Tracking and Predicting COVID-19 Epidemic in China Mainland

Sun, Hui; Qiu, Yumou; Yan, Han; Huang, Yi‐Zhi; Zhu, Yuru; Chen, Song Xi

doi:10.1101/2020.02.17.20024257

Cited by 60 publications

(60 citation statements)

References 14 publications

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“…Let the unit time be 1 day. Based on the previous studies [6,7], we fix 1/ε = 5, and thus, ε = 0.2 and γ = 0.1, respectively. We fix S + E + I + R to be 1 so that each population implies the proportion to the total population.…”

Section: Modelmentioning

confidence: 99%

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

Kuniya

2020

JCM

284

301

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The first case of coronavirus disease 2019 in Japan was reported on 15 January 2020 and the number of reported cases has increased day by day. The purpose of this study is to give a prediction of the epidemic peak for COVID-19 in Japan by using the real-time data from 15 January to 29 February 2020. Taking into account the uncertainty due to the incomplete identification of infective population, we apply the well-known SEIR compartmental model for the prediction. By using a least-square-based method with Poisson noise, we estimate that the basic reproduction number for the epidemic in Japan is R 0 = 2.6 (95%CI, 2.4-2.8) and the epidemic peak could possibly reach the early-middle summer. In addition, we obtain the following epidemiological insights: (1) the essential epidemic size is less likely to be affected by the rate of identification of the actual infective population;(2) the intervention has a positive effect on the delay of the epidemic peak; (3) intervention over a relatively long period is needed to effectively reduce the final epidemic size.

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Section: Modelmentioning

confidence: 99%

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

Kuniya

2020

JCM

284

301

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“…A temporary reduction in σ can account for both population-wide interventions and interventions specific to identified infectious (or possibly infectious) individuals. The SIR model with a time-dependent reproduction number (or equivalently, a time-dependent contact rate) has been considered before, for instance in [3,22]. Typically, an epidemic does not result in substantial permanent change in the contact rate of a population.…”

Section: Problem Description and Assumptionsmentioning

confidence: 99%

“…According to (22), the optimal control value will switch when u x = u y , which leads to (19). Meanwhile, substituting (22) in (21) in the case u y < u x yields the linear hyperbolic PDE u t = γyu y − βxy(u y − u x ), whose characteristics are just the trajectories of the SIR system (1) illustrated in Figure 1, which are also contours of x ∞ . It can be shown that once u y − u x < 0, this inequality will continue to hold along each such characteristic.…”

Section: Infinite-time Controlmentioning

confidence: 99%

Optimal control of an SIR epidemic through finite-time non-pharmaceutical intervention

Ketcheson

2020

Preprint

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We consider the problem of controlling an SIR-model epidemic by temporarily reducing the rate of contact within a population. The control takes the form of a multiplicative reduction in the contact rate of infectious individuals. The control is allowed to be applied only over a finite time interval, while the objective is to minimize the total number of individuals infected in the long-time limit, subject to some cost function for the control. We first consider the no-cost scenario and analytically determine the optimal control and solution. We then study solutions when a cost of intervention is included, as well as a cost associated with overwhelming the available medical resources. Examples are studied through the numerical solution of the associated Hamilton-Jacobi-Bellman equation. Finally, we provide some examples related directly to the current pandemic.

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“…Many researchers have conducted mathematical model-based studies to investigate the transmission dynamics and forecast the number of COVID-19 infections. A mathematical susceptible-infectious-recovered (SIR) and susceptible-exposed-infectious-recovered (SEIR) were considered to forecast the number of coronavirus infections in China [7,8]. A flowchart of the SIHR model is presented in Figure 3 Based on the mathematical model described above, the time-varying transmission rate is considered as follows:…”

Section: Statistical Modelmentioning

confidence: 99%

Estimating and forecasting the spread of COVID-19 in South Korea: A Bayesian SIHR-based dynamic model with non-pharmaceutical interventions

Kang

Lee

Kim

et al. 2020

Preprint

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Background: As of May 22, 2020, the total number of confirmed COVID-19 cases is over 5 million worldwide and more than 300 thousand people have lost their lives to the virus. South Korea also experienced a sharp increase in late February, but owing to non-pharmaceutical interventions, the number of confirmed cases has been decreasing since March. In this study, we aimed to investigate the transmission dynamics with these effects as well as forecast the spread of COVID-19 in South Korea, using a flexible statistical model.Methods: We analyzed the COVID-19 data obtained at the Korea Centers for Disease Control and Prevention from Feb 18 to April 30. Using a Bayesian susceptible-infectious-hospitalized-removed (SIHR) dynamic model, we estimated the dynamic transmission rate considering the non-pharmaceutical intervention effects and forecast the confirmed cases.Results: The estimated transmission rate without any control effects was 0.4605 with 95% credible interval (0.4468, 0.4745). During the days with effects between February 26 and March 6, the daily transmission rate decreased by about 89.48% of that of the previous day. With consistent control effects, it remained at 0.1549 with 95% credible interval (0.1497, 0.1602). Based on the estimated transmission rate, the forecast number of COVID-19 infections in South Korea showed an overall decreasing pattern.Conclusions: We considered and estimated the dynamic effects of the non-pharmaceutical interventions on COVID-19 using a Bayesian SIHR-based model. This study shows that non-pharmaceutical interventions including active testing, quarantine and isolation, personal preventive measures, and social distancing are crucial to curb the transmission. Our findings contribute to a better understanding of non-pharmaceutical interventions in COVID-19.

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Tracking and Predicting COVID-19 Epidemic in China Mainland

Cited by 60 publications

References 14 publications

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

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

Optimal control of an SIR epidemic through finite-time non-pharmaceutical intervention

Estimating and forecasting the spread of COVID-19 in South Korea: A Bayesian SIHR-based dynamic model with non-pharmaceutical interventions

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