tetano
Editor, Senior Moderator
Elife
. 2022 Aug 1;11:e79134.
doi: 10.7554/eLife.79134. Online ahead of print.
The unmitigated profile of COVID-19 infectiousness
Ron Sender[SUP] 1 [/SUP], Yinon Bar-On[SUP] 1 [/SUP], Sang Woo Park[SUP] 2 [/SUP], Elad Noor[SUP] 1 [/SUP], Jonathan Dushoff[SUP] 3 [/SUP], Ron Milo[SUP] 1 [/SUP]
Affiliations
Abstract
Quantifying the temporal dynamics of infectiousness of individuals infected with SARS-CoV-2 is crucial for understanding the spread of COVID-19 and for evaluating the effectiveness of mitigation strategies. Many studies have estimated the infectiousness profile using observed serial intervals. However, statistical and epidemiological biases could lead to underestimation of the duration of infectiousness. We correct for these biases by curating data from the initial outbreak of the pandemic in China (when mitigation was minimal), and find that the infectiousness profile of the original strain is longer than previously thought. Sensitivity analysis shows our results are robust to model structure, assumed growth rate and potential observational biases. Although unmitigated transmission data is lacking for variants of concern (VOC), previous analyses suggest that the alpha and delta variants have faster within-host kinetics, which we extrapolate to crude estimates of variant-specific unmitigated generation intervals. Knowing the unmitigated infectiousness profile of infected individuals can inform estimates of the effectiveness of isolation and quarantine measures. The framework presented here can help design better quarantine policies in early stages of future epidemics.
Keywords: computational biology; epidemiology; global health; human; systems biolo
. 2022 Aug 1;11:e79134.
doi: 10.7554/eLife.79134. Online ahead of print.
The unmitigated profile of COVID-19 infectiousness
Ron Sender[SUP] 1 [/SUP], Yinon Bar-On[SUP] 1 [/SUP], Sang Woo Park[SUP] 2 [/SUP], Elad Noor[SUP] 1 [/SUP], Jonathan Dushoff[SUP] 3 [/SUP], Ron Milo[SUP] 1 [/SUP]
Affiliations
- PMID: 35913120
- DOI: 10.7554/eLife.79134
Abstract
Quantifying the temporal dynamics of infectiousness of individuals infected with SARS-CoV-2 is crucial for understanding the spread of COVID-19 and for evaluating the effectiveness of mitigation strategies. Many studies have estimated the infectiousness profile using observed serial intervals. However, statistical and epidemiological biases could lead to underestimation of the duration of infectiousness. We correct for these biases by curating data from the initial outbreak of the pandemic in China (when mitigation was minimal), and find that the infectiousness profile of the original strain is longer than previously thought. Sensitivity analysis shows our results are robust to model structure, assumed growth rate and potential observational biases. Although unmitigated transmission data is lacking for variants of concern (VOC), previous analyses suggest that the alpha and delta variants have faster within-host kinetics, which we extrapolate to crude estimates of variant-specific unmitigated generation intervals. Knowing the unmitigated infectiousness profile of infected individuals can inform estimates of the effectiveness of isolation and quarantine measures. The framework presented here can help design better quarantine policies in early stages of future epidemics.
Keywords: computational biology; epidemiology; global health; human; systems biolo