An evaluation of COVID-19 in Italy: A data-driven modeling analysis

Ding, Yongmei; Gao, Liyuan

doi:10.1016/j.idm.2020.06.007

Cited by 24 publications

(16 citation statements)

References 13 publications

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“…Lockdowns and the effectiveness of reduction in the contacts in Italy have been measured via our modified model. The results show a decrease in infected people due to stay-at-home orders and tracing quarantine interventions 2 .…”

Section: Introductionmentioning

confidence: 91%

Panel Data Modelling of COVID-19 Infected Cases

Arunachalam

Pakkirisamy

2021

Preprint

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Background and Objective: The novel coronavirus pandemic, known as COVID-19, could not have been more predictable; thus, the world encountered health crises and substantial economic crises. This paper analyses the trends in COVID-19 cases in October 2020 in four southern districts of Tamil Nadu state, India, using a panel regression model. Materials and Methods: Panel data on the number of COVID-19-infected cases were collected from daily bulletins published through the official website www.stopcorona.tn.gov.in maintained by the Government of Tamil Nadu state, India. Panel data regression models were employed to study the trends. EViews Ver.11. Software was used to estimate the model and its parameters. Results: In all four districts, the COVID-19-infected case data followed a normal distribution. Maximum numbers of COVID-19-infected cases were registered in Kanniyakumari, followed by Tirunelveli, Thoothukudi and Tenkasi districts. The fewest COVID-19 cases were registered in Tenkasi, followed by Tirunelveli, Thoothukudi and Kanniyakumari districts. A random e2ffects model was found to be an appropriate model to study the trend.Conclusion: The panel data regression model is found to be more appropriate than traditional models. The Hausman test and Wald test confirmed the selection of the random effects model. The Jarque-Bera normality test ensured the normality of the residuals. In all four districts under study, the number of COVID-19 infections showed a decreasing trend at a rate of 1.68% during October 2020.

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Section: Introductionmentioning

confidence: 91%

Panel Data Modelling of COVID-19 Infected Cases

Arunachalam

Pakkirisamy

2021

Preprint

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“…Lockdowns and the effectiveness of reduction in the contacts in Italy have been measured using the modified model. The results showed a decrease in infected people due to stay-at-home orders and tracing quarantine interventions 2 .The prediction, regarding the number of COVID-19 cases in India based on differential equation model. The model shows that the imposition of a countrywide lockdown plays a important role in restraining the spread of the disease 3 .…”

Section: Introductionmentioning

confidence: 97%

Estimating Trends in COVID-19 Infected Cases Based on Panel Data Regression Modelling

Rajarathin¹,

Tamilselva²

2021

J. of Applied Sciences

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Background and Objective:The novel coronavirus pandemic, known as COVID-19, could not have been more predictable; thus, the world encountered health crises and substantial economic crises. This paper analysed the trends in COVID-19 cases in October 2020 in four southern districts of Tamil Nadu state, India, using a panel regression model. Materials and Methods: Panel data on the number of COVID-19-infected cases were collected from daily bulletins published through the official website www.stopcorona.tn.gov.in maintained by the Government of Tamil Nadu state, India. Panel data regression models were employed to study the trends. EViews Ver.11. Software was used to estimate the model and its parameters. Results: In all four districts, the COVID-19-infected case data followed a normal distribution. Maximum numbers of COVID-19-infected cases were registered in Kanniyakumari, followed by Tirunelveli, Thoothukudi and Tenkasi districts. The fewest COVID-19 cases were registered in Tenkasi, followed by Tirunelveli, Thoothukudi and Kanniyakumari districts. A random effects model was found to be an appropriate model to study the trend. Conclusion: The panel data regression model is found to be more appropriate than traditional models. The Hausman test and Wald test confirmed the selection of the random effects model. The Jarque-Bera normality test ensured the normality of the residuals. In all four districts under study, the number of COVID-19 infections showed a decreasing trend at a rate of 1.68% during October 2020.

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“…Ding and Gao [14] propose to evaluate the effect of public health intervention and expect to search the faster way to prevent the COVID-19 in Italy, by means of a data-driven modelling analysis. They consider that the exponential model is not suitable to the Italian situation and tried the SEIR model and modified its parameters’ system according to this country lockdown state; they also considered asymptomatic cases since the similar SIR model was applied in the process of interpretation to prevent and control COVID-19 in China.…”

Section: Introductionmentioning

confidence: 99%