynamics.
- To evaluate the potential of these tools for public health resource allocation.
Main Methods:
- Utilized time-series data from South Korea's Centre for Disease Control (KCDC) from January 20 to March 4, 2020.
- Employed an automated machine learning algorithm (Prophet) for 7-day forecasting.
- Assessed prediction accuracy using Mean Absolute Percentage Error (MAPE).
Main Results:
- Over 145,541 tests conducted, with 5,166 positive COVID-19 cases by March 4, 2020.
- The model achieved a MAPE of 7.42%, with prediction differences ranging from 4.08% to 12.77%.
- Predicted values showed good approximation to observed COVID-19 case numbers.
Conclusions:
- Automated machine learning tools like Prophet are effective for forecasting COVID-19 spread.
- Accurate forecasting can assist countries in efficient healthcare resource allocation.
- Machine learning provides valuable insights for managing infectious disease outbreaks.
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