Ke and Perelson Publish New Model Linking COVID-19 Viral Load to Infectiousness
Ke and Perelson Publish New Model Linking COVID-19 Viral Load to Infectiousness
Dr. Ruian Ke and Dr. Alan Perelson, both from the Los Alamos National Laboratory and New Mexico Consortium, recently published a new study, In vivo kinetics of SARS-CoV-2 infection and its relationship with a person’s infectiousness, linking COVID-19 viral load to infectiousness. This work looks at understanding the impact of drugs and vaccines that lower the viral load of COVID -19 of infected individuals, and also for rapid testing strategies.
COVID-19 is highly contagious and is still spreading, causing more than five million deaths globally as of December 2021. The virus infects cells in the upper respiratory tract, causing high viral load and enabling effective transmission to others.
Increasing our understanding how viral load, infectiousness, and symptom onset are related is extremely important for non-pharmaceutical and pharmaceutical interventions and for predicting the course of the disease.
This study, led by Dr. Ke, looks at estimating key within-host viral dynamic parameters by using much more precise modeling approaches.
They created viral dynamic models of the COVID-19 infection and fit them into data to appraise key within-host parameters, emphasizing within-host reproductive number and infected cell half-life. Then they have developed a model linking viral load to infectiousness.
The results of this study have further included data on antigen and reverse transcription-polymerase chain reaction (RT-PCR) tests and compared their utility in determining infection and preventing transmission.
To read the published article in the Proceedings of the National Academy of Sciences of the United States of America (PNAS), see: In vivo kinetics of SARS-CoV-2 infection and its relationship with a person’s infectiousness
Read the entire article by Gemma Wilson about this work at: A new model linking SARS-CoV-2 viral load to infectiousness may guide further testing strategy.
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