Spatial Statistics and Influencing Factors of the COVID-19 Epidemic at Both Prefecture and County Levels in Hubei Province, China

Xiong, Yan; Wang, Yunpeng; Chen, Feng; Zhu, Mingyong

doi:10.3390/ijerph17113903

Cited by 84 publications

(99 citation statements)

References 52 publications

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“…On 23 January 2020, restrictions on mobility were imposed on Wuhan city, and partial movement restrictions were enacted in numerous cities across China. Prior studies show the positive effect of restriction of human mobility on the mitigation of the COVID-19 spread in China [ 9 , 10 , 11 , 12 ]. COVID-19 has shown the improvement of China’s global health technology and capability [ 13 ].…”

Section: Background Literature and Hypothesis Developmentmentioning

confidence: 99%

Risk Assessment on Continued Public Health Threats: Evidence from China’s Stock Market

2020

IJERPH

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Given the disturbing effects of the coronavirus disease 2019 (COVID-19) outbreak, we are motivated to examine whether the continued increase of the provincial public health threats affects the firms’ accumulative abnormal return. Using the 178,805 firm-day observations from Chinese listed firms from 10 January to 31 March 2020, we find that the accumulative abnormal return is significantly lower among firms located in the provinces where face the continued increase of new confirmed COVID-19 cases. The relations remain constant after several robustness tests. These findings suggest that investors concern about the potential risk when firms are located in the provinces with higher threats to public health. We also find that the negative effect of increasing public health threats on abnormal return is weaker for firms surrounded by a provincial environment with stronger information accessibility and economic growth. Overall, this study extends the literature by presenting systematic evidence on the effect of the continued increase of provincial public health threats on the market reaction in Chinese listed firms.

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Section: Background Literature and Hypothesis Developmentmentioning

confidence: 99%

Risk Assessment on Continued Public Health Threats: Evidence from China’s Stock Market

2020

IJERPH

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“…[52-55] ❖ Travelling [56] ❖ Medical condition [35,36] ❖ Age [37][38][39] ❖ Environmental [40][41][42][43] ❖ Socio-economic [44][45][46][47][48][49][50][51] ❖ Population density and income [57] ❖ Population density, health resources, and political [58] ❖ Geographical location, age, household structure, and medical [59] ❖ Population, migration index, GPD, and consumer metrics [60] ❖ Age, sex, race, economic status, and population flow [61] EXTRINSIC INTRINSIC DYNAMIC MULTIVARIATE Figure 4: Impact Modeling. The reference numbers of research papers are given in square brackets.…”

Section: ❖ Population Flowmentioning

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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“…Several studies have shown that multiple factors, such as air pollution ( Conticini et al, 2020 ; Muhammad et al, 2020 ; Ogen, 2020 ; Tobías et al, 2020 ; Sannigrahi et al, 2020e ), climatic conditions ( Ahmadi et al, 2020 ; Bashir et al, 2020 ; Sobral et al, 2020 ), environmental phenomena ( Bao and Zhang, 2020 ; Jahangiri et al, 2020 ; Sharma et al, 2020 ; Xie and Zhu, 2020 ) as well as other socio- demographic factors ( Mollalo et al, 2020 ; Xiong et al, 2020 ; Bolaño-Ortiz et al, 2020 ; Sannigrahi et al, 2020a , Sannigrahi et al, 2020b ) are associated with COVID-19 incidents, casualties and spread. For example, Bashir et al (2020) found that environmental pollutants like carbon monoxide (CO), particulate matter (PM 2.5 , PM 10), Nitrogen dioxide (NO 2 ) are closely correlated with the COVID-19 cases in California.…”

Section: Introductionmentioning

confidence: 99%

“… Dowd et al (2020) also found that countries with higher proportion of older and younger population could be a burden on public health. Beside these, migration and mobility played a significant role in the spreading of COVID-19 ( Kraemer et al, 2020 ; Oztig and Askin, 2020 ; Xiong et al, 2020 ). All these studies are combined indicating a heterogeneous association between social, economic, demographic, environmental influencing factors and COVID-19 across the scale.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the plausible application of advanced machine learnings in exploring determinant factors of present pandemic: A case for continent specific COVID-19 analysis

Chakraborti

Maiti

Pramanik

et al. 2021

Science of The Total Environment

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Coronavirus disease, a novel severe acute respiratory syndrome (SARS COVID-19), has become a global health concern due to its unpredictable nature and lack of adequate medicines. Machine Learning (ML) models could be effective in identifying the most critical factors which are responsible for the overall fatalities caused by COVID-19. The functional capabilities of ML models in epidemiological research, especially for COVID-19, are not substantially explored. To bridge this gap, this study has adopted two advanced ML models, viz. Random Forest (RF) and Gradient Boosted Machine (GBM), to perform the regression modelling and provide subsequent interpretation. Five successive steps were followed to carry out the analysis: (1) identification of relevant key explanatory variables; (2) application of data dimensionality reduction for eliminating redundant information; (3) utilizing ML models for measuring relative influence (RI) of the explanatory variables; (4) evaluating interconnections between and among the key explanatory variables and COVID-19 case and death counts; (5) time series analysis for examining the rate of incidences of COVID-19 cases and deaths. Among the explanatory variables considered in this study, air pollution, migration, economy, and demographic factor were found to be the most significant controlling factors. Since a very limited research is available to discuss the superiority of ML models for identifying the key determinants of COVID-19, this study could be a reference for future public health research. Additionally, all the models and data used in this study are open source and freely available, thereby, reproducibility and scientific replication will be achievable easily.

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Spatial Statistics and Influencing Factors of the COVID-19 Epidemic at Both Prefecture and County Levels in Hubei Province, China

Cited by 84 publications

References 52 publications

Risk Assessment on Continued Public Health Threats: Evidence from China’s Stock Market

Risk Assessment on Continued Public Health Threats: Evidence from China’s Stock Market

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

Evaluating the plausible application of advanced machine learnings in exploring determinant factors of present pandemic: A case for continent specific COVID-19 analysis

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