Current trends and future prediction of novel coronavirus disease (COVID-19) epidemic in China: a dynamical modeling analysis

Wang, Kai; Lu, Zhenzhen; Li, Hui; Li, Huling; Lin, Dan-Dan; Cai, Yongli; Feng, Xing; Song, Yunlin; Feng, Zhiwei; Ji, Wenting; Wang, Xiaoyan; Yin, Yi; Wang, Lei; Peng, Zhihang

doi:10.3934/mbe.2020173

Cited by 41 publications

(27 citation statements)

References 13 publications

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“…The mathematical modelling used in the study is analogous to the transmission dynamics model of COVID-19 in [15][16][17][18][19][20][21][22][23][24]. The SIFR model help us to estimate the cumulative number of infected cases of COVID-19 in Harbin in April and the effective reproduction number using found infected cases.…”

Section: Discussionmentioning

confidence: 99%

Estimation of COVID-19 outbreak size in Harbin, China

Song

Jia

Zhou

2020

Preprint

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Background: Since the first level response to public health emergencies (FLRPHE) was launched on January 25, 2020 in Heilongjiang province, China, the outbreak of COVID-19 seems to be under control. However, an outbreak of COVID-19 caused by imported case developed in Harbin during April 2020. Here, we provide an estimate of the COVID-19 outbreak size in Harbin in April based on the number of found infected cases, assess the proportion of found and unfound infected cases in infected cases and evaluate the effective reproduction number which shows the transmission risk of COVID-19. Methods: We used data from April 9 to April 30, 2020, on the number of found infected cases from the outbreak in Harbin to infer the number of infections in Harbin in the outbreak of COVID-19 and give the proportion of found and unfound infected cases in infected cases. Data on found infected cases were obtained from the the reports of Health Commission of Heilongjiang Province. A Susceptible-Unfound infected-Found infected-Removed model was used to fit the data on found infected cases of COVID-19 in Harbin using the least square method and simulate the transmission of COVID-19. The effective reproduction number was estimated. Results: The COVID-19 outbreak size estimated in Harbin in April reaches 174, where 54% of infected cases were found and 46% of infected cases were not found out. Our findings suggest that the effective reproduction number decreased drastically in contrast with the value of 3.6 on April 9 after that the effective interventions were implemented by Heilongjiang province government. Finally, the effective reproduction number arrived the value of 0.04 which is immensely below the threshold value 1, which means that Heilongjiang province government got the outbreak of COVID-19 in Harbin under control. Conclusions: The COVID-19 outbreak size in Harbin based on the assumptions that infected people with COVID-19 in the incubation period have the same infectivity with infectious people with COVID-19 could have been overestimated. As an increasing number of imported infected cases got into China and a growing number of asymptomatic infected people were found, our study provides evidence that unfound infected cases would increase the risk of local outbreak of COVID-19 in China.

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Section: Discussionmentioning

confidence: 99%

Estimation of COVID-19 outbreak size in Harbin, China

Song

Jia

Zhou

2020

Preprint

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“…The second outflow is the population that flows out to the recovery group directly without needing treatment by transmission rate of recovery . The third outflow is a population that flows out to the infected group with the transmission rate of infected , and the fourth outflow is the population that experiences natural death by the transmission rate 4,6,15,21,22,24,26 .…”

Section: Formulation Of a Novel Coronavirus Disease (Seirq Model)mentioning

confidence: 99%

“…All inflows and outflows are shown in the flowchart in Fig. 1, and the five groups can be converted into equations to formulate the following system of first-order ordinary nonlinear differential equations 4,6,15,21,22,24,26 :…”

Section: Formulation Of a Novel Coronavirus Disease (Seirq Model)mentioning

confidence: 99%

“…Some of the other parameters have been calculated, estimated, or assumed, as in Table 2. Table 2: The values of parameters in SEIRQ 3,8,10,15,19,21,24,26 Parameter Value Background Moreover, the reproduction number RBN is . In other words, the transmission rate at which the susceptible individual converted to an exposed individual is higher than one, which means the spread of COVID-19 is unstable.…”

Section: Applying the Seirq Model To Saudi Arabia Data Of The Spread mentioning

confidence: 99%

“…On the other hand, the published models have not, to date, taken into account the environmental position of COVID-19 transmission. Several other modeling studies for the COVID-19 outbreak have also been carried out 6,8,[14][15][16][17][18][19][20][21][22] .…”

Section: Introductionmentioning

confidence: 99%

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A New Dynamical Modelling of the Epidemic Diseases to Assessing the Rates of Spread of COVID-19 in Saudi Arabia: SEIRQ Model

Youssef¹,

Alghamdi²,

Ezzat³

et al. 2020

Preprint

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A new model of critical epidemic dynamics for the emergence of the new coronavirus COVID-19 is being established in this paper. A new approach to the assessment and control of the COVID 19 epidemic is given with the SEIRQ pandemic model. This paper uses real knowledge on the distribution of COVID-19 in Saudi Arabia for mathematical modeling and dynamic analyses. The reproductive number and detailed stability analysis are provided in the SEIRQ model dynamics. In a Jacobian method of linearization, we will address the domain of the solution and the equilibrium situation based on the SEIRQ model. The equilibrium and its importance have been proven, and a study of the stability of the equilibrium free from diseases has been implemented. The reproduction number was evaluated in accordance with its internal parameters. The Lyapunov theorem of stability has proven the global stability of the current model's equilibrium. The SEIRQ model was contrasted by comparing the results based on the SEIRQ model with the real COVID-19 spread data in Saudi Arabia. Numerical evaluation and predictions were given. The results indicate that the SEIRQ model is a strong model for the study of the spread of epidemics, such as COVID-19. At the end of this work, we implemented an optimum protocol that can quickly stop the spread of COVID-19 among the Saudi populations.

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Estimation of novel coronavirus (COVID‐19) reproduction number and case fatality rate: A systematic review and meta‐analysis

Ahammed

Anjum

Rahman

et al. 2021

Health Science Reports

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Background and aims Realizing the transmission potential and the magnitude of the coronavirus disease 2019 (COVID‐19) aids public health monitoring, strategies, and preparation. Two fundamental parameters, the basic reproduction number ( R 0 ) and case fatality rate (CFR) of COVID‐19, help in this understanding process. The objective of this study was to estimate the R 0 and CFR of COVID‐19 and assess whether the parameters vary in different regions of the world. Methods We carried out a systematic review to find the reported estimates of the R 0 and the CFR in articles from international databases between January 1 and August 31, 2020. Random‐effect models and Forest plots were implemented to evaluate the mean effect size of R 0 and the CFR. Furthermore, R 0 and CFR of the studies were quantified based on geographic location, the tests/thousand population, and the median population age of the countries where the studies were conducted. To assess statistical heterogeneity among the selected articles, the I 2 statistic and the Cochran's Q test were used. Results Forty‐five studies involving R 0 and 34 studies involving CFR were included. The pooled estimation of R 0 was 2.69 (95% CI: 2.40, 2.98), and that of the CFR was 2.67 (2.25, 3.13). The CFR in different regions of the world varied significantly, from 2.49 (2.08, 2.94) in Asia to 3.40 (2.81, 4.04) in North America. We observed higher mean CFR values for the countries with lower tests (3.15 vs 2.16) and greater median population age (3.13 vs 2.27). However, R 0 did not vary significantly in different regions of the world. Conclusions An R 0 of 2.69 and a CFR of 2.67 indicate the severity of the COVID‐19. Although R 0 and CFR may vary over time, space, and demographics, we recommend considering these figures in control and prevention measures.

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Current trends and future prediction of novel coronavirus disease (COVID-19) epidemic in China: a dynamical modeling analysis

Cited by 41 publications

References 13 publications

Estimation of COVID-19 outbreak size in Harbin, China

Estimation of COVID-19 outbreak size in Harbin, China

A New Dynamical Modelling of the Epidemic Diseases to Assessing the Rates of Spread of COVID-19 in Saudi Arabia: SEIRQ Model

Estimation of novel coronavirus (COVID‐19) reproduction number and case fatality rate: A systematic review and meta‐analysis

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