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COVID-19 GPH|Home|PHGKB Last data update: May 25, 2022 . (Total: 35799 Documents since 2020)

Last Posted: May-25-2022 07:47:27
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COVID-19 vaccine effectiveness against the omicron (BA.2) variant in England
FCM Kiresbom et al, Lancet Inf Dis, May 24, 2022

Overall, we found no reduction in vaccine effectiveness against symptomatic disease with BA.2 compared with BA.1. As previously observed, we found that vaccine effectiveness wanes over time, but there was no difference in the rate of decline between the two sub-lineages. Due to the small number of people with BA.2, we were not able to stratify by manufacturer in these analyses; however, we found little difference according to vaccine after a third dose in previous analyses of the omicron variant.

Further antibody escape by Omicron BA.4 and BA.5 from vaccine and BA.1 serum
A Tuekrakhon et al, BIORXIV, May 23, 2022

Here, we study the neutralization of BA.4/5 using a range of vaccine and naturally immune serum and panels of monoclonal antibodies. BA.4/5 shows reduced neutralization by serum from triple AstraZeneca or Pfizer vaccinated individuals compared to BA.1 and BA.2. Furthermore, using serum from BA.1 vaccine breakthrough infections there are likewise, significant reductions in the neutralization of BA.4/5, raising the possibility of repeat Omicron infections.

Aerosol particle emission increases exponentially above moderate exercise intensity resulting in superemission during maximal exercise
B Mutsch et al, PNAS, May 23, 2022

Airborne transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) or other pathogens is probably increased during indoor exercise, but data on the emission of aerosol particles by an exercising individual are lacking. Here, we report that aerosol particle emission increases on average 132-fold from 580 ± 489 particles/min at rest to 76,200 ± 48,000 particles/min during maximal exercise. Aerosol particle emission increases moderately up to an exercise intensity of ˜2 W/kg and exponentially at higher exercise intensities. These data not only explain SARS-CoV-2 transmissions during indoor group exercise but also can be used to design better targeted mitigation measures for physical activity indoors such as physical education in school, dance events during weddings, or high-intensity gym classes such as spinning.

Analysis of 6.4 million SARS-CoV-2 genomes identifies mutations associated with fitness
F Obermeyer et al, Science, May 24, 2022

Repeated emergence of SARS-CoV-2 variants with increased fitness underscores the value of rapid detection and characterization of new lineages. We have developed PyR0, a hierarchical Bayesian multinomial logistic regression model that infers relative prevalence of all viral lineages across geographic regions, detects lineages increasing in prevalence, and identifies mutations relevant to fitness. Applying PyR0 to all publicly available SARS-CoV-2 genomes, we identify numerous substitutions that increase fitness, including previously identified spike mutations and many non-spike mutations within the nucleocapsid and nonstructural proteins. PyR0 forecasts growth of new lineages from their mutational profile, ranks the fitness of lineages as new sequences become available, and prioritizes mutations of biological and public health concern for functional characterization.

news Latest News and Publications
Novel photonic methods for diagnosis of SARS-CoV-2 infection.
Joshi Naveen, et al. Translational biophotonics 2022 0 0. (1-2) e202200001

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A deep reinforcement learning based decision-making approach for avoiding crowd situation within the case of Covid'19 pandemic.
Abdallah Wejden, et al. Computational intelligence 2022 0 0. (2) 416-437

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A machine learning approach to differentiate between COVID-19 and influenza infection using synthetic infection and immune response data.
Farhang-Sardroodi Suzan, et al. Mathematical biosciences and engineering : MBE 2022 0 0. (6) 5813-5831

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A new hybrid ensemble machine-learning model for severity risk assessment and post-COVID prediction system.
Shakhovska Natalya, et al. Mathematical biosciences and engineering : MBE 2022 0 0. (6) 6102-6123

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Bayesian imputation of COVID-19 positive test counts for nowcasting under reporting lag.
Jersakova Radka, et al. Journal of the Royal Statistical Society. Series C, Applied statistics 2022 0 0.

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COVID-19 Mobile Health Apps: An Overview of Mobile Applications in Indonesia.
Sujarwoto Sujarwoto, et al. Frontiers in public health 2022 0 0. 879695

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Pandemic lockdown, isolation, and exit policies based on machine learning predictions.
Evgeniou Theodoros, et al. Production and operations management 2022 0 0.

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SMD-YOLO: An efficient and lightweight detection method for mask wearing status during the COVID-19 pandemic.
Han Zhenggong, et al. Computer methods and programs in biomedicine 2022 0 0. 106888

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Who are the 'silent spreaders'?: contact tracing in spatio-temporal memory models.
Hu Yue, et al. Neural computing & applications 2022 0 0. 1-21

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Telemedicine and artificial intelligence to support self-isolation of COVID-19 patients: Recent updates and challenges.
Huang Jessica A, et al. Digital health 2022 0 0. 20552076221100634

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About COVID-19 GPH

COVID-19 GPH is an online, continuously updated, searchable database of published scientific literature, CDC and NIH resources, and other materials that capture emerging discoveries and applications of genomics, molecular and other precision medicine and precision public health tools in the investigation and control of COVID-19. Contents include PubMed records via an automated pubmed search algorithm, preprint records from NIH iCite, the relevant information from many media sources picked by experts, and linkages to contents from our curated PHGKB databases.
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All Records35799
Genomics Precision Health25865
Non-Genomics Precision Health9934

Publication Categories Brand

Variants 8746
Vaccines 7962
Mechanism 7650
Treatment 7280
Diagnosis 5244
Prevention 4209
Surveillance 3036
Forecasting 2773
Transmission 1613
Health Equity 916

Publication Types

PubMed Records26292
Online News/Reports/Publications432

Genomics Precision Health (GPH): The use of pathogen and human genomics and advanced molecular detection methods in discovery, clinical and public health investigations and response to COVID-19.
Non Genomics Precision Health (non-GPH): The use of big data, data science, digital health, machine learning and predictive analytic methods (not involving genomics) in discovery, clinical and public health investigations and response to COVID-19

Following categories are annotated by LitCovid from NCBI NIH.
MechanismUnderlying cause(s) of covid-19 infections and transmission & possible drug mechanism of action
Transmission Characteristics and modes of covid-19 transmissions, such as human-to-human
DiagnosisDisease assessment through symptoms, test results, and radiological features
PreventionPrevention, control, response and management strategies
Case ReportDescriptions of specific patient cases
ForecastingModelling and estimating the trend of covid-19 spread

Following categories are annotated by the text mining tool from CDC PHGKB
Health EquityRelevant to health equity. Search terms are derived from a list provided by the Association for Territorial Health Officials which include terms such as diversity, health disparities, and others.
VaccineRelevant to vaccine development, evaluation, implementation and impact. For additional information on vaccines and COVID-19. Check out general CDC Information pages
VariantRelevant to SARS-CoV-2 variants and their impact on public health. For additional information on variants COVID-19. Check out CDC New Variants of the Virus that Causes COVID-19 page
SurveillanceRelevant to SARS-CoV-2 public health surveillance and tracking. For additional information on COVID-19 surveillance, check out CDC COVID-19 Data Tracker

Disclaimer: Articles listed in the Public Health Genomics and Precision Health Knowledge Base are selected by the CDC Office of Public Health Genomics to provide current awareness of the literature and news. Inclusion in the update does not necessarily represent the views of the Centers for Disease Control and Prevention nor does it imply endorsement of the article's methods or findings. CDC and DHHS assume no responsibility for the factual accuracy of the items presented. The selection, omission, or content of items does not imply any endorsement or other position taken by CDC or DHHS. Opinion, findings and conclusions expressed by the original authors of items included in the update, or persons quoted therein, are strictly their own and are in no way meant to represent the opinion or views of CDC or DHHS. References to publications, news sources, and non-CDC Websites are provided solely for informational purposes and do not imply endorsement by CDC or DHHS.