City Lockdown and Nationwide Intensive Community Screening Are Effective In Controlling COVID-19 Epidemic: Analysis Based on A Modified SIR Model

Peng, Tailai; Liu, Xinhao; Ni, Hefeng; Cui, Zhe

doi:10.21203/rs.3.rs-22056/v1

Cited by 4 publications

(3 citation statements)

References 10 publications

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“…This is also shown in the word cloud in Figure 4. The work of Lamsal (2020b) [11] , Peng et al (2020) [22] , Barkur (2020) [2] , and Dubey (2020) [4] all reported an overall positive sentiment on the nationwide lockdown due to COVID19. This shows that the lockdown exercise, though with some economic effects on the nations, was an acceptable control measure in the fight against the pandemic.…”

Section: Resultsmentioning

confidence: 99%

“…In another study which focused on the current coronavirus global pandemic, Peng et al (2020) [22] built a modified Susceptible Infected and Recovered (SIR) model from the classic SIR model by introducing a "proportion of the infected that is not effectively isolated from the susceptible at a given time point". Apart from the study's validation of the effectiveness of the city lockdown and intensive community screening approaches in China, it also predicted an exponential increase of infection cases in Italy and South Korea if the city lockdown and intensive community screening approaches are not followed.…”

Section: Review Of Related Workmentioning

confidence: 99%

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Sentiment analysis of the NIGERIAN nationwide lockdown due to COVID19 outbreak

Ogbuju¹,

Oladipo²,

Yemi-Peters³

et al. 2021

Int. J. Comput. Artif. Intell.

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Sentiment analysis is a classification technique that specializes in categorizing a body of texts into various emotions. This categorization had proven to be handy in classifying tweets into positive, negative, or neutral emotions. Nigerians had been on a nationwide lockdown due to COVID19 since 30th March 2020. The analysis of the emotions of Nigerians during this period is expedient to understand the effectiveness of exercise and the impact it has on the masses. The focus of this paper is to determine the sentiment analysis of Nigerians within the period of the lockdown exercise. Using a lexicon-based analytic architecture and a streaming API to TwitterNG, we extracted a total of 22, 249 tweets from the timelines of national stakeholders on COVID19 and location-based tweets from the general public. The tweets were extracted and collated using a set of ten hashtags/keywords from 30th March to 11th May 2020. The analysis was done in R Programming Software with the application of the NRC lexicon approach to classifying the emotions of Nigerians within the period. The result showed that Nigerians expressed an overall positive sentiment to the lockdown exercise despite a few negative expressions.

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Section: Resultsmentioning

confidence: 99%

Section: Review Of Related Workmentioning

confidence: 99%

Sentiment analysis of the NIGERIAN nationwide lockdown due to COVID19 outbreak

Ogbuju¹,

Oladipo²,

Yemi-Peters³

et al. 2021

Int. J. Comput. Artif. Intell.

View full text Add to dashboard Cite

show abstract

“…The SEIR model divides people into four compartments: susceptible, exposed, infectious, and removed 18 ; it has been applied to many virus outbreaks such as MERS 19,20 and SARS 21,22 , which established its public prominence [23][24][25] . However, as these classical models do not consider the unique features of COVID-19, many studies have built upon them by introducing new parameters or new groups into the existing models to represent more types of population, which can assist in representing reality more accurately [26][27][28] .…”

Section: Introductionmentioning

confidence: 99%

An Improved SEIR Model Considering the Impact of Contact-Restricting Policies: A Case Study of COVID-19 in Wuhan

Chen

Cheng

2021

Preprint

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Dynamic modeling of infectious disease can simulate transmission processes of COVID-19, a newly been found infectious respiratory disease that has a substantial impact on both people's health and social development, and therefore plays an important role in the prediction and prevention of epidemics. Although there are many models that can accurately represent the number of infected patients, the influence of human factors on the transmission of the virus has not been fully investigated. Here, by considering the influence of policies on restricting contact between people, we modified the SEIR infectious disease model and developed a new model called the Quarantine-considering SEIR model (hereafter referred to as Q-SEIR), combining with dynamic parameter, contact rate, obtained by machine learning method, we can represent the effects of human movement and contact behavior during the epidemic. The experimental results show that this method can effectively represent the effect of patterns of population activity on the development of the epidemic. On one hand, our research results provide guidance for the government before issuing measures to restrict the movement and socialization of people; and on the other hand, our findings help identify the development stage of the epidemic more clearly for the public as well as provide information for citizens’ travel decisions.

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Analytical Solution of the Susceptible-Infected-Recovered/Removed Model for the Not-Too-Late Temporal Evolution of Epidemics for General Time-Dependent Recovery and Infection Rates

Schlickeiser,

Kröger

2023

COVID

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The dynamical equations of the susceptible-infected-recovered/removed (SIR) epidemics model play an important role in predicting and/or analyzing the temporal evolution of epidemic outbreaks. Crucial input quantities are the time-dependent infection (a(t)) and recovery (μ(t)) rates regulating the transitions between the compartments S→I and I→R, respectively. Accurate analytical approximations for the temporal dependence of the rate of new infections J˚(t)=a(t)S(t)I(t) and the corresponding cumulative fraction of new infections J(t)=J(t0)+∫t0tdxJ˚(x) are available in the literature for either stationary infection and recovery rates or for a stationary value of the ratio k(t)=μ(t)/a(t). Here, a new and original accurate analytical approximation is derived for general, arbitrary, and different temporal dependencies of the infection and recovery rates, which is valid for not-too-late times after the start of the infection when the cumulative fraction J(t)≪1 is much less than unity. The comparison of the analytical approximation with the exact numerical solution of the SIR equations for different illustrative examples proves the accuracy of the analytical approach.

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City Lockdown and Nationwide Intensive Community Screening Are Effective In Controlling COVID-19 Epidemic: Analysis Based on A Modified SIR Model

Cited by 4 publications

References 10 publications

Sentiment analysis of the NIGERIAN nationwide lockdown due to COVID19 outbreak

Sentiment analysis of the NIGERIAN nationwide lockdown due to COVID19 outbreak

An Improved SEIR Model Considering the Impact of Contact-Restricting Policies: A Case Study of COVID-19 in Wuhan

Analytical Solution of the Susceptible-Infected-Recovered/Removed Model for the Not-Too-Late Temporal Evolution of Epidemics for General Time-Dependent Recovery and Infection Rates

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