COVID-19 GPH|COVID-19 Genomics and Precision Public Health Weekly Update|PHGKB

COVID-19 GPH|COVID-19 Genomics and Precision Public Health Weekly Update|PHGKB

Non-Genomics Precision Health Studies

• Using influenza surveillance networks to estimate state-specific prevalence of SARS-CoV-2 in the United States.
  Silverman Justin D et al. Science translational medicine 2020 Jun
  The authors estimated the proportion of observed influenza-like illness during the early pandemic that was in excess of the seasonal variation seen in prior years, then adjusted this estimate to take into account subclinical infections. Their model estimated that more than 80% of individuals with SARS-CoV-2 infections in the US went undetected in March 2020.
• Deep transfer learning artificial intelligence accurately stages COVID-19 lung disease severity on portable chest radiographs.
  Zhu Jocelyn et al. PloS one 2020 (7) e0236621
  This study employed deep-learning convolutional neural networks to stage lung disease severity of Coronavirus Disease 2019) infection on portable chest x-ray with radiologist score of disease severity as ground truth. Deep-learning convolutional neural networks accurately stages disease severity on portable chest x-ray of COVID-19 lung infection.
• Drug treatments for covid-19: living systematic review and network meta-analysis
  RAC Siemieniuk et al, BMJ, July 30, 2020
  Glucocorticoids probably reduce mortality and mechanical ventilation in patients with covid-19 compared with standard care. The effectiveness of most interventions is uncertain because most of the randomised controlled trials so far have been small and have important study limitations.
• Characteristics and Outcomes of Contacts of COVID-19 Patients Monitored Using an Automated Symptom Monitoring Tool — Maine, May–June 2020
  A Krueger et al, MMWR, August 3, 2020
  Maine found that using automated symptom monitoring as a part of the state’s contact tracing program was well received, with the majority of monitored contacts (96.4%) agreeing to automated symptom monitoring. Automated symptom monitoring promptly identified COVID-19 diagnoses among monitored contacts.
• Application of Social Vulnerability Index to Identify High- risk Population of Contracting COVID-19 Infection: a state-level study.
  O Estefania et al, MEDRXIV, August 4, 2020
  By applying the social vulnerability index and analyzing data from a total of 102 counties across the state of Illinois, we investigated which factors enhanced the risk of contracting the infection and which were related to a lower risk of infection.