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STAR Protoc ; 3(3): 101648, 2022 09 16.
Article in English | MEDLINE | ID: covidwho-1967230


Here, we describe a bioinformatics pipeline that evaluates the interactions between coagulation-related proteins and genetic variants with SARS-CoV-2 proteins. This pipeline searches for host proteins that may bind to viral protein and identifies and scores the protein genetic variants to predict the disease pathogenesis in specific subpopulations. Additionally, it is able to find structurally similar motifs and identify potential binding sites within the host-viral protein complexes to unveil viral impact on regulated biological processes and/or host-protein impact on viral invasion or reproduction. For complete details on the use and execution of this protocol, please refer to Holcomb et al. (2021).

COVID-19 , SARS-CoV-2 , Binding Sites , COVID-19/genetics , Host Microbial Interactions , Humans , SARS-CoV-2/genetics , Viral Proteins/genetics
STAR Protoc ; 3(2): 101463, 2022 06 17.
Article in English | MEDLINE | ID: covidwho-1886130


Non-pharmacological interventions (NPIs) are important for controlling infectious diseases such as COVID-19, but their implementation is currently monitored in an ad hoc manner. To address this issue, we present a three-stage machine learning framework called EpiTopics to facilitate the surveillance of NPI. In this protocol, we outline the use of transfer-learning to address the limited number of NPI-labeled documents and topic modeling to support interpretation of the results. For complete details on the use and execution of this protocol, please refer to Wen et al. (2022).

COVID-19 , Communicable Diseases , Fluprednisolone/analogs & derivatives , Humans , Machine Learning , Public Health
STAR Protoc ; 2(4): 100943, 2021 12 17.
Article in English | MEDLINE | ID: covidwho-1510407


During the COVID-19 pandemic, US states developed Crisis Standards of Care (CSC) algorithms to triage allocation of scarce resources to maximize population-wide benefit. While CSC algorithms were developed by ethical debate, this protocol guides their quantitative assessment. For CSC algorithms, this protocol addresses (1) adapting algorithms for empirical study, (2) quantifying predictive accuracy, and (3) simulating clinical decision-making. This protocol provides a framework for healthcare systems and governments to test the performance of CSC algorithms to ensure they meet their stated ethical goals. For complete details on the use and execution of this protocol, please refer to Jezmir et al. (2021).

COVID-19/therapy , Critical Care/standards , Health Care Rationing/standards , Practice Guidelines as Topic/standards , Standard of Care/ethics , Triage/standards , COVID-19/virology , Critical Care/ethics , Health Care Rationing/ethics , Humans , SARS-CoV-2/isolation & purification , Triage/ethics , Triage/methods