A Social Media Infodemic-Based Prediction Model for the Number of Severe and Critical COVID-19 Patients in the Lockdown Area

Qi Yan, Siqing Shan, Menghan Sun, Feng Zhao, Yangzi Yang and Yinong Li
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Qi Yan: School of Economics and Management, Beihang University, Beijing 100191, China
Siqing Shan: School of Economics and Management, Beihang University, Beijing 100191, China
Menghan Sun: School of Economics and Management, Beihang University, Beijing 100191, China
Feng Zhao: School of Economics and Management, Beihang University, Beijing 100191, China
Yangzi Yang: School of Economics and Management, Beihang University, Beijing 100191, China
Yinong Li: School of Economics and Management, Beihang University, Beijing 100191, China

IJERPH, 2022, vol. 19, issue 13, 1-16

Abstract: Accurately predicting the number of severe and critical COVID-19 patients is critical for the treatment and control of the epidemic. Social media data have gained great popularity and widespread application in various research domains. The viral-related infodemic outbreaks have occurred alongside the COVID-19 outbreak. This paper aims to discover trustworthy sources of social media data to improve the prediction performance of severe and critical COVID-19 patients. The innovation of this paper lies in three aspects. First, it builds an improved prediction model based on machine learning. This model helps predict the number of severe and critical COVID-19 patients on a specific urban or regional scale. The effectiveness of the prediction model, shown as accuracy and satisfactory robustness, is verified by a case study of the lockdown in Hubei Province. Second, it finds the transition path of the impact of social media data for predicting the number of severe and critical COVID-19 patients. Third, this paper provides a promising and powerful model for COVID-19 prevention and control. The prediction model can help medical organizations to realize a prediction of COVID-19 severe and critical patients in multi-stage with lead time in specific areas. This model can guide the Centers for Disease Control and Prevention and other clinic institutions to expand the monitoring channels and research methods concerning COVID-19 by using web-based social media data. The model can also facilitate optimal scheduling of medical resources as well as prevention and control policy formulation.

Keywords: COVID-19; prediction model; machine learning; sentiment polarity; social media (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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