Prediction of the Epidemic Peak of Coronavirus Disease in Japan, 2020

Kuniya, Toshikazu

doi:10.3390/jcm9030789

Cited by 283 publications

(321 citation statements)

References 14 publications

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“…Kuniya (2020) estimated the outbreak peak of coronavirus disease in Japan with the SEIR compartmental model [24]. In another study, the number of reproduction of the Wuhan novel coronavirus 2019-nCoV was estimated with the susceptible-exposed-infected-removed (SEIR) compartment model [25].There are studies on coronavirus disease by different researchers using various statistical methods.…”

Section: Discussionmentioning

confidence: 99%

Modelling of Covid-19 Outbreak Indicators in China Between January and April

Çelik

Ankaralı

Pasin

2020

Preprint

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Background:The aim of this study is to explain the changes of outbreak indicators for coronavirus in China with nonlinear models and time series analysis. There are lots of methods for modelling. But we want to determine the best mathematical model and the best time series method among other models. Methods:The data was obtained between January 22 and April 21, 2020 from China records. The number of total cases and the number of total deaths were used for the calculations. For modelling Weibull, Negative Exponential, Von Bertalanffy, Janoscheck, Lundqvist-Korf and Sloboda models were used and AR, MA, ARMA, Holt, Brown and Damped models were used for time series. The determination coefficient (R 2 ), Pseudo R 2 and mean square error were used for nonlinear modelling as criteria for determining the model that best describes the number of cases, the number of total deaths and BIC (Bayesian Information Criteria) was used for time series.Results: According to our results, the Sloboda model among the growth curves and ARIMA (0,2,1) model among the times series models were most suitable models for modelling of the number of total cases. In addition Lundqvist-Korf model among the growth curves and Holt linear trend exponential smoothing model among the times series models were most suitable model for modelling of the number of total deaths. Our time series models forecast that the number of total cases will 83311 on 5 May and the number of total deaths will be 5273. Conclusions:Because results of the modelling has providing information on measures to be taken and giving prior information for subsequent similar situations, it is of great importance modeling outbreak indicators for each country separately.

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Section: Discussionmentioning

confidence: 99%

Modelling of Covid-19 Outbreak Indicators in China Between January and April

Çelik

Ankaralı

Pasin

2020

Preprint

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“…The mortality rate is lower than in SARS (Zhou, Yu et al 2020) but the incidence rate is significantly higher. The current COVID-19 epidemic differs in several aspects from the previous one caused by SARS, which was finally extinguished (Cossarizza, De Biasi et al 2020, Kucharski, Russell et al 2020, Kuniya 2020, Leung 2020, Mizumoto, Kagaya et al 2020, Parodi and Liu 2020. First, COVID-19 has a higher basic reproductive number, R 0 , than SARS.…”

Section: Introductionmentioning

confidence: 95%

Covid-19 Dynamics: A Heterogeneous Model

Герасимов

Lebedev

et al. 2020

Preprint

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The ongoing Coronavirus disease 2019 (COVID-19) epidemic is different from the previous epidemic of severe acute respiratory syndrome (SARS), which demands a rigorous analysis for the selection of anti-epidemic measures and their lifting when the epidemic subsides. Here we estimate the basic reproductive number for COVID-19 and propose a dynamical model for the time course of infection number. With this model, we assessed the effects of different measures for infection risk control. The model is different from the previous ones as it models the population as heterogeneous, with subpopulations having different infection risks. Our analyses showed that after this heterogeneity is incorporated in the model, several characteristics of the epidemic are estimated more accurately: the total number of cases and peak number of cases are lower compared to the homogeneous case, the early-stage growth rate in the number of infection cases is little affected, and the decrease in the number of infections slows down during the epidemic late stage. The comparison of our model results with the available data for COVID-19 indicates that the anti-epidemic measures undertaken in China and the rest of the world managed to decrease the basic reproductive number but did not assure an accumulation of sufficient collective immunity. Thus, the epidemic has a high likelihood to restart, which necessitates a careful approach to lifting the quarantine measures.

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“…The rapid increase in clinical cases of the new coronavirus disease, COVID-19, suggests that its transmissibility is high. Several studies indicated that the most probable basic reproductive number (R 0 ) is between two and four for the coronavirus (meaning that each infectious individual may directly generate two to four others) [1][2][3][4][5][6] . However, some estimates suggested R 0 higher than four with confidence intervals that reach values close to eight 2,7 .…”

Section: Introductionmentioning

confidence: 99%

Translating transmissibility measures into recommendations for coronavirus prevention

2020

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The rapid increase in clinical cases of the new coronavirus disease, COVID-19, suggests high transmissibility. However, the estimates of the basic reproductive number reported in the literature vary widely. Considering this, we drew the function of contact-rate reduction required to control the transmission from both detectable and undetectable sources. Based on this, we offer a set of recommendations for symptomatic and asymptomatic populations during the current pandemic. Understanding the dynamics of transmission is essential to support government decisions and improve the community's adherence to preventive measures.

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Prediction of the Epidemic Peak of Coronavirus Disease in Japan, 2020

Cited by 283 publications

References 14 publications

Modelling of Covid-19 Outbreak Indicators in China Between January and April

Modelling of Covid-19 Outbreak Indicators in China Between January and April

Covid-19 Dynamics: A Heterogeneous Model

Translating transmissibility measures into recommendations for coronavirus prevention

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