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. 2022:1368:111-139.
doi: 10.1007/978-981-16-8969-7_6.

Predicting the Disease Severity of Virus Infection

Xin Qi #  1 , Li Shen #  2 , Jiajia Chen  3 , Manhong Shi  4 , Bairong Shen  5
Affiliations

Predicting the Disease Severity of Virus Infection

Xin Qi et al. Adv Exp Med Biol. 2022.

Abstract

The COVID-19 pandemic has resulted in unprecedented burden on global health and economic systems, promoting worldwide efforts to understand, control, and fight the disease. Due to the wide spectrum of clinical severity, effective risk factors, biomarkers, and models for predicting disease severity and mortality in COVID-19 patients are urgently needed to provide guidance for clinical intervention and management. In this chapter, we first describe the infection features of different COVID-19 strains and the potential of clinical features, cytokine storm and biomarkers in predicting the severity of COVID-19 patients. We focus on how scoring systems, mathematical models and artificial intelligence (AI)-based models can promote the classification of COVID-19 severity at the population or individual level. Moreover, the development perspective of biomarkers and models for predicting the severity of COVID-19 is prospected. Therefore, this chapter highlights the clinical significance of biomarkers and models related to COVID-19 severity and provides important clues for improving the outcomes of COVID-19 patients, thereby facilitating timely disease assessment and precision medicine for individual COVID-19 patients.

Keywords: AI-based model; Biomarker; COVID-19 severity; Cytokine storm; Mathematical model; Risk factor; Scoring system.

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