Analysis of second outbreak of COVID-19 after relaxation of control measures in India

Yu, Xinchen; Qi, Guoyuan; Hu, Jianbing

doi:10.1007/s11071-020-05989-6

Cited by 29 publications

(33 citation statements)

References 15 publications

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“…The IP is located between Stages II (outbreak from Points O to A) and III (post-peak from Points B to Q) [ 15 ]. Figure 1 shows the relationship between IP days and the CNICs present on the x -axis and y -axis on the right, respectively.…”

Section: Methodsmentioning

confidence: 99%

“…The CNIC in a country/region can be modeled on an ogive curve (OC) to illustrate the pattern and forecast a future epidemic. The IP is defined at the moment of the outbreak to decrease after a peak [ 15 ]. However, there was still no agreement in determining the IP days, until now.…”

Section: Introductionmentioning

confidence: 99%

“…The infection days can be converted into a unit (named probit or logit [ 27 , 28 ]) between −5 and 5 on a continuum scale (on the x -axis from the left to the right side). The IP can then be determined at the moment of the outbreak to decrease around the IP [ 15 ]. The concept of importance is to create an OC for each country/area using an objective approach (e.g., modeling parameters) to evaluate the IP.…”

Section: Introductionmentioning

confidence: 99%

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Using the IPcase Index with Inflection Points and the Corresponding Case Numbers to Identify the Impact Hit by COVID-19 in China: An Observation Study

Wang

Chien

Chou

2021

IJERPH

110

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Coronavirus disease 2019 (COVID-19) occurred in Wuhan and rapidly spread around the world. Assessing the impact of COVID-19 is the first and foremost concern. The inflection point (IP) and the corresponding cumulative number of infected cases (CNICs) are the two viewpoints that should be jointly considered to differentiate the impact of struggling to fight against COVID-19 (SACOVID). The CNIC data were downloaded from the GitHub website on 23 November 2020. The item response theory model (IRT) was proposed to draw the ogive curve for every province/metropolitan city/area in China. The ipcase-index was determined by multiplying the IP days with the corresponding CNICs. The IRT model was parameterized, and the IP days were determined using the absolute advantage coefficient (AAC). The difference in SACOVID was compared using a forest plot. In the observation study, the top three regions hit severely by COVID-19 were Hong Kong, Shanghai, and Hubei, with IPcase indices of 1744, 723, and 698, respectively, and the top three areas with the most aberrant patterns were Yunnan, Sichuan, and Tianjin, with IP days of 5, 51, and 119, respectively. The difference in IP days was determined (χ2 = 5065666, df = 32, p < 0.001) among areas in China. The IRT model with the AAC is recommended to determine the IP days during the COVID-19 pandemic.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Using the IPcase Index with Inflection Points and the Corresponding Case Numbers to Identify the Impact Hit by COVID-19 in China: An Observation Study

Wang

Chien

Chou

2021

IJERPH

110

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“…4.3 EP effectiveness of the superior leading EP model Equations ( 49)-( 52) are substituted into Eqs. ( 35) and ( 36)- (38) after the partial derivative is obtained, and it is easy to obtain Eqs. ( 29)- (32).…”

Section: Analysis Of the Spontaneous Ep Modelmentioning

confidence: 99%

“…Bentout et al [ 37 ] used the least-squares method and the method of minimizing the sum of squares of residuals to fit the data and estimated the epidemiological parameters and the basic reproductive number. Yu et al [ 38 ] proposed a compartment model based on the least-squares method to study the development of COVID-19 in India after relaxing control. Hellewell et al [ 39 ] used the branching process model to analyze the effects of a simulation involving isolating patients and tracking close contacts.…”

Section: Introductionmentioning

confidence: 99%

Prevention schemes for future pandemic cases: mathematical model and experience of interurban multi-agent COVID-19 epidemic prevention

Yin

Zhang

2021

Nonlinear Dyn

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To enhance the effectiveness of epidemic prevention (EP) in urban sustainability transformation, joint prevention and control mechanism should be established to prevent and control the COVID-19 epidemic. The interurban multi-agent EP strategy, as a key component of this mechanism, includes the spontaneous EP model, the superior leading EP model, and the collaborative EP model. In this study, firstly, the theoretical mechanism of the interurban multiagent EP strategy was analyzed. Then, we proposed a three-party differential game model including factors such as the risk coefficient for the virus infection and EP experience teaching. Finally, prevention strategies, prevention efficiency, and prevention losses were compared under the three models based on theoretical analysis and numerical analysis. The results of this study are as follows. COVID-19 EP should be guided by a model of central government (CG) leadership, interurban collaboration, and social participation. The CG and urban governments (UGs) should comprehensively carry out COVID-19 EP from various aspects, including EP experience teaching, mass EP comfort, the utilization rate of EP funds, and the ability to implement strategies. During the course of the COVID-19 EP, when the CG and UGs transition from spontaneous EP model to a higher-level EP model, the UG's EP efforts will be enhanced. Under the collaborative EP model, the CG and UGs undergo the highest levels of EP effort. Compared with spontaneous EP model, the superior leading EP model can promote a Pareto improvement for all parties. From the perspective of total loss, the collaborative EP model is superior to the other two EP models. This study not only provides practical guidance for coordinating interurban relationships and enabling multiagents to fully form joint forces, but also provides theoretical support for the establishment of an interurban joint EP mechanism under unified leadership.Keywords COVID-19 Á Joint prevention and control Á Urban governance Á Multi-agent collaboration Á Urban sustainability transformation Á Differential game

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Dynamics of Coronavirus and Malaria Diseases: Modeling and Analysis

Rehman

Singh

2022

Nonlinear Dynamics and Applications

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Analysis of second outbreak of COVID-19 after relaxation of control measures in India

Cited by 29 publications

References 15 publications

Using the IPcase Index with Inflection Points and the Corresponding Case Numbers to Identify the Impact Hit by COVID-19 in China: An Observation Study

Using the IPcase Index with Inflection Points and the Corresponding Case Numbers to Identify the Impact Hit by COVID-19 in China: An Observation Study

Prevention schemes for future pandemic cases: mathematical model and experience of interurban multi-agent COVID-19 epidemic prevention

Dynamics of Coronavirus and Malaria Diseases: Modeling and Analysis

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