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At the end of 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a novel human coronavirus, emerged and rapidly caused a global pandemic. SARS-CoV-2 is the causative agent of coronavirus disease 2019 (COVID-19), which affects the respiratory tract and lungs of infected individuals. Due to the increased transmissibility of the SARS-CoV-2 virus compared to its previous versions, determining as fully as possible the various structural aspects of the virus became critical for the development of therapeutics and vaccines to combat this virus. Knowing the structures of viral proteins and their glycosylation is an essential foundation for the understanding of the mechanism of the disease. Glycopeptide analysis has been used to map the glycosylation of viral glycoproteins, including those of influenza and HIV. Thanks to the developments in the field over the last few decades, scientists were able to quickly develop therapeutics against SARS-CoV-2. This chapter discusses the four structural proteins of SARS-CoV-2, their glycosylation and modifications, and the techniques used to map SARS-CoV-2 glycosylation.
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Statistical Network Analysis: A Review with Applications to the Coronavirus Disease 2019 Pandemic
Summary As the coronavirus disease 2019 outbreak evolves, statistical network analysis is playing an essential role in informing policy decisions. Therefore, researchers who are new to such studies need to understand the techniques available to them. As a field, statistical network analysis aims to develop methods that account for the complex dependencies found in network data. Over the last few decades, the area has rapidly accumulated methods, including techniques for network modelling and simulating the spread of infectious disease. This article reviews these network modelling techniques and their applications to the coronavirus disease 2019 pandemic.
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- PAR ID:
- 10453811
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- International Statistical Review
- Volume:
- 88
- Issue:
- 2
- ISSN:
- 0306-7734
- Format(s):
- Medium: X Size: p. 419-440
- Size(s):
- p. 419-440
- Sponsoring Org:
- National Science Foundation
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