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Hot Topics of the Day|PHGKB
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06/05/2020

Hot Topics of the Day are picked by experts to capture the latest information and publications on public health genomics and precision health for various diseases and health topics. Sources include published scientific literature, reviews, blogs and popular press articles.

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Archived Hot Topics of the Day By Date

The Importance of Long-term Care Populations in Models of COVID-19
K Pillemer et al, JAMA, June 5, 2020

Both modelers and public health policy makers should recognize that COVID-19 is not a unitary epidemic. It likely consists of multiple, contemporaneous, and intertwined sub-outbreaks prominently including those in long term care settings. Just as the outbreaks occurring in these 2 settings are distinct, the responses to them need to differ.

Targeting SARS-CoV-2 Receptors as a Means for Reducing Infectivity and Improving Antiviral and Immune Response: An Algorithm-based Method For Overcoming Resistance To Antiviral Agents.
Gelman Ram et al. Emerging microbes & infections 2020 Jun 1-21

Genetic polymorphisms mediating behavioural and immune response to pathogens may moderate the impact of the COVID-19 pandemic: a pilot study
RP Rakjumar, MEDRXIV< June 4, 2020

A prediction model based on machine learning for diagnosing the early COVID-19 patients
SN Nan et al, MEDRXIV June 4, 2020

Swab Tests and COVID 19, Italy case studied using Artificial Intelligence, Statistical Analysis and MLR
B Pirouz et al, MEDRXIV, June 4, 2020

COVID-19 genomic susceptibility: Definition of ACE2 variants relevant to human infection with SARS-CoV-2 in the context of ACMG/AMP Guidance
CL Shovlin et al, MEDRXIV, June 4, 2020

Risk factors for mortality in patients with Coronavirus disease 2019 (COVID-19) infection: a systematic review and meta-analysis of observational studies
M Parohan et al MEDRXIV, June 4, 2020

14 studies with 29,909 COVID-19 infected patients and 1,445 cases of death were included in the current meta-analysis. Older age (=65 years old), male gender, hypertension, CVDs, diabetes, COPD and malignancies were associated with greater risk of death from COVID-19 infection.

Machine learning in predicting respiratory failure in patients with COVID-19 pneumonia - challenges, strengths, and opportunities in a global health emergency
D Ferrari et al, MEDRXIV, June 3, 2020

Using viral genomics to estimate undetected infections and extent of superspreading events for COVID-19
LM Li et al, MEDRXIV, June 3, 2020

Using a Bayesian inference framework to fit a branching process model to viral phylogeny and time series case data, we estimated time-varying reproductive numbers and their variance, the total numbers of infected individuals, the probability of case detection over time, and the estimated time to detection of an outbreak for 12 locations in Europe, China, and US.

Uncertainty Quantification in Epidemiological Models for COVID-19 Pandemic
L Taghizadeh et al, MEDRXIV, June 3, 2020

HiDRA-seq: High-Throughput SARS-CoV-2 Detection by RNA Barcoding and Amplicon Sequencing
E Yanguez et al, BIORXIV, June 2, 2020

Genes May Leave Some People More Vulnerable to Severe Covid-19: Geneticists have turned up intriguing links between DNA and the disease. Patients with Type A blood, for example, seem to be at greater risk.
C Zimmer, NY Times, June 3, 2020

Tumour predisposition and cancer syndromes as models to study gene–environment interactions
M Carbone et al, Nature Rev Cancer, May 2020

Tumour predisposition syndromes in which cancers arise at an accelerated rate and in different organs — the equivalent of a sensitized background — provide a unique opportunity to examine how gene–environment interactions influence cancer risk when the initiating genetic defect responsible for malignancy is known.


Disclaimer: Articles listed in Hot Topics of the Day are selected by Public Health Genomics Branch to provide current awareness of the scientific 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 Clips, 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.
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