Selected article for: "generation interval and interval distribution"

Author: Tapiwa Ganyani; Cecile Kremer; Dongxuan Chen; Andrea Torneri; Christel Faes; Jacco Wallinga; Niel Hens
Title: Estimating the generation interval for COVID-19 based on symptom onset data
  • Document date: 2020_3_8
  • ID: cq6mivr9_48
    Snippet: is the (which was not peer-reviewed) The copyright holder for this preprint As expected, the proportion of pre-symptomatic transmission increases from 48% (95%CI 32-67%) in the baseline scenario to 66% (95%CI 45-84%) when allowing for negative serial intervals, for the Singapore data, and from 62% (95%CI 50-76%) to 77% (95%CI 65-87%) for the Tianjin data. Hence, a substantial proportion of transmission appears to occur before symptom onset, which.....
    Document: is the (which was not peer-reviewed) The copyright holder for this preprint As expected, the proportion of pre-symptomatic transmission increases from 48% (95%CI 32-67%) in the baseline scenario to 66% (95%CI 45-84%) when allowing for negative serial intervals, for the Singapore data, and from 62% (95%CI 50-76%) to 77% (95%CI 65-87%) for the Tianjin data. Hence, a substantial proportion of transmission appears to occur before symptom onset, which is an important point to consider when planning intervention strategies. We also estimated R 0 , solely to illustrate the bias that occurs when using the serial interval as a proxy for the generation interval [7] . Whereas the impact was limited for our analyses, estimates based on the generation interval are larger and should be preferred to inform intervention policies. Indeed, as expected, the reproduction number was underestimated when using the serial interval distribution which is more variable than the generation interval distribution.

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