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1.
Nonlinear Dyn ; 101(3): 1981-1993, 2020.
Article in English | MEDLINE | ID: covidwho-1906361

ABSTRACT

Due to the strong infectivity of COVID-19, it spread all over the world in about three months and thus has been studied from different aspects including its source of infection, pathological characteristics, diagnostic technology and treatment. Yet, the influences of control strategies on the transmission dynamics of COVID-19 are far from being well understood. In order to reveal the mechanisms of disease spread, we present dynamical models to show the propagation of COVID-19 in Wuhan. Based on mathematical analysis and data analysis, we systematically explore the effects of lockdown and medical resources on the COVID-19 transmission in Wuhan. It is found that the later lockdown is adopted by Wuhan, the fewer people will be infected in Wuhan, and nevertheless it will have an impact on other cities in China and even the world. Moreover, the richer the medical resources, the higher the peak of new infection, but the smaller the final scale. These findings well indicate that the control measures taken by the Chinese government are correct and timely.

2.
Infect Dis Model ; 6: 618-631, 2021.
Article in English | MEDLINE | ID: covidwho-1169180

ABSTRACT

In 2020, an unexpectedly large outbreak of the coronavirus disease 2019 (COVID-19) epidemic was reported in mainland China. As we known, the epidemic was caused by imported cases in other provinces of China except for Hubei in 2020. In this paper, we developed a differential equation model with tracing isolation strategy with close contacts of newly confirmed cases and discrete time imported cases, to perform assessment and risk analysis for COVID-19 outbreaks in Tianjin and Chongqing city. Firstly, the model behavior without imported cases was given. Then, the real-time regeneration number in Tianjin and Chongqing city revealed a trend of rapidly rising, and then falling fast. Finally, sensitivity analysis demonstrates that the earlier with Wuhan lock-down, the fewer cases in these two cities. One can obtain that the tracing isolation of close contacts of newly confirmed cases could effectively control the spread of the disease. But it is not sensitive for the more contact tracing isolation days on confirmed cases, the fewer cases. Our investigation model could be potentially helpful to provide model building technology for the transmission of COVID-19.

3.
Math Biosci Eng ; 17(5): 5961-5986, 2020 09 10.
Article in English | MEDLINE | ID: covidwho-965777

ABSTRACT

An outbreak of rapidly spreading coronavirus established human to human transmission and now became a pandemic across the world. The new confirmed cases of infected individuals of COVID-19 are increasing day by day. Therefore, the prediction of infected individuals has become of utmost important for health care arrangements and to control the spread of COVID-19. In this study, we propose a compartmental epidemic model with intervention strategies such as lockdown, quarantine, and hospitalization. We compute the basic reproduction number (R0), which plays a vital role in mathematical epidemiology. Based on R0, it is revealed that the system has two equilibrium, namely disease-free and endemic. We also demonstrate the non-negativity and boundedness of the solutions, local and global stability of equilibria, transcritical bifurcation to analyze its epidemiological relevance. Furthermore, to validate our system, we fit the cumulative and new daily cases in India. We estimate the model parameters and predict the near future scenario of the disease. The global sensitivity analysis has also been performed to observe the impact of different parameters on R0. We also investigate the dynamics of disease in respect of different situations of lockdown, e.g., complete lockdown, partial lockdown, and no lockdown. Our analysis concludes that if there is partial or no lockdown case, then endemic level would be high. Along with this, the high transmission rate ensures higher level of endemicity. From the short time prediction, we predict that India may face a crucial phase (approx 6000000 infected individuals within 140 days) in near future due to COVID-19. Finally, numerical results show that COVID-19 may be controllable by reducing the contacts and increasing the efficacy of lockdown.


Subject(s)
Communicable Disease Control/legislation & jurisprudence , Coronavirus Infections/epidemiology , Coronavirus Infections/transmission , Pneumonia, Viral/epidemiology , Pneumonia, Viral/transmission , Quarantine , Social Isolation , Algorithms , Basic Reproduction Number , Betacoronavirus , COVID-19 , Communicable Disease Control/methods , Humans , India/epidemiology , Models, Theoretical , Pandemics , Public Policy , SARS-CoV-2
4.
Math Biosci Eng ; 17(4): 3710-3720, 2020 05 21.
Article in English | MEDLINE | ID: covidwho-688913

ABSTRACT

Since December 2019, an outbreak of a novel coronavirus pneumonia (WHO named COVID-19) swept across China. In Shanxi Province, the cumulative confirmed cases finally reached 133 since the first confirmed case appeared on January 22, 2020, and most of which were imported cases from Hubei Province. Reasons for this ongoing surge in Shanxi province, both imported and autochthonous infected cases, are currently unclear and demand urgent investigation. In this paper, we developed a SEIQR difference-equation model of COVID-19 that took into account the transmission with discrete time imported cases, to perform assessment and risk analysis. Our findings suggest that if the lock-down date in Wuhan is earlier, the infectious cases are fewer. Moreover, we reveal the effects of city lock-down date on the final scale of cases: if the date is advanced two days, the cases may decrease one half (67, 95% CI: 66-68); if the date is delayed for two days, the cases may reach about 196 (95% CI: 193-199). Our investigation model could be potentially helpful to study the transmission of COVID-19, in other provinces of China except Hubei. Especially, the method may also be used in countries with the first confirmed case is imported.


Subject(s)
Betacoronavirus , Coronavirus Infections/transmission , Models, Biological , Pandemics , Pneumonia, Viral/transmission , Basic Reproduction Number/statistics & numerical data , COVID-19 , China/epidemiology , Computer Simulation , Coronavirus Infections/epidemiology , Coronavirus Infections/prevention & control , Humans , Markov Chains , Mathematical Concepts , Monte Carlo Method , Pandemics/prevention & control , Pandemics/statistics & numerical data , Pneumonia, Viral/epidemiology , Pneumonia, Viral/prevention & control , Quarantine/statistics & numerical data , SARS-CoV-2 , Time Factors , Travel/statistics & numerical data
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