Case-Fatality Risk Estimates for COVID-19 Calculated by Using a Lag Time for Fatality

Wilson, Nick; Kvalsvig, Amanda; Barnard, Lucy; Baker, Michael G

doi:10.3201/eid2606.200320

Cited by 305 publications

(277 citation statements)

References 5 publications

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“…This IFR was then adjusted by the proportion assumed to be symptomatic (at 67%, as above) to give a case fatality risk (CFR) of 0.45% (i.e., 0.3% x 100%/67% = 0.45%). Nevertheless, we note that higher estimates exist, including a CFR for "China outside of Hubei Province" of 0.81% (95%CI: 0.67 to 0.98; and adjusted for the time delay in reporting deaths) [22]. Another CFR for "China outside of Hubei Province" was similar, at 0.9% (95% credible interval: 0.6-1.3%; also adjusted for the time delay in reporting deaths) [23].…”

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confidence: 68%

Modelling the Potential Health Impact of the COVID-19 Pandemic on a Hypothetical European Country

Wilson

Barnard

Kvalsig

et al. 2020

Preprint

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A SEIR simulation model for the COVID-19 pandemic was developed (http://covidsim.eu) and applied to a hypothetical European country of 10 million population. Our results show which interventions potentially push the epidemic peak into the subsequent year (when vaccinations may be available) or which fail. Different levels of control (via contact reduction) resulted in 22% to 63% of the population sick, 0.2% to 0.6% hospitalised, and 0.07% to 0.28% dead (n=6,450 to 28,228).

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confidence: 68%

Modelling the Potential Health Impact of the COVID-19 Pandemic on a Hypothetical European Country

Wilson

Barnard

Kvalsig

et al. 2020

Preprint

Self Cite

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“…We fix the fatality rate ω = 42% of ICU patients by studying historical records of dead individuals as a function of those requiring intensive care. In turn, we estimate the period from ICU admission to death as ψ −1 = 7 days [37] and the stay in ICU for those overcoming the disease as χ −1 = 10 days [38].…”

Section: A Parameters For the Modelization Of The Spreading Of Covid-19mentioning

confidence: 99%

A mathematical model for the spatiotemporal epidemic spreading of COVID19

Arenas

Cota

Gómez‐Gardeñes

et al. 2020

Preprint

158

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166 countries/regions, including cases of human-to-human transmission around the world. The proportions of this epidemics is probably one of the largest challenges faced by our interconnected modern societies. According to the current epidemiological reports, the large basic reproduction number, R0 ∼ 2.3, number of secondary cases produced by an infected individual in a population of susceptible individuals, as well as an asymptomatic period (up to 14 days) in which infectious individuals are undetectable without further analysis, pave the way for a major crisis of the national health capacity systems. Recent scientific reports have pointed out that the detected cases of COVID19 at young ages is strikingly short and that lethality is concentrated at large ages. Here we adapt a Microscopic Markov Chain Approach (MMCA) metapopulation mobility model to capture the spread of COVID-19. We propose a model that stratifies the population by ages, and account for the different incidences of the disease at each strata. The model is used to predict the incidence of the epidemics in a spatial population through time, permitting investigation of control measures. The model is applied to the current epidemic in Spain, using the estimates of the epidemiological parameters and the mobility and demographic census data of the national institute of statistics (INE). The results indicate that the peak of incidence will happen in the first half of April 2020 in absence of mobility restrictions. These results can be refined with improved estimates of epidemiological parameters, and can be adapted to precise mobility restrictions at the level of municipalities. The current estimates largely compromises the Spanish health capacity system, in particular that for intensive care units, from the end of March. However, the model allows for the scrutiny of containment measures that can be used for health authorities to forecast with accuracy their impact in prevalence of COVID-19. Here we show by testing different epidemic containment scenarios that we urge to enforce total lockdown to avoid a massive collapse of the Spanish national health system.

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“…We fix the fatality rate ω = 42% of ICU patients by studying historical records of dead individuals as a function of those requiring intensive care. In turn, we estimate the period from ICU admission to death as ψ −1 = 7 days [19] and the stay in ICU for those overcoming the disease as χ −1 = 20 days [1].…”

Section: Supplementary Note 5 Dynamical Approximation Of the Effectimentioning

confidence: 99%

“…Data from Spain Regarding the population structure in Spain, we have obtained the population distribution, population pyramid, daily population flows and average household size at the municipality level from Instituto Nacional de Estadística 18 whereas the age-specific contact matrices have been extracted from 19…”

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confidence: 99%

Derivation of the effective reproduction number ℛ for COVID-19 in relation to mobility restrictions and confinement

Arenas

Cota

Gómez‐Gardeñes

et al. 2020

Preprint

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This expression for R is an extremely useful tool to design containment policies that are able to suppress the epidemics. We applied our epidemic model for the case of Spain, successfully forecasting both the observed incidence in each region and the overload of the health system. The expression for R allowed us to determine the precise reduction of mobility κ 0 needed to bend the curve of epidemic incidence, which turned out to be κ 0 ∼ 0.7.This value, for the case of Spain, translates to a total lockdown with the exception of the mobility associated to essential services, a policy that was finally enforced on March 28.

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Case-Fatality Risk Estimates for COVID-19 Calculated by Using a Lag Time for Fatality

Cited by 305 publications

References 5 publications

Modelling the Potential Health Impact of the COVID-19 Pandemic on a Hypothetical European Country

Modelling the Potential Health Impact of the COVID-19 Pandemic on a Hypothetical European Country

A mathematical model for the spatiotemporal epidemic spreading of COVID19

Derivation of the effective reproduction number ℛ for COVID-19 in relation to mobility restrictions and confinement

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