Enhanced predictability and interpretability of COVID-19 severity based on SARS-CoV-2 genomic diversity: a comprehensive study encompassing four years of data DOI Creative Commons
Miao Miao,

Yonghong Ma,

Jiao Tan

и другие.

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Ноя. 6, 2024

Despite the end of global Coronavirus Disease 2019 (COVID-19) pandemic, risk factors for COVID-19 severity continue to be a pivotal area research. Specifically, studying impact genomic diversity Severe Acute Respiratory Syndrome 2 (SARS-CoV-2) on is crucial predicting severe outcomes. Therefore, this study aimed investigate SARS-CoV-2 genome sequence, genotype, patient age, gender, and vaccination status COVID-19, develop accurate robust prediction models. The training set (n = 12,038), primary testing 4,006), secondary 2,845) consist sequences with information, which were obtained from Global Initiative Sharing all Individual Data (GISAID) spanning over four years. Four machine learning methods employed construct By extracting features, optimizing model parameters, integrating models, improved accuracy. Furthermore, Shapley Additive exPlanes (SHAP) was applied analyze interpretability identify factors, providing insights management cases. proposed ensemble achieved an F-score 88.842% Area Under Curve (AUC) 0.956 dataset. In addition such as status, 40 amino acid site mutation characteristics identified have significant COVID-19. This work has potential facilitate early identification patients high risks illness, thus effectively reducing rates cases mortality.

Язык: Английский

From Wuhan to Omicron K.P2 strain: A comprehensive review of SARS-CoV-2 phylogeny and public health implications of the latest booster vaccine DOI Creative Commons
Adewunmi Akingbola, Olajumoke Adewole, Adegbesan Abiodun Christopher

и другие.

Human Vaccines & Immunotherapeutics, Год журнала: 2025, Номер 21(1)

Опубликована: Апрель 11, 2025

The SARS-CoV-2 virus continues to evolve, with the Omicron KP.2 variant, a descendant of BA.2.86, emerging as public health concern due its rapid spread and resistance existing immunity. This review examines phylogenetic evolution SARS-CoV-2, focusing on key mutations (R346T, F456L, V1104L), alongside epidemiological implications. It also discusses development approval KP.2-adapted booster vaccine, shown in clinical trials significantly enhance immune responses protect against symptomatic severe disease, particularly vulnerable groups. Despite vaccine advancements, challenges global distribution inequity persist, especially low- middle-income countries, increasing risk vaccine-resistant variants. manuscript underscores importance equitable access control pandemic prevent future outbreaks, while highlighting need for continuous surveillance broader-spectrum research evolves.

Язык: Английский

Процитировано

0
Enhanced predictability and interpretability of COVID-19 severity based on SARS-CoV-2 genomic diversity: a comprehensive study encompassing four years of data DOI Creative Commons
Miao Miao,

Yonghong Ma,

Jiao Tan

и другие.

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Ноя. 6, 2024

Despite the end of global Coronavirus Disease 2019 (COVID-19) pandemic, risk factors for COVID-19 severity continue to be a pivotal area research. Specifically, studying impact genomic diversity Severe Acute Respiratory Syndrome 2 (SARS-CoV-2) on is crucial predicting severe outcomes. Therefore, this study aimed investigate SARS-CoV-2 genome sequence, genotype, patient age, gender, and vaccination status COVID-19, develop accurate robust prediction models. The training set (n = 12,038), primary testing 4,006), secondary 2,845) consist sequences with information, which were obtained from Global Initiative Sharing all Individual Data (GISAID) spanning over four years. Four machine learning methods employed construct By extracting features, optimizing model parameters, integrating models, improved accuracy. Furthermore, Shapley Additive exPlanes (SHAP) was applied analyze interpretability identify factors, providing insights management cases. proposed ensemble achieved an F-score 88.842% Area Under Curve (AUC) 0.956 dataset. In addition such as status, 40 amino acid site mutation characteristics identified have significant COVID-19. This work has potential facilitate early identification patients high risks illness, thus effectively reducing rates cases mortality.

Язык: Английский

Процитировано

0