Prediction of the confirmed cases and deaths of global COVID-19 using artificial intelligence

Guo, Qingchun; He, Zhenfang

doi:10.1007/s11356-020-11930-6

Cited by 56 publications

(35 citation statements)

References 42 publications

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“…Congruent with a prior study [ 41 ], this study confirmed the effects of control measures. Regression outcomes provided coarse estimates of controlling performance comparisons of COVID-19 pandemic.…”

Section: Discussionsupporting

confidence: 88%

Panel Associations Between Newly Dead, Healed, Recovered, and Confirmed Cases During COVID-19 Pandemic

Guan

2021

J Epidemiol Glob Health

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Background Currently, the knowledge of associations among newly recovered cases (NR), newly healed cases (NH), newly confirmed cases (NC), and newly dead cases (ND) can help to monitor, evaluate, predict, control, and curb the spreading of coronavirus disease 2019 (COVID-19). This study aimed to explore the panel associations of ND, NH, and NR with NC. Methods Data from China Data Lab in Harvard Dataverse with China (January 15, 2020 to January 14, 2021), the United States of America (the USA, January 21, 2020 to April 5, 2021), and the World (January 22, 2020 to March 20, 2021) had been analyzed. The main variables included in the present analysis were ND, NH, NR, and NC. Pooled regression, stacked within-transformed linear regression, quantile regression for panel data, random-effects negative binomial regression, and random-effects Poisson regression were conducted to reflect the associations of ND, NH, and NR with NC. Event study analyses were performed to explore how the key events influenced NC. Results Descriptive analyses showed that mean value of ND/NC ratio regarding China was more than those regarding the USA and the World. The results from tentative analysis reported the significant relationships among ND, NH, NR, and NC regarding China, the USA, and the World. Panel regressions confirmed associations of ND, NH, and NR with NC regarding China, the USA, and the World. Panel event study showed that key events influenced NC regarding USA and the World more greatly than that regarding China. Conclusion The findings in this study confirmed the panel associations of ND, NH, and NR with NC in the three datasets. The efficiencies of various control strategies of COVID-19 pandemic across the globe were compared by the regression outcomes. Future direction of research work could explore the influencing mechanisms of the panel associations.

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Section: Discussionsupporting

confidence: 88%

Panel Associations Between Newly Dead, Healed, Recovered, and Confirmed Cases During COVID-19 Pandemic

Guan

2021

J Epidemiol Glob Health

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“…Vector Machine(SVM) model, 26 Artificial Neural Networks (ANNs) 27 and other deep learning models 28,29 have been used in forecasting.…”

Section: Temporal Modelingmentioning

confidence: 99%

Use of Artificial Intelligence on spatio-temporal data to generate insights during COVID-19 pandemic: A Review

Jayatilaka

Hassan²,

Marikkar

et al. 2020

Preprint

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The COVID-19 pandemic, within a short time span, has had a significant impact on every aspect of life in almost every country on the planet. As it evolved from a local epidemic isolated to certain regions of China, to the deadliest pandemic since the influenza outbreak of 1918, scientists all over the world have only amplified their efforts to combat it. In that battle, Artificial Intelligence, or AI, with its wide ranging capabilities and versatility, has played a vital role and thus has had a sizable impact. In this review, we present a comprehensive analysis of the use of AI techniques for spatio-temporal modeling and forecasting and impact modeling on diverse populations as it relates to COVID-19. Furthermore, we catalogue the articles in these areas based on spatio-temporal modeling, intrinsic parameters, extrinsic parameters, dynamic parameters and multivariate inputs (to ascertain the penetration of AI usage in each sub area). The manner in which AI is used and the associated techniques utilized vary for each body of work. Majority of articles use deep learning models, compartment models, stochastic methods and numerous statistical methods. We conclude by listing potential paths of research for which AI based techniques can be used for greater impact in tackling the pandemic.

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“…The Chinese government launched a lockdown campaign on January 23, 2020. China closed business activities, restricted travel and required people to stay at home starting in late January of 2020 (Guo and He, 2021). These prevention and control actions are related to the air pollution during that period.…”

Section: Introductionmentioning

confidence: 99%

Changes in Air Quality from the COVID to the Post-COVID Era in the Beijing-Tianjin-Tangshan Region in China

Guo

Wang

et al. 2021

Aerosol Air Qual. Res.

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Prediction of the confirmed cases and deaths of global COVID-19 using artificial intelligence

Cited by 56 publications

References 42 publications

Panel Associations Between Newly Dead, Healed, Recovered, and Confirmed Cases During COVID-19 Pandemic

Panel Associations Between Newly Dead, Healed, Recovered, and Confirmed Cases During COVID-19 Pandemic

Use of Artificial Intelligence on spatio-temporal data to generate insights during COVID-19 pandemic: A Review

Changes in Air Quality from the COVID to the Post-COVID Era in the Beijing-Tianjin-Tangshan Region in China

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