Multi-scale causality analysis between COVID-19 cases and mobility level using ensemble empirical mode decomposition and causal decomposition

Cho, Jung-Hoon; Kim, Dong Kyu; Kim, Eui-Jin

doi:10.1016/j.physa.2022.127488

Cited by 8 publications

(5 citation statements)

References 53 publications

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“…On the contrary, when the number of people infected with COVID-19 decreases, the economy gradually recovers, workers start to take jobs, incomes increase, and the month-on-month growth of CPI decrease. This means that the number of people infected with COVID-19 is proportional to the month-on-month growth of CPI, which can also be reflected by the symbol of IC3 in Equation (7). In terms of correlation and trend comparison, the month-on-month growth of CPI can be seen as an influencing factor corresponding to IC3.…”

Section: The Relationship and Comparison Between Ics And Important Fa...mentioning

confidence: 98%

“…For predicting the COVID-19 outbreak, Hasan et al [6] suggested a hybrid model combining artificial neural network (ANN) and EEMD. Cho et al [7] used EEMD to obtain the intrinsic modal functions (IMFs) of high frequency, medium frequency and low frequency, and then analyzed the relationship between mobility level and COVID-19 from the short-term, medium-term and long-term. Further, in order to study the internal influencing factors of COVID-19, this paper introduces ICA based on EEMD.…”

Section: Introductionmentioning

confidence: 99%

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Factor analysis of COVID-19 in China based on modal decomposition and independent component analysis

Hou,

2023

Second International Conference on Applied Statistics, Computational Mathematics, and Software Engineering (ASCMSE 2023)

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Since December 2019, the novel coronavirus disease 2019 (COVID-19) has erupted around the world, seriously affecting people's lives, hindering economic development, and threatening public health security. As a result, it is critical to investigate the COVID-19 impacting elements to take the required steps to deal with the current crisis and minimize potential hazards in the future. Based on data from daily confirmed COVID-19 cases in China, this data series is first decomposed into multiple intrinsic modal functions (IMFs) using modal decomposition, and then the IMFs are restructured according to the relative hamming distance (RHD). To investigate the influence of factors internal to COVID-19, statistically independent sub-series (ICs) are isolated from the restructured modalities using independent component analysis (ICA). In addition, it is known that temperature, the consumer expectation index (CEI), the migration scale index (MSI), and the month-on-month growth of the consumer price index (CPI) have an impact on COVID-19. Finally, the analysis shows that the trend between ICs and these factors is highly similar, thus identifying several relatively independent main influencing factors of COVID-19 from the data itself.

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Section: The Relationship and Comparison Between Ics And Important Fa...mentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Factor analysis of COVID-19 in China based on modal decomposition and independent component analysis

Hou,

2023

Second International Conference on Applied Statistics, Computational Mathematics, and Software Engineering (ASCMSE 2023)

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“…In contrast, approximately 61 million infections are delayed as a result of the intervention, equating to the avoidance of 495 million confirmed cases ( Hsiang et al, 2020 ). In general, regions with higher GDP and higher levels of political democracy tend to be more insistent on maintaining social distance ( Cho et al, 2022 ). And the longer the policy lasts and the more rigorously it is enforced, the more effective it becomes ( Sun et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Interaction between travel restriction policies and the spread of COVID-19

et al. 2023

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“…The pandemic caused by SARS-CoV-2 started in December 2019 in China and spread to 195 countries affecting more than 670 million people and causing more than 6.8 million deaths [ 1 ]. The incidence and mortality among different countries varied due to a complex interaction between factors [ 2 ], some related to the particularity of each country such as geography, population density, altitude levels, humidity and temperature [ 3 ], others related to the population [ 4 ] such as migration [ 5 , 6 ], socioeconomic status [ 7 ], health status and presence of comorbidities; as well as the public policies use [ 6 , 8 ]. In addition to conditions related to the precision of the data such as the amount and type of diagnostic tests used, the report of cases to the coordinating institution and in relation with mortality, the capacity of health attention for each place.…”

Section: Introductionmentioning

confidence: 99%

A U-shaped protection of altitude against mortality and infection of COVID-19 in Peru: an ecological study

et al. 2023

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Background The COVID-19 pandemic has affected the world in multiple ways and has been a challenge for the health systems of each country. From the beginning, risk factors for the severity and mortality of the disease were considered, as the spread of the virus was related to the living conditions of each population. Methods In this ecological study we have evaluated the role of geography, precisely the altitude above sea level in the incidence and mortality of COVID-19 in Peru. Incidence and mortality data were taken from the open-access database of the government of Peru until March 2021. COVID-19 cases and COVID-19 mortality were treated as cases/density population and 1000 x cases/inhabitants while altitude was treated as continuous and as a categorical variable divided in 7 categories. The relationship between COVID-19 cases or deaths for COVID-19 and altitude as continuous variable was determined using Spearman correlation test. Meanwhile when altitude was considered as a categorical variable, Poisson regression or negative binomial analyses were applied. Results A significant inverse correlation was found between COVID-19 cases by population density and altitude (r=-0.37 p < 0.001). By altitude categories, the lowest risk for infection was observed between 3,000 and 3,500 m (IRR 0.08; 95% CI 0.05,0.12). Moreover, we found an inverse correlation between altitude and COVID-19 mortality (r=-0.39 p < 0.001). Also, the lowest risk for mortality was observed between 3,000 and 3,500 m (IRR 0.12; 95%CI 0.08; 0.18). Similar results were found when analyses were adjusted for inhabitants and stratified by sex. Conclusion This study reports an inverse relationship between COVID-19 incidence and mortality with respect to the altitude of residence, particularly, a u-shaped protection is shown, with a highest benefit between 3000 and 3500 m. The possibility of using hypoxia as an alternative treatment requires more complex studies that should allow knowing the physiological and environmental mechanisms of the protective role.

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Multi-scale causality analysis between COVID-19 cases and mobility level using ensemble empirical mode decomposition and causal decomposition

Cited by 8 publications

References 53 publications

Factor analysis of COVID-19 in China based on modal decomposition and independent component analysis

Factor analysis of COVID-19 in China based on modal decomposition and independent component analysis

Interaction between travel restriction policies and the spread of COVID-19

A U-shaped protection of altitude against mortality and infection of COVID-19 in Peru: an ecological study

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