Impact analysis of environmental and social factors on early-stage COVID-19 transmission in China by machine learning

Han, Yifei; Huang, Jinliang; Li, Rendong; Shao, Quanqin; Dong-feng, Han; Luo, Xiyue; Qiu, Juan

doi:10.1016/j.envres.2022.112761

Cited by 13 publications

(12 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There were also many studies showing that air pollution ( 17 ) and regional culture ( 18 ) impact the behavior and health of residents. Additionally, COVID-19 is more likely to occur in prosperous cities closer to the epicenter, located on higher altitudes, with high concentrations of air pollutants except NO 2 and O 3 , under conditions of extreme weather and high minimum relative humidity ( 19 ). Zhao et al ( 20 ) found the residents' environmental health literacy (EHL) level of urban residents is significantly higher than that of rural residents in Shaanxi Province, so they stressed the importance of education and popularizing basic environmental health knowledge.…”

Section: Introductionmentioning

confidence: 99%

Study of the Economic, Environmental, and Social Factors Affecting Chinese Residents' Health Based on Machine Learning

Pan²,

Meng³

et al. 2022

Front. Public Health

View full text Add to dashboard Cite

The Healthy China Strategy puts realistic demands for residents' health levels, but the reality is that various factors can affect health. In order to clarify which factors have a great impact on residents' health, based on China's provincial panel data from 2011 to 2018, this paper selects 17 characteristic variables from the three levels of economy, environment, and society and uses the XG boost algorithm and Random forest algorithm based on recursive feature elimination to determine the influencing variables. The results show that at the economic level, the number of industrial enterprises above designated size, industrial added value, population density, and per capita GDP have a greater impact on the health of residents. At the environmental level, coal consumption, energy consumption, total wastewater discharge, and solid waste discharge have a greater impact on the health level of residents. Therefore, the Chinese government should formulate targeted measures at both economic and environmental levels, which is of great significance to realizing the Healthy China strategy.

show abstract

Section: Introductionmentioning

confidence: 99%

Study of the Economic, Environmental, and Social Factors Affecting Chinese Residents' Health Based on Machine Learning

Pan²,

Meng³

et al. 2022

Front. Public Health

View full text Add to dashboard Cite

show abstract

“…In our study, we used Python 3.8.8 with the package Scikitlearn 0.24.2 to build a Gradient Boosting Decision Tree (GBDT) based classification model to conduct RFE. Similar to the popular Random Forest (RF) algorithm, GBDT is also in the form of ensemble decision trees but usually performs better than RF does [34]. The Gini index which represents the importance of each parameter can be calculated by the following equation [35]:…”

Section: Ndvi = (Nir -R) / (Nir + R)mentioning

confidence: 99%

Dynamic Mapping of Inland Freshwater Aquaculture Areas in Jianghan Plain, China

Han

Huang

Ling

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

Freshwater aquaculture in Jianghan Plain plays a key role in the whole industry of China. It was expanding rapidly to meet the fast-growing demand of consumption in these decades. The spatial distribution change of aquaculture in Jianghan Plain has attracted many researchers in recent years and it is worth further investigating. However, the accuracy and the quality of inland aquaculture classification and mapping still have space to be improved. Our study attempts to use Sentinel-1 and Sentinel-2 data to classify aquaculture areas, non-aquaculture water, and non-water areas. We applied image segmentation and pixel-based feature computation to obtain candidate datasets. Recursive feature elimination (RFE) was used to find the optimal datasets which were then imported into a modified UNet for classification. This workflow was then applied to the annual datasets for better observation of the spatial change of aquaculture areas. In total 12 indices and features were found most influential and preserved after RFE. The overall classification accuracy reached 96.83%, while the precision and recall of aquaculture areas reached 84.47% and 90.48%, respectively. The combination of object-based and pixel-based image analysis showed its advantage in inland aquaculture classification compared to other studies. Integrated rice-crawfish farming was found a key factor motivating the rapid development of aquaculture. Policies about the fishing ban and ecosystem restoration were also found deeply affecting the spatial change of aquaculture in Jianghan Plain.

show abstract

“…Furthermore, they argue about strategies that will be useful for legislators to improve the environment. In another study, Han et al (2022) used ML methods to predict COVID-19 occurrence and intensity in China using several input parameters. Out of 113 input parameters, they eliminated some which have a negligible effect on COVID.…”

Section: Machine Learningmentioning

confidence: 99%

A sustainable trend in COVID-19 research: An environmental perspective

Vadiati

Ghasemi²,

Samani³

et al. 2023

Front. Environ. Sci.

View full text Add to dashboard Cite

Coronavirus disease 2019 (COVID-19) has spread across the globe producing hundreds of thousands of deaths, shutting down economies, closing borders and causing havoc on an unprecedented scale. Its potent effects have earned the attention of researchers in different fields worldwide. Among them, authors from different countries have published numerous research articles based on the environmental concepts of COVID-19. The environment is considered an essential receptor in the COVID-19 pandemic, and it is academically significant to look into publications to follow the pathway of hot topics of research and upcoming trends in studies. Reviewing the literature can therefore provide valuable information regarding the strengths and weaknesses in facing the COVID-19 pandemic, considering the environmental viewpoint. The present study categorizes the understanding caused by environmental and COVID-19-related published papers in the Scopus metadata from 2020 to 2021. VOSviewer is a promising bibliometric tool used to analyze the publications with keywords “COVID-19*” and “Environment.” Then, a narrative evaluation is utilized to delineate the most interesting research topics. Co-occurrence analysis is applied in this research, which further characterizes different thematic clusters. The published literature mainly focused on four central cluster environmental concepts: air pollution, epidemiology and virus transmission, water and wastewater, and environmental policy. It also reveals that environmental policy has gained worldwide interest, with the main keyword “management” and includes keywords like waste management, sustainability, governance, ecosystem, and climate change. Although these keywords could also appear in other environmental policy-related research studies, the importance of the COVID-19 pandemic requires such comprehensive research. The fourth cluster involves governance and management concerns encountered during the pandemic. Mapping the research topics in different clusters will pave the way for researchers to view future potential ideas and studies better. The scope for further research needs from the perspective of environmental concepts is reviewed and recommended, which can expand the vital role and value of environmental sciences in alerting, observing, and COVID-19 prediction for all four clusters. In other words, the research trend would shift from qualitative studies and perspectives to quantitative ones.

show abstract

Impact analysis of environmental and social factors on early-stage COVID-19 transmission in China by machine learning

Cited by 13 publications

References 29 publications

Study of the Economic, Environmental, and Social Factors Affecting Chinese Residents' Health Based on Machine Learning

Study of the Economic, Environmental, and Social Factors Affecting Chinese Residents' Health Based on Machine Learning

Dynamic Mapping of Inland Freshwater Aquaculture Areas in Jianghan Plain, China

A sustainable trend in COVID-19 research: An environmental perspective

Contact Info

Product

Resources

About