Forecast of the COVID-19 Epidemic Based on RF-BOA-LightGBM.

Zhe Li,Dehua Hu

doi:10.3390/healthcare9091172

Abstract

In this paper, we utilize the Internet big data tool, namely Baidu Index, to predict the development trend of the new coronavirus pneumonia epidemic to obtain further data. By selecting appropriate keywords, we can collect the data of COVID-19 cases in China between 1 January 2020 and 1 April 2020. After preprocessing the data set, the optimal sub-data set can be obtained by using random forest feature selection method. The optimization results of the seven hyperparameters of the LightGBM model by grid search, random search and Bayesian optimization algorithms are compared. The experimental results show that applying the data set obtained from the Baidu Index to the Bayesian-optimized LightGBM model can better predict the growth of the number of patients with new coronary pneumonias, and also help people to make accurate judgments to the development trend of the new coronary pneumonia.

Highlights

During the outbreak of infectious diseases, social media is usually the most active platform for the exchange of information on infectious disease, and the information released is often of good real-time
The experimental results show that applying the data set obtained from the Baidu Index to the Bayesian-optimized LightGBM model can better predict the growth of the number of patients with new coronary pneumonias, and help people to make accurate judgments to the development trend of the new coronary pneumonia
School of Public Health, Sun Yat-sen University, and others studied the use of Internet search queries or social media data to monitor the temporal and spatial trends of the Avian Influenza (H7N9) in China, and the results show that the number of H7N9 cases is positively correlated with Baidu Index and Weibo Index search results in space and time [2]

Summary

Introduction

During the outbreak of infectious diseases, social media is usually the most active platform for the exchange of information on infectious disease, and the information released is often of good real-time. L. Lu et al used Baidu index and micro-index to conduct a comparative study on influenza surveillance in China [1].

Methods

Results

Conclusion