Estimating the infection and case fatality ratio for COVID-19 using age-adjusted data from the outbreak on the Diamond Princess cruise ship

Russell, Tim; Hellewell, Joel; Jarvis, Christopher I; Zandvoort, Kevin van; Abbott, Sam; Ratnayake, Ruwan; Flasche, Stefan; Eggo, Rosalind M; Edmunds, W. John; Kucharski, Adam J.

doi:10.1101/2020.03.05.20031773

Cited by 227 publications

(315 citation statements)

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“…These values for the IFR are consistent with the findings of [2] (0.66% in China) and [3] (0.9% in the UK). The value of 1.3% estimated on the Diamond Princess cruise ship [4] falls above the top end of our 95% CI. This reflects the age distribution on the ship, which was skewed towards older individuals (mean age: 58 years), among whom the IFR is higher [3,4].…”

Section: Discussionmentioning

confidence: 41%

“…Based on the PCR-confirmed cases in international residents repatriated from China on January 2020, [2] obtained an estimate of the infection fatality ratio (IFR) of 0.66% in China, and, adjusting for non-uniform attack rates by age, an IFR of 0.9% was obtained in the UK [3]. Using data from the quarantined Diamond Princess cruise ship in Japan and correcting for delays between confirmation and death [4] obtained an IFR of 1.3%.…”

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confidence: 99%

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Using early data to estimate the actual infection fatality ratio from COVID-19 in France

Roques

Klein

Papaïx

et al. 2020

Preprint

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Background. The number of screening tests carried out in France and the methodology used to target the patients tested do not allow for a direct computation of the actual number of cases and the infection fatality ratio (IFR). The main objective of this work is to estimate the actual number of people infected with COVID-19 and to deduce the IFR during the observation window in France.Methods. We develop a 'mechanistic-statistical' approach coupling a SIR epidemiological model describing the unobserved epidemiological dynamics, a probabilistic model describing the data acquisition process and a statistical inference method.Results. The actual number of infected cases in France is probably higher than the observations: we find here a factor ×8 (95%-CI: 5-12) which leads to an IFR in France of 0.5% (95%-CI: 0.3−0.8) based on hospital death counting data. Adjusting for the number of deaths in nursing homes, we obtain an IFR of 0.8% (95%-CI: 0.45 − 1.25).Conclusions. This IFR is consistent with previous findings in China (0.66%) and in the UK (0.9%) and lower than the value previously computed on the Diamond Princess cruse ship data (1.3%).

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Section: Discussionmentioning

confidence: 41%

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confidence: 99%

Using early data to estimate the actual infection fatality ratio from COVID-19 in France

Roques

Klein

Papaïx

et al. 2020

Preprint

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“…Model output on cumulative death counts ( ) is fitted to the reported time series of deaths Λ (see Data) using a Bayesian MCMC approach previously implemented in other modelling studies [7][8][9][10] . Model variables are summarized in probability of dying with severe disease θ Gaussian distribution G(M=0.14, SD=0.007) [1,2,11,17] proportion of population at risk of severe disease ρ Gamma distribution G1(S=5, R=5/0.01), G2(S=5, R=5/0.001) --population size N UK 66.87M, Italy 60M ---…”

Section: Modelmentioning

confidence: 99%

Fundamental principles of epidemic spread highlight the immediate need for large-scale serological surveys to assess the stage of the SARS-CoV-2 epidemic

Lourenço

Paton

Thompson

et al. 2020

Preprint

213

269

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The spread of a novel pathogenic infectious agent eliciting protective immunity is typically characterised by three distinct phases: (I) an initial phase of slow accumulation of new infections (often undetectable), (II) a second phase of rapid growth in cases of infection, disease and death, and (III) an eventual slow down of transmission due to the depletion of susceptible individuals, typically leading to the termination of the (first) epidemic wave. Before the implementation of control measures (e.g. social distancing, travel bans, etc) and under the assumption that infection elicits protective immunity, epidemiological theory indicates that the ongoing epidemic of SARS-CoV-2 will conform to this pattern. Here, we calibrate a susceptible-infected-recovered (SIR) model to data on cumulative reported SARS-CoV-2 associated deaths from the United Kingdom (UK) and Italy under the assumption that such deaths are well reported events that occur only in a vulnerable fraction of the population. We focus on model solutions which take into consideration previous estimates of critical epidemiological parameters such as the basic reproduction number (R0), probability of death in the vulnerable fraction of the population, infectious period and time from infection to death, with the intention of exploring the sensitivity of the system to the actual fraction of the population vulnerable to severe disease and death. Our simulations are in agreement with other studies that the current epidemic wave in the UK and Italy in the absence of interventions should have an approximate duration of 2-3 months, with numbers of deaths lagging behind in time relative to overall infections. Importantly, the results we present here suggest the ongoing epidemics in the UK and Italy started at least a month before the first reported death and have already led to the accumulation of significant levels of herd immunity in both countries. There is an inverse relationship between the proportion currently immune and the fraction of the population vulnerable to severe disease. This relationship can be used to determine how many people will require hospitalisation (and possibly die) in the coming weeks if we are able to accurately determine current levels of herd immunity. There is thus an urgent need for investment in technologies such as virus (or viral pseudotype) neutralization assays and other robust assays which provide reliable read-outs of protective immunity, and for the provision of open access to valuable data sources such as blood banks and paired samples of acute and convalescent sera from confirmed cases of SARS-CoV-2 to validate these. Urgent development and assessment of such tests should be followed by rapid implementation at scale to provide real-time data. These data will be critical to the proper assessment of the effects of social distancing and other measures currently being adopted to slow down the case incidence and for informing future policy direction.

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“…We first calculated the naive case fatality ratio, nCFR, (i.e. deaths/cases) for each age group, then scaled down the naive CFR based on a correction factor estimated from data from the Diamond Princess [35] to give an adjusted CFR. We then calculated risk of hospitalisation based on the ratio of severe and critical cases to cases (18.5%) and deaths to cases (2.3%) in the early China data, which we took to imply 8.04 times more hospitalisations than deaths in each age group.…”

Section: Hospital Burden Estimationmentioning

confidence: 99%

The effect of non-pharmaceutical interventions on COVID-19 cases, deaths and demand for hospital services in the UK: a modelling study

Davies

Kucharski

Eggo

et al. 2020

Preprint

156

135

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BackgroundNon-pharmaceutical interventions have been implemented to reduce transmission of SARS-CoV-2 in the UK. Projecting the size of an unmitigated epidemic and the potential effect of different control measures has been critical to support evidence-based policymaking during the early stages of the epidemic. MethodsWe used a stochastic age-structured transmission model to explore a range of intervention scenarios, including the introduction of school closures, social distancing, shielding of elderly groups, self-isolation of symptomatic cases, and extreme "lockdown"-type restrictions. We simulated different durations of interventions and triggers for introduction, as well as combinations of interventions. For each scenario, we projected estimated new cases over time, patients requiring inpatient and critical care (intensive care unit, ICU) treatment, and deaths. FindingsWe found that mitigation measures aimed at reducing transmission would likely have decreased the reproduction number, but not sufficiently to prevent ICU demand from exceeding NHS availability. To keep ICU bed demand below capacity in the model, more extreme restrictions were necessary. In a scenario where "lockdown"-type interventions were put in place to reduce transmission, these interventions would need to be in place for a large proportion of the coming year in order to prevent healthcare demand exceeding availability. InterpretationThe characteristics of SARS-CoV-2 mean that extreme measures are likely required to bring the epidemic under control and to prevent very large numbers of deaths and an excess of demand on hospital beds, especially those in ICUs.

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Estimating the infection and case fatality ratio for COVID-19 using age-adjusted data from the outbreak on the Diamond Princess cruise ship

Cited by 227 publications

References 5 publications

Using early data to estimate the actual infection fatality ratio from COVID-19 in France

Using early data to estimate the actual infection fatality ratio from COVID-19 in France

Fundamental principles of epidemic spread highlight the immediate need for large-scale serological surveys to assess the stage of the SARS-CoV-2 epidemic

The effect of non-pharmaceutical interventions on COVID-19 cases, deaths and demand for hospital services in the UK: a modelling study

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