domingo, 19 de abril de 2020

Stanford Medicine scientists hope to use data from wearable devices to predict illness, including COVID-19 Stanford University, April 14, 2020

Hot Topics of the Day|PHGKB
Coronavirus
COVID-19 test characteristics including sensitivity, specificity, and diagnostic yield are critical for understanding the risk of false-negatives in the context of community transmission and variable clinical symptoms.
We used estimates of seasonality, immunity, and cross-immunity for betacoronaviruses OC43 and HKU1 from time series data from the USA to inform a model of SARS-CoV-2 transmission. We projected that recurrent wintertime outbreaks of SARS-CoV-2 will probably occur after the initial, most severe pandemic wave.
Prediction models for diagnosis and prognosis in Covid-19
M Sperrin et al, BMJ Editorial, April 14, 2020
In just over three months, there were 27 studies with 31 models. This number highlights the importance of publishing the systematic review as a living review—continually updated as evidence mounts. Unfortunately, the review demonstrates that the quality of the identified models is uniformly poor and none can be recommended for clinical use.
Real-time reverse transcriptase polymerase chain reaction–based assays performed in a laboratory on respiratory specimens are the reference standard for COVID-19 diagnostics. However, point-of-care technologies and serologic immunoassays are rapidly emerging. Although excellent tools exist for the diagnosis of symptomatic patients in well-equipped laboratories.

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