Epidemiological Benchmarks of the COVID-19 Outbreak Control in China After Wuhan's Lockdown: A Modelling Study with an Empirical Approach

Ku, Chu Chang; Ng, Ta-Chou; Lin, Hsien-Ho

doi:10.2139/ssrn.3543589

Cited by 17 publications

(18 citation statements)

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“…The understanding of lockdown in a pandemic context of COVID-19 is an emergency state where someone is not allowed to enter or out of a certain area in a certain period of time. This kind of action is carried out during the emergency situation (Ku et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

A Mathematical Model of the Covid-19 Cases in Indonesia (Under and Without Lockdown Enforcement)

Sugiyanto

Abrori

2020

BIOMEDNATPROCH

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COVID-19 stands for Corona (CO), Virus (VI), Disease (D) and year 2019 (19), which is COVID-19 first appeared in 2019. Mathematical model of covid deployment in Indonesia under and without lockdown case uses the SIRV model, such as Susceptible, Infected, Recovery, and Virus. The results of this model state that under lockdown the spread of COVID-19 could be stopped. If it were not under lockdown it can multiply 1,276 times higher over two months.

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Section: Introductionmentioning

confidence: 99%

A Mathematical Model of the Covid-19 Cases in Indonesia (Under and Without Lockdown Enforcement)

Sugiyanto

Abrori

2020

BIOMEDNATPROCH

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“…The literature support, states that restrictions during the pandemic decrease the number of basic reproduction [12], that justifies the decision of the Peruvian state to declare the quarantine and later post-quarantine because , is even greater than one and in the same way did the vast majority of South American countries but fewer days of confinement than Peru. The study of Hao [9], on contagion in China, with data from 21 January to 17 February 2020, under the MSIR and MSEIR models, the basic reproduction number parameter is 1.5, and during 20 to 30 January excluding Hubei other research obtains = 1.5283, by SUQC method, considering that there are not forty rigorous and control measures [23] and according to Chu-Chang Ku, and Ta-Chou Ng [5], the basic reproduction de contagio of Covid-19, after the closure of Wuhan most of its provinces oscillate between 1.66 (CI: 0.72, 2.87) in Fujian and 5.51 (CI: 3.87-6.85) in Jilin, during January 24 to February 12 by the SIR model. In addition, in four cities in Korea, = 1.5 (IC:1.4-1.6) was obtained by the syncretized probability distribution model using reports between January 20 and February 18 [21] and in the Marche region of Italy the estimates of calculated by maximum probability, within the four 1216 provinces studied with reports from February 26 to April 20 of this year, the city Ancona has 1.512 (IC:0.75-2.75) [4].…”

Section: Discussion:-mentioning

confidence: 99%

Basic Reproduction Number of the Covid-19 Epidemic in Cities of Peru

Huanacuni¹,

Chura²

2020

IJAR

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Actually, the Covid-19 is still in force in Peru, due to its complicated understanding of the epidemic's transmission rate, the present research was proposed with the objective of estimating the basic reproduction number , for the transmission by Covid-19 and simulate the behavior of infections in the cities of Lima, Callao, Piura, Lambayeque, Libertad and Loreto, through the binomial chain model and using the infection reports; the parameters that maximize the chain probability function were estimated. The estimates of , at 95% confidence interval were 1.

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“…They find that the world-widely implemented travel restriction policies, social distancing, and mass gathering restrictions play a crucial role in reducing the infection rate. Ku et al (2020) develop an empirical approach based on a Bass Susceptible-Infected-Recovered model to predict specific transmission parameters, exogenous forces of infection, and effective population sizes to determine the reproductive number (R 0 ) for the COVID-19 transmission in Chinese provinces.…”

Section: Literature Reviewmentioning

confidence: 99%

COVID-19: Optimal Allocation of Ventilator Supply under Uncertainty and Risk

2021

Preprint

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This study presents a new risk-averse multi-stage stochastic epidemics-ventilator-logistics compartmental model to address the resource allocation challenges of mitigating COVID-19. This epidemiological logistics model involves the uncertainty of untested asymptomatic infections and incorporates short-term human migration. Disease transmission is also forecasted through a new formulation of transmission rates that evolve over space and time with respect to various non-pharmaceutical interventions, such as wearing masks, social distancing, and lockdown. The proposed multi-stage stochastic model overviews different scenarios on the number of asymptomatic individuals while optimizing the distribution of resources, such as ventilators, to minimize the total expected number of newly infected and deceased people. The Conditional Value at Risk (CVaR) is also incorporated into the multi-stage mean-risk model to allow for a trade-off between the weighted expected loss due to the outbreak and the expected risks associated with experiencing disastrous pandemic scenarios. We apply our multi-stage mean-risk epidemics-ventilator-logistics model to the case of controlling the COVID-19 in highly-impacted counties of New York and New Jersey. We calibrate, validate, and test our model using actual infection, population, and migration data. The results indicate that short-term migration influences the transmission of the disease significantly. The optimal number of ventilators allocated to each region depends on various factors, including the number of initial infections, disease transmission rates, initial ICU capacity, the population of a geographical location, and the availability of ventilator supply. Our data-driven modeling framework can be adapted to study the disease transmission dynamics and logistics of other similar epidemics and pandemics.

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Epidemiological Benchmarks of the COVID-19 Outbreak Control in China After Wuhan's Lockdown: A Modelling Study with an Empirical Approach

Cited by 17 publications

References 0 publications

A Mathematical Model of the Covid-19 Cases in Indonesia (Under and Without Lockdown Enforcement)

A Mathematical Model of the Covid-19 Cases in Indonesia (Under and Without Lockdown Enforcement)

Basic Reproduction Number of the Covid-19 Epidemic in Cities of Peru

COVID-19: Optimal Allocation of Ventilator Supply under Uncertainty and Risk

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