COVID-19: Should We Test Everyone?
@article{Yi2020COVID19SW, title={COVID-19: Should We Test Everyone?}, author={Grace Y. Yi and Wenqing He and Dennis K. J. Lin and Chun Ming Yu}, journal={arXiv: Applications}, year={2020} }
Since the beginning of 2020, the coronavirus disease 2019 (COVID-19) has spread rapidly in the city of Wuhan, P.R. China, and subsequently, across the world. The swift spread of the virus is largely attributed to its stealth transmissions in which infected patients may be asymptomatic. Undetected transmissions present a remarkable challenge for the containment of the virus and pose an appalling threat to the public. An urgent question that has been asked by the public is "Should I be tested for…
4 Citations
Predictive values, uncertainty, and interpretation of serology tests for the novel coronavirus
- BiologyScientific reports
- 2021
Calculations, interpretations, and plots of positive and negative predictive values under a variety of scenarios are provided and implications for society overall and across diverse locations with different levels of disease burden are discussed.
Are antibodies tests accurate? Understanding predictive values and uncertainty of serology tests for the novel coronavirus.
- BiologymedRxiv
- 2020
Calculations, interpretations, and plots of positive and negative predictive values under a variety of scenarios are provided and implications for society overall and across diverse locations with different levels of disease burden are discussed.
Internet Search Effort on Covid-19 and the Underlying Public Interventions and Epidemiological Status
- Medicine
- 2021
The relative importance of indices of personal freedom, economy, and epidemiology on the public interest on Covid-19 expressed by internet searches on the topic was quantified and results indicated that the trained model exhibited a fit of R2 = 91% between the actual and predicted web search effort.
How Often Should People be Tested for Corona to Avoid a Shutdown
- Medicine
- 2020
Based on the well known SIR model, a model for predicting the number of necessary testings of asymptomatic persons in order to push Reff below 1, thus suppressing an outbreak is developed.
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