Literature DB >> 32473660

COVID-19 and the difficulty of inferring epidemiological parameters from clinical data - Authors' reply.

Robert Verity1, Lucy Okell1, Ilaria Dorigatti1, Peter Winskill1, Charlie Whittaker1, Patrick Walker1, Christl Donnelly1, Neil Ferguson1, Azra Ghani2.   

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Year:  2020        PMID: 32473660      PMCID: PMC7255722          DOI: 10.1016/S1473-3099(20)30443-6

Source DB:  PubMed          Journal:  Lancet Infect Dis        ISSN: 1473-3099            Impact factor:   25.071


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We are grateful for Simon Wood and colleagues' comments on our study, which explore some important sensitivities in the data that were available early in the COVID-19 pandemic. Wood and colleagues' re-analysis puts more weight on the Diamond Princess outbreak data, arriving at an infection fatality ratio (IFR) in the range 0·23–0·65%, whereas our analysis used data from repatriation flights out of Wuhan, leading to an IFR in the range 0·39–1·33%. Both datasets are opportunistic, and neither is perfectly representative of the underlying population of interest. For example, although the Diamond Princess outbreak has a uniquely well characterised population, the transmission setting is unusual and therefore not necessarily representative of the broader populations that such estimates would be applied to. Furthermore, the health status of cruise ship passengers is not necessarily the same as the general population of a similar age, and the standard of care received by these passengers is likely to be different to that received in settings where the health system is under more strain. Given these limitations and the fact that the Diamond Princess outbreak data were incomplete at the time of our analysis (late February, 2020), we opted to focus on repatriation flight data. Epidemics of novel diseases are inherently rapidly changing environments, which bring unique challenges from a data analysis point of view. Our position was neatly summarised by Michael Ryan, executive director of the WHO Health Emergencies Programme, who said that “perfection is the enemy of the good when it comes to emergency management. Speed trumps perfection.” Having early estimates, although imperfect, of the order of magnitude of the IFR (ie, knowing whether the IFR is nearer to 1% or 0·01%) is essential for strategic planning, and in this sense, the re-analysis by Wood and colleagues places the IFR on the same scale as our initial estimate. We also strongly support the call for appropriately designed prevalence studies, which are now urgently needed to provide direct estimates of the IFR with fewer limitations.
  1 in total

1.  Estimates of the severity of coronavirus disease 2019: a model-based analysis.

Authors:  Robert Verity; Lucy C Okell; Ilaria Dorigatti; Peter Winskill; Charles Whittaker; Natsuko Imai; Gina Cuomo-Dannenburg; Hayley Thompson; Patrick G T Walker; Han Fu; Amy Dighe; Jamie T Griffin; Marc Baguelin; Sangeeta Bhatia; Adhiratha Boonyasiri; Anne Cori; Zulma Cucunubá; Rich FitzJohn; Katy Gaythorpe; Will Green; Arran Hamlet; Wes Hinsley; Daniel Laydon; Gemma Nedjati-Gilani; Steven Riley; Sabine van Elsland; Erik Volz; Haowei Wang; Yuanrong Wang; Xiaoyue Xi; Christl A Donnelly; Azra C Ghani; Neil M Ferguson
Journal:  Lancet Infect Dis       Date:  2020-03-30       Impact factor: 25.071

  1 in total
  2 in total

1.  Assessing the spreading potential of an undetected case of COVID-19 in orthopaedic surgery.

Authors:  K N Schneider; C L Correa-Martínez; G Gosheger; C Rickert; D Schorn; A Mellmann; V Schwierzeck; S Kampmeier
Journal:  Arch Orthop Trauma Surg       Date:  2020-06-10       Impact factor: 3.067

2.  Quantifying heterogeneity in SARS-CoV-2 transmission during the lockdown in India.

Authors:  Nimalan Arinaminpathy; Jishnu Das; Tyler H McCormick; Partha Mukhopadhyay; Neelanjan Sircar
Journal:  medRxiv       Date:  2020-09-15
  2 in total

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