Selected article for: "case data and CFR estimate"
Author: Timothy W Russell; Joel Hellewell; Christopher I Jarvis; Kevin van-Zandvoort; Sam Abbott; Ruwan Ratnayake; Stefan Flasche; Rosalind M Eggo; Adam J Kucharski
Title: Estimating the infection and case fatality ratio for COVID-19 using age-adjusted data from the outbreak on the Diamond Princess cruise ship
  • Document date: 2020_3_8
    • ID: hwqgotl2_2
    Snippet: Adjusting for outcome delay in CFR estimates During an outbreak, the so-called naive CFR (nCFR), i.e. the ratio of reported deaths date to reported cases to date, will underestimate the true CFR because the outcome (recovery or death) is not known for all cases [4, 5] . We can therefore estimate the true denominator for the CFR (i.e. the number of cases with known outcomes) by accounting for the delay from confirmation-to-death [5] . We assumed t.....
    Document: Adjusting for outcome delay in CFR estimates During an outbreak, the so-called naive CFR (nCFR), i.e. the ratio of reported deaths date to reported cases to date, will underestimate the true CFR because the outcome (recovery or death) is not known for all cases [4, 5] . We can therefore estimate the true denominator for the CFR (i.e. the number of cases with known outcomes) by accounting for the delay from confirmation-to-death [5] . We assumed the delay from confirmation-to-death followed the same distribution as estimated hospitalisation-to-death, based on data from the COVID-19 outbreak in Wuhan, China, between the 17 th December 2019 and the 22 th January 2020, accounting right-censoring in the data as a result of as-yet-unknown disease outcomes ( Figure 1 , panels A and B) [6] . As a sensitivity analysis, we also consider raw "non-truncated" distributions, which do not account for censoring; the raw and truncated distributions have a mean of 8.6 days and 13 days respectively. To correct the CFR, we use the case and death incidence data to estimate the number of cases with known outcomes [5] :

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