The Influence of Potential Infection on the Relationship between Temperature and Confirmed Cases of COVID-19 in China

Lin, Weiran; He, Qiuqin

doi:10.3390/su13158504

Cited by 2 publications

(3 citation statements)

References 28 publications

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“…They used the classic copula model whose parameters are static. Lin and He ( 2021 ) analyzed the relationship between temperature and confirmed case by considering the impact of the number of potential infection using the semiparametric multivariate density estimation. The joint probability density between the two variables is formed using static Gumbel-Hoougard copula.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Unlike other studies which mostly identify factors that influence COVID-19 cases through a cross-sectional approach (Jamshidi et al. 2021 ; Lin and He 2021 ; D’Urso et al. 2022 ), this research focuses on a temporal approach to identify the dependencies between percentage change in human mobility and the number of active COVID-19 cases because both variables are dynamic.…”

Section: Introductionmentioning

confidence: 99%

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Considering the temporal interdependence of human mobility and COVID-19 concerning Indonesia’s large-scale social distancing policies

2022

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The year 2020 has marked the beginning of a new life in which humans must struggle and adapt to coexist with a new coronavirus, known as COVID-19. Population density is one of the most significant factors affecting the speed of COVID-19’s spread, and it is closely related to human activity and movement. Therefore, many countries have implemented policies that restrict human movement to reduce the risk of transmission. This study aims to identify the temporal dependence between human mobility and virus transmission, indicated by the number of active cases, in the context of large-scale social restriction policies implemented by the Indonesian government. This analysis helps identify which government policies can significantly reduce the number of active COVID-19 cases in Indonesia. We conducted a temporal interdependency analysis using a time-varying Gaussian copula, where the parameter fluctuates throughout the observation. We use the percentage change in human mobility data and the number of active COVID-19 cases in Indonesia from March 28, 2020, to July 9, 2021. The results show that human mobility in public areas significantly influenced the number of active COVID-19 cases. Moreover, the temporal interdependencies between the two variables behaved differently according to the implementation period of large-scale social distancing policies. Among the five types of policies implemented in Indonesia, the policy that had the most significant influence on the number of active COVID-19 cases was several restrictions during the Implementation of Restrictions on Community Activities (Pelaksanaan Pembatasan Kegiatan Masyarakat/PPKM) period. We conclude that the strictness of rules restricting social activities generally affected the number of active COVID-19 cases, especially in the early days of the pandemic. Finally, the government can implement policies that are at least equivalent to the rules in PPKM if, in the future, cases of COVID-19 spike again.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Considering the temporal interdependence of human mobility and COVID-19 concerning Indonesia’s large-scale social distancing policies

2022

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“…Markovich [9] discussed the good characteristics of the Gamma kernel estimator and extended it to the estimation of multivariate density functions and their partial derivatives. Based on the Gamma estimator, Lin [10] discussed the relationship between temperature and number of potential novel coronavirus infections in China. However, Zhang [11,12] showed that the Beta kernel estimator and Gamma kernel estimator have serious boundary problems and perform worse than the well-known boundary kernel estimation method, when the true density function does not meet the shoulder condition (that is, the first derivative of the density function at the boundary is 0).…”

Section: Introductionmentioning

confidence: 99%

An Improved Model for Kernel Density Estimation Based on Quadtree and Quasi-Interpolation

2022

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There are three main problems for classical kernel density estimation in its application: boundary problem, over-smoothing problem of high (low)-density region and low-efficiency problem of large samples. A new improved model of multivariate adaptive binned quasi-interpolation density estimation based on a quadtree algorithm and quasi-interpolation is proposed, which can avoid the deficiency in the classical kernel density estimation model and improve the precision of the model. The model is constructed in three steps. Firstly, the binned threshold is set from the three dimensions of sample number, width of bins and kurtosis, and the bounded domain is adaptively partitioned into several non-intersecting bins (intervals) by using the iteration idea from the quadtree algorithm. Then, based on the good properties of the quasi-interpolation, the kernel functions of the density estimation model are constructed by introducing the theory of quasi-interpolation. Finally, the binned coefficients of the density estimation model are constructed by using the idea of frequency replacing probability. Simulation of the Monte Carlo method shows that the proposed non-parametric model can effectively solve the three shortcomings of the classical kernel density estimation model and significantly improve the prediction accuracy and calculation efficiency of the density function for large samples.

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The Influence of Potential Infection on the Relationship between Temperature and Confirmed Cases of COVID-19 in China

Cited by 2 publications

References 28 publications

Considering the temporal interdependence of human mobility and COVID-19 concerning Indonesia’s large-scale social distancing policies

Considering the temporal interdependence of human mobility and COVID-19 concerning Indonesia’s large-scale social distancing policies

An Improved Model for Kernel Density Estimation Based on Quadtree and Quasi-Interpolation

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