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Predicting the Disease Severity of Virus Infection

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Translational Informatics

Part of the book series: Advances in Experimental Medicine and Biology ((AEMB,volume 1368))

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.

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Acknowledgments

This work was supported by the COVID-19 Research Projects of West China Hospital Sichuan University (Grant no. HX-2019-nCoV-057), the Regional Innovation Cooperation between Sichuan and Guangxi Provinces (2020YFQ0019), National Natural Science Foundation of China (Grant Nos. 32070671, 31900490, 31770903), the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (Grant No. 19KJB180027), 2021 Anhui Provincial Universities Excellent Top-notch Talents Training Program (gxyq2021200), Anhui Science and Technology University Stabilization and Introduction of Talents (XWWD202101).

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  1. **n Qi and Li Shen contributed equally to this work.

Authors and Affiliations

  1. School of Chemistry and Life Sciences, Suzhou University of Science and Technology, Suzhou, China

    **n Qi & Jiajia Chen

  2. Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan, China

    Li Shen & Bairong Shen

  3. College of Information and Network Engineering, Anhui Science and Technology University, Fengyang, China

    Manhong Shi

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  1. **n Qi
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  2. Li Shen
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  3. Jiajia Chen
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  4. Manhong Shi
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  5. Bairong Shen
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Correspondence to **n Qi or Bairong Shen .

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  1. Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan, China

    Bairong Shen

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Qi, X., Shen, L., Chen, J., Shi, M., Shen, B. (2022). Predicting the Disease Severity of Virus Infection. In: Shen, B. (eds) Translational Informatics. Advances in Experimental Medicine and Biology, vol 1368. Springer, Singapore. https://doi.org/10.1007/978-981-16-8969-7_6

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