Toward More Precision in Implementation Science in the Age of COVID-19 M Clyne et al, CDC Blog, June 26, 2020

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Toward More Precision in Implementation Science in the Age of COVID-19
M Clyne et al, CDC Blog, June 26, 2020

The COVID-19 pandemic has shed a new light on the importance of implementation science both in delivering the right interventions for the control of the pandemic (direct impact) as well as adapting to the disruption in health services as a result of the pandemic.

Using influenza surveillance networks to estimate state-specific prevalence of SARS-CoV-2 in the United States
JD Silverman et al, Sci Trans Med, June 2020

We show how influenza-like illness (ILI) outpatient surveillance data can be used to estimate the prevalence of SARS-CoV-2. The results suggest a conceptual model for the COVID-19 epidemic in the US characterized by rapid spread across the US with over 80% infected patients remaining undetected.

Surveillance testing of SARS-CoV-2
DB Larremore et al, MEDRXIV, June 25, 2020

Sequencing identifies multiple early introductions of SARS-CoV-2 to the New York City Region
MT Maurano et al, MEDRXIV, June 25, 2020

We report the analysis of 864 SARS-CoV-2 sequences from cases in the New York City in Spring 2020. The majority of cases throughout the region had no recent travel history or known exposure, and genetically linked cases were spread throughout the region. Comparison to global viral sequences showed that early transmission was most linked to cases from Europe.

Using Machine Learning to assess Covid-19 risks
A Prakash, MEDRXIV, June 25, 2020

Integration of viral transcriptome sequencing with structure and sequence motifs predicts novel regulatory elements in SARS-CoV-2
BJ Cox, BIORXIV, June 24, 2020

Using Machine Learning of Clinical Data to Diagnose COVID-19
WT Li et al, MEDRXIV, June 24, 2020

Detection of SARS-Coronavirus-2 in wastewater, using the existing environmental surveillance network: An epidemiological gateway to an early warning for COVID-19 in communities.
S Sharif et al, MEDRXIV, June 24, 2020

Variants in ACE2 and TMPRSS2 genes are not major determinants of COVID-19 severity in UK Biobank subjects
D Curtis, MEDRXIV, June 25, 2020

COVID-19 Mathematical Modeling
CDC Information, June 2020 

Mathematical modeling helps CDC and partners respond to the COVID-19 pandemic by informing decisions about pandemic planning, resource allocation, and implementation of social distancing measures and other interventions.

Evidence used to update the list of underlying medical conditions that increase a person’s risk of severe illness from COVID-19
CDC, June 2020 

This list is a living document that will be periodically updated by CDC, and it could rapidly change as the science evolves. Severe illness from COVID-19 was defined as hospitalization, admission to the ICU, intubation or death. The level of evidence for each condition was determined by CDC reviewers based on available information.