Predicting the number of reported and unreported cases for the COVID-19 epidemics in China, South Korea, Italy, France, Germany and United Kingdom

Liu, Z.; Magal, Pierre; Webb, Geoff

doi:10.1016/j.jtbi.2020.110501

Cited by 87 publications

(94 citation statements)

References 20 publications

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“…In this article, we proposed a new epidemic model involving the daily number of tests as an input of the model. The model itself extends our previous models presented in ( Griette et al., 2020 ; Liu et al., 2020a , 2020b , 2020c , 2020d , 2021 ). We proposed a new method to use the data in such a context based on the fact that the cumulative number of tests grows linearly at the early stage of the epidemic.…”

Section: Discussionmentioning

confidence: 57%

“…More recently the method was extended and successfully applied to a Japanese age-structured dataset in ( Griette et al., 2020 ). The method was also extended to investigate the predictability of the outbreak in several countries including China, South Korea, Italy, France, Germany and the United Kingdom in ( Liu et al., 2021 ). The application of the Bayesian method was also considered in ( Cotta et al., 2020 ).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Clarifying predictions for COVID-19 from testing data: The example of New York State

Griette

Magal

2021

Infectious Disease Modelling

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Section: Discussionmentioning

confidence: 57%

Section: Introductionmentioning

confidence: 99%

Clarifying predictions for COVID-19 from testing data: The example of New York State

Griette

Magal

2021

Infectious Disease Modelling

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“…When transformed to the basic reproduction number R 0 , the differences across the countries were more obvious. Fixing the fraction of asymptomatic infectious that become reported symptomatic infectious at 0.6, Magal et al utilized a similar modified SEIR model and reported basic reproduction number R 0 to be 3.79, 4.21 and 4.45 for Italy, Germany, and France, respectively [ 27 ]. It seemed that our estimates of R 0 are close to their results.…”

Section: Discussionmentioning

confidence: 99%

Modelling the initial epidemic trends of COVID-19 in Italy, Spain, Germany, and France

Wang

Ding

et al. 2020

PLoS ONE

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The Coronavirus Disease 2019 (COVID-19) has fast spread to over 200 countries and regions worldwide since its outbreak, while in March, Europe became the emerging epicentre. In this study, we aimed to model the epidemic trends and estimate the essential epidemic features of COVID-19 in Italy, Spain, Germany, and France at the initial stage. The numbers of daily confirmed cases and total confirmed cases were extracted from the Coronavirus disease (COVID-19) situation reports of WHO. We applied an extended Susceptible-Exposed-Infectious-Removed (SEIR) model to fit the epidemic trend and estimated corresponding epidemic features. The transmission rate estimates were 1.67 (95% credible interval (CrI), 1.64–1.71), 2.83 (2.72–2.85), 1.91 (1.84–1.98), and 1.89 (1.82–1.96) for Italy, Spain, Germany, and France, corresponding to the basic reproduction numbers (R0) 3.44 (3.35–3.54), 6.25 (5.97–6.55), 4.03 (3.84–4.23), and 4.00 (3.82–4.19), respectively. We found Spain had the lowest ascertainment rate of 0.22 (0.19–0.25), followed by France, Germany, and Italy of 0.45 (0.40–0.50), 0.46 (0.40–0.52), and 0.59 (0.55–0.64). The peaks of daily new confirmed cases would reach on April 16, April 5, April 21, and April 19 for Italy, Spain, Germany, and France if no action was taken by the authorities. Given the high transmissibility and high covertness of COVID-19, strict countermeasures, such as national lockdown and social distancing, were essential to be implemented to reduce the spread of the disease.

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“…More recently the method was extended and successfully applied to a Japanese age-structured dataset in [11]. The method was also extended to investigate the predictability of the outbreak in several countries China, South Korea, Italy, France, Germany and the United Kingdom in [18]. The application of the Bayesian method was also considered in [9].…”

Section: Introductionmentioning

confidence: 99%

Clarifying predictions for COVID-19 from testing data: the example of New-York State

Griette¹,

Magal²

2020

Preprint

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In this article, we use testing data as an input of a new epidemic model. We get nice a concordance between the best fit the model to the reported cases data for New-York state. We also get a good concordance of the testing dynamic and the epidemic's dynamic in the cumulative cases. Finally, we can investigate the effect of multiplying the number of tests by 2, 5, 10, and 100 to investigate the consequences on the reduction of the number of reported cases.

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Predicting the number of reported and unreported cases for the COVID-19 epidemics in China, South Korea, Italy, France, Germany and United Kingdom

Abstract: Highlights corona virus, reported and unreported cases, isolation, quarantine, public closings; epidemic mathematical model

Cited by 87 publications

References 20 publications

Clarifying predictions for COVID-19 from testing data: The example of New York State

Clarifying predictions for COVID-19 from testing data: The example of New York State

Modelling the initial epidemic trends of COVID-19 in Italy, Spain, Germany, and France

Clarifying predictions for COVID-19 from testing data: the example of New-York State

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