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Prediction method of the pandemic trend of COVID-19 based on machine learning |
| REN Jianqiang1,2, CUI Yapeng1,2, NI Shunjiang1,2 |
1. Institute of Public Safety Research, Department of Engineering Physics, Tsinghua University, Beijing 100084, China;
2. Beijing Key Laboratory of City Integrated Emergency Response Science, Beijing 100084, China |
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Abstract [Objective] To estimate and predict the actual infection scale of COVID-19 in a population, a COVID-19 pandemic trend prediction method based on machine learning is proposed. This method uses detection data to predict the development trend of the pandemic and can implicitly consider the impact of prevention and control measures. Additionally, this method can predict the number of confirmed cases in the future and estimate the actual infection scale of COVID-19.[Methods] In this paper, a three-step prediction model based on machine learning (TSPM-ML) is proposed. Machine learning algorithms, such as neural networks, random forest, long short-term memory (LSTM), and sequence to sequence (seq2seq), are introduced into the prediction of the COVID-19 development situation, and the detection data are used to predict the number of people diagnosed and the actual scale of the infection in the future. The TSPM-ML includes three steps: (1) predicting the actual infection scale of COVID-19 based on the detection data, (2) predicting the future development trend of the actual infection scale based on the predicted results of the first step, and (3) predicting the number of people diagnosed in the future based on the actual infection scale obtained in the second step. The TSPM-ML is used to predict the actual pandemic situation in Germany, France, South Korea, the United States, Russia, and Finland.[Results] The largest prediction error is in the United States, with a forecast error of 23.71 per million people, while South Korea has the smallest prediction error of 0.63 per million people. Overall, the prediction results of the TSPM-ML are consistent with the simulation and actual data, and the reliability of the model is verified.[Conclusions] The predicted results are consistent with the actual data, and the TSPM-ML is highly reliable. The prediction results can enable government management departments to more accurately understand the actual development trend of COVID-19 and allocate medical resources more effectively, and provide decision support for COVID-19 prevention and control.
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| Keywords
machine learning
prevention and control measures
epidemic trend prediction
public health emergencies
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Issue Date: 12 May 2023
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2023,
Vol. 63
