Economic globalization and the COVID-19 pandemic: global spread and inequalities

Jeanne, Ludovic; Bourdin, Sébastien; Nadou, Fabien; Noiret, Gabriel

doi:10.1007/s10708-022-10607-6

Cited by 44 publications

(25 citation statements)

References 29 publications

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“…Figure 3a applying Eq 1. to the CR values (lag=0.83 yr, valueAmpl=6000, bias=-25000 deaths). Figure 3b applying Eq (1).…”

Section: Resultsmentioning

confidence: 99%

“…In 2020, 2021 and 2022, the COVID-19 pandemic has unprecedently affected health´s people and enhancing inequalities (Jeanne et al, 2020) [1], but also has contributed to global recession with adverse consequences for unemployment and poverty (IMF/WB, 2020) [2]. This extreme situation, demands stronger and efficient states that provide a "healthy" balance of the give-and-take relationship between the state and the individual, offering the benefits of the accumulated advances of science, technology and social sciences to the lay people [3].…”

Section: Economic Globalization and The Covid-19 Pandemic: Global Spr...mentioning

confidence: 99%

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Cosmic rays lagged influences on COVID-19 pandemic

Sánchez-Sesma¹

2022

Braz. J. Hea. Rev.

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Aims:The present COVID-19 pandemic (C19P) has been challenging our socities all over the world. In this work, based on massive health information daily updated, 2020- 2022, the C19P daily death numbers at a global level, are empirically modelled, analyzed and forecasted. Methods: An empirical lagged cosmic ray model is proposed to explain the C19P global daily death (GDD) record. This GDD record was modelled, with consequent forecasts for the rest of the 2022 and 2023. Complementarily, two important records for US influenza and global measles are also modelled with cosmic-ray influences with decreasing lags. Findings: The detected lagged cosmic ray inluences suggest a decaying stage for the next year (August 2022-August 2023). Self-similar modelling of errors detect and forecast decaying impacts, due to vaccine campaigns, that suggest a decaying final phase of C19P. This scenario, after to be calibrated with other self-similar models, has provided to stakeholders of significant data and criteria to define, sustain and support plans for the next year, based on cosmic ray influences. Conclusion: The empirical global modeling of the C19P based on cosmic ray influences has strongly suggested, not only a decaying scenario, but also the complex sensitivity of living organisms to variable solar (cosmic) radiation. Certainly, further research is needed. However, these scenarios provide a well-based perspective on non-linear dynamics of C19P and other future pandemics, that will complement the standard health and economic models.

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“…Figure 3a applying Eq 1. to the CR values (lag=0.83 yr, valueAmpl=6000, bias=-25000 deaths). Figure 3b applying Eq (1).…”

Section: Resultsmentioning

confidence: 99%

Section: Economic Globalization and The Covid-19 Pandemic: Global Spr...mentioning

confidence: 99%

Cosmic rays lagged influences on COVID-19 pandemic

Sánchez-Sesma¹

2022

Braz. J. Hea. Rev.

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“…However, the data is mainly taken from open datasets, and real-time clinical data is not considered in the study. Jeanne et al [41] combined economic variables and globally collected infected patients data and drew up a linear regression model to identify the economic factor affected by the number of increasing COVID-19 positive cases worldwide. Bhattacharya et al [42] proposed the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\theta $ \end{document} -ARNN approach as an effective forecasting that captures the linear and non-linear patterns of COVID-19 patients.…”

Section: State-of-the-artmentioning

confidence: 99%

ABV-CoViD: An Ensemble Forecasting Model to Predict Availability of Beds and Ventilators for COVID-19 Like Pandemics

et al. 2022

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Recently, healthcare stakeholders have orchestrated steps to strengthen and curb the COVID-19 wave. There has been a surge in vaccinations to curb the virus wave, but it is crucial to strengthen our healthcare resources to fight COVID-19 and like pandemics. Recent researchers have suggested effective forecasting models for COVID-19 transmission rate, spread, and the number of positive cases, but the focus on healthcare resources to meet the current spread is not discussed. Motivated from the gap, in this paper, we propose a scheme, ABV-CoViD (Availibility of Beds and Ventilators for COVID-19 patients), that forms an ensemble forecasting model to predict the availability of beds and ventilators (ABV) for the COVID-19 patients. The scheme considers a region-wise demarcation for the allotment of beds and ventilators (BV), termed resources, based on region-wise ABV and COVID-19 positive patients (inside the hospitals occupying the BV resource). We consider an integration of artificial neural network (ANN) and auto-regressive integrated neural network (ARIMA) model to address both the linear and non-linear dependencies. We also consider the effective wave spread of COVID-19 on external patients (not occupying the BV resources) through a θ-ARNN model, which gives us long-term complex dependencies of BV resources in the future time window. We have considered the COVID-19 healthcare dataset on 3 USA regions (Illinois, Michigan, and Indiana) for testing our ensemble forecasting scheme from January 2021 to May 2022. We evaluated our scheme in terms of statistical performance metrics and validated that ensemble methods have higher accuracy. In simulation, for linear modelling, we considered the ARIM A(1, 0, 12) model, and N 8−3−2 model for ANN modelling. We considered the θ − ARN N (12, 6) forecasting. On a population of 2, 93, 90, 897, the obtained mean absolute error (MAE) on average for 3 regions is 170.5514. The average root means square error (RMSE) of θ-ARNN is 333.18, with an accuracy of 98.876%, which shows the scheme's efficacy in ABV measurement over conventional and manual resource allocation schemes.

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“…On January 30, 2020, the World Health Organization (WHO) declared COVID-19 as a Public Health Emergency of International Concern 1 and by March 11, 2020, declared the first pandemic caused by the coronavirus. Up to July 2021, COVID-19 has affected over 187 million people with more than 4 million associated deaths and in addition, has induced catastrophic public health and socio-economic affliction globally ( 1 ).…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal evolution of the COVID-19 across African countries

Naffeti

Bourdin

Aribi

et al. 2022

Front. Public Health

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The aim of this study is to make a comparative study on the reproduction number R0 computed at the beginning of each wave for African countries and to understand the reasons for the disparities between them. The study covers the two first years of the COVID-19 pandemic and for 30 African countries. It links pandemic variables, reproduction number R0, demographic variable, median age of the population, economic variables, GDP and CHE per capita, and climatic variables, mean temperature at the beginning of each waves. The results show that the diffusion of COVID-19 in Africa was heterogeneous even between geographical proximal countries. The difference of the basic reproduction number R0 values is very large between countries and is significantly correlated with economic and climatic variables GDP and temperature and to a less extent with the mean age of the population.

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Economic globalization and the COVID-19 pandemic: global spread and inequalities

Cited by 44 publications

References 29 publications

Cosmic rays lagged influences on COVID-19 pandemic

Cosmic rays lagged influences on COVID-19 pandemic

ABV-CoViD: An Ensemble Forecasting Model to Predict Availability of Beds and Ventilators for COVID-19 Like Pandemics

Spatio-temporal evolution of the COVID-19 across African countries

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