Analysis of COVID-19 transmission in Shanxi Province with discrete time imported cases

Li, Mingtao; Sun, Gui‐Quan; Zhang, Juan; Zhao, Yu; Pei, Xin; Li, Li; Wang, Yong; Zhang, Wenyi; Zhang, Zi-Ke; Jin, Zhen

doi:10.3934/mbe.2020208

Cited by 68 publications

(56 citation statements)

References 17 publications

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“…All provincial government initiated a first-level emergency response from 25 January 2020 (Chinese lunar new year). And some non-pharmaceutical interventions include strict controls on travel, the surveillance for newly confirmed cases, and the tracing and management with close contacts of newly confirmed cases, and the registered individuals with home quarantine for at least 14 days ( Chinazzi et al, 2020 ; Li et al, 2020b ). All of these measures are designed to early detection, early reporting and early quarantine for COVID-19.…”

Section: Introductionmentioning

confidence: 99%

Transmission analysis of COVID-19 with discrete time imported cases: Tianjin and Chongqing as cases

Li¹,

Chen²,

Zhang³

et al. 2021

Infectious Disease Modelling

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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.

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Section: Introductionmentioning

confidence: 99%

Transmission analysis of COVID-19 with discrete time imported cases: Tianjin and Chongqing as cases

Li¹,

Chen²,

Zhang³

et al. 2021

Infectious Disease Modelling

Self Cite

View full text Add to dashboard Cite

show abstract

“…3 has eight free parameters; some of them can be set a-priori based on epidemiological, clinical or policy-related evidence. The incubation time σ −1 has been found by different clinical groups to be around 5 days [2,31], thereby yielding σ 1/5, a value also used in similar modeling studies [34,35]. The rate of testing c t is country-specific; in Italy, the "Istituto Superiore di Sanità" (ISS) has reported an average time from symptoms onset to diagnosis (via pharyngeal swab) of 5 days, within the time range considered in this work.…”

Section: Proposed Compartment Modelmentioning

confidence: 64%

Modeling Growth, Containment and Decay of the COVID-19 Epidemic in Italy

Capuano

2020

Front. Phys.

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A careful inspection of the cumulative curve of confirmed COVID-19 infections in Italy and in other hard-hit countries reveals three distinct phases: i) an initial exponential growth (unconstrained phase), ii) an algebraic, power-law growth (containment phase), and iii) a relatively slow decay. We propose a parsimonious compartment model based on a time-dependent rate of depletion of the susceptible population that captures all such phases for a plausible range of model parameters. The results suggest an intimate interplay between the growth behavior, the timing and implementation of containment strategies, and the subsequent saturation of the outbreak.

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“…Various infectious disease models ( 5 – 7 ) are proposed to estimate the transmission of COVID-19 ( 8 – 12 ) and investigate the impact of public health interventions on mitigating the spread ( 13 – 17 ). Several studies modeled the transmission by stochastic dynamical systems ( 8 – 10 , 15 ), such as susceptible-exposed-infectious-recovered (SEIR) models ( 8 ), extended Kalman filter ( 18 – 20 ), and individual-based simulation models ( 13 , 14 ).…”

Section: Introductionmentioning

confidence: 99%

Survival-Convolution Models for Predicting COVID-19 Cases and Assessing Effects of Mitigation Strategies

Wang

Xie

Wang

et al. 2020

Front. Public Health

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Countries around the globe have implemented unprecedented measures to mitigate the coronavirus disease 2019 (COVID-19) pandemic. We aim to predict the COVID-19 disease course and compare the effectiveness of mitigation measures across countries to inform policy decision making using a robust and parsimonious survival-convolution model. We account for transmission during a pre-symptomatic incubation period and use a time-varying effective reproduction number ( R t ) to reflect the temporal trend of transmission and change in response to a public health intervention. We estimate the intervention effect on reducing the transmission rate using a natural experiment design and quantify uncertainty by permutation. In China and South Korea, we predicted the entire disease epidemic using only early phase data (2–3 weeks after the outbreak). A fast rate of decline in R t was observed, and adopting mitigation strategies early in the epidemic was effective in reducing the transmission rate in these two countries. The nationwide lockdown in Italy did not accelerate the speed at which the transmission rate decreases. In the United States, R t significantly decreased during a 2-week period after the declaration of national emergency, but it declined at a much slower rate afterwards. If the trend continues after May 1, COVID-19 may be controlled by late July. However, a loss of temporal effect (e.g., due to relaxing mitigation measures after May 1) could lead to a long delay in controlling the epidemic (mid-November with fewer than 100 daily cases) and a total of more than 2 million cases.

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Analysis of COVID-19 transmission in Shanxi Province with discrete time imported cases

Cited by 68 publications

References 17 publications

Transmission analysis of COVID-19 with discrete time imported cases: Tianjin and Chongqing as cases

Transmission analysis of COVID-19 with discrete time imported cases: Tianjin and Chongqing as cases

Modeling Growth, Containment and Decay of the COVID-19 Epidemic in Italy

Survival-Convolution Models for Predicting COVID-19 Cases and Assessing Effects of Mitigation Strategies

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