Exploring the Relationship among Human Activities, COVID-19 Morbidity, and At-Risk Areas Using Location-Based Social Media Data: Knowledge about the Early Pandemic Stage in Wuhan

Yuan, Mengyue; Liu, Tong; Yang, Chao

doi:10.3390/ijerph19116523

Cited by 5 publications

(3 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition to disease-related features and socio-demographics, pandemic spread is influenced by population activities and social interactions. Several features, including social distancing [20][21][22], visits to points of interest [23][24][25], trip distance [26], and interpersonal contact density [27,28] have been shown to have positive relationships with the COVID-19 spread risk.…”

Section: Features Influencing the Spread Of Covid-19mentioning

confidence: 99%

Attributed Network Embedding Model for Exposing COVID-19 Spread Trajectory Archetypes

et al. 2023

Preprint

View full text Add to dashboard Cite

The spread of COVID-19 revealed that transmission risk patterns are not homogenous across different cities and communities, and various heterogeneous features can influence the spread trajectories. Hence, for predictive pandemic monitoring, it is essential to explore latent heterogeneous features in cities and communities that distinguish their specific pandemic spread trajectories. To this end, this study creates a network embedding model capturing cross-county visitation networks, as well as heterogeneous features related to population activities, human mobility, socio-demographic features, disease attribute, and social interaction to uncover clusters of counties in the United States based on their pandemic spread transmission trajectories. We collected and computed location intelligence features from 2,787 counties from March 3 to June 29, 2020 (initial wave). Second, we constructed a human visitation network, which incorporated county features as node attributes, and visits between counties as network edges. Our attributed network embeddings approach integrates both typological characteristics of the cross-county visitation network, as well as heterogeneous features. We conducted clustering analysis on the attributed network embeddings to reveal four archetypes of spread risk trajectories corresponding to four clusters of counties. Subsequently, we identified four features—population density, GDP, minority status, and POI visits—as important features underlying the distinctive transmission risk patterns among the archetypes. The attributed network embedding approach and the findings identify and explain the non-homogenous pandemic risk trajectories across counties for predictive pandemic monitoring. The study also contributes to data-driven and deep learning-based approaches for pandemic analytics to complement the standard epidemiological models for policy analysis in pandemics.

show abstract

Section: Features Influencing the Spread Of Covid-19mentioning

confidence: 99%

Attributed Network Embedding Model for Exposing COVID-19 Spread Trajectory Archetypes

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Maxent machine learning models have also been widely used for the evaluation of regional epidemic risk levels [26,27]. In addition, a portion of research has also focused on a specific element for risk prediction, with building density [28] and human activity patterns [29] being used to determine and identify potential site transmission risk in cities.…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-Source Data and Machine Learning-Based Refined Governance for Responding to Public Health Emergencies in Beijing: A Case Study of COVID-19

Huang

Zang

et al. 2023

IJGI

View full text Add to dashboard Cite

The outbreak of COVID-19 in Beijing has been sporadic since the beginning of 2022 and has become increasingly severe since October. In China’s policy of insisting on dynamic clearance, fine-grained management has become the focus of current epidemic prevention and control. In this paper, we conduct a refined COVID-19 risk prediction and identification of its influencing factors in Beijing based on neighborhood-scale spatial statistical units. We obtained geographic coordinate data of COVID-19 cases in Beijing and quantified them into risk indices of each statistical unit. Additionally, spatial autocorrelation was used to analyze the epidemic risk clustering characteristics. With the multi-source data, 20 influencing elements were constructed, and their spatial heterogeneity was explored by screening 8 for Multiscale Geographically weighted regression (MGWR) model analysis. Finally, a neural network classification model was used to predict the risk of COVID-19 within the sixth ring of Beijing. The MGWR model and the neural network classification model showed good performance: the R2 of the MGWR model was 0.770, and the accuracy of the neural network classification model was 0.852. The results of this study show that: (1) COVID-19 risk is uneven, with the highest clustering within the Fifth Ring Road of Beijing; (2) The results of the MGWR model show that population structure, population density, road density, residential area density, and living service facility density have significant spatial heterogeneity on COVID-19 risk; and (3) The prediction results show a high COVID-19 risk, with the most severe risk being in the eastern, southeastern and southern regions. It should be noted that the prediction results are highly consistent with the current epidemic situation in Shijingshan District, Beijing, and can provide a strong reference for fine-grained epidemic prevention and control in Beijing.

show abstract

“…Understanding the relationship between violent crime and land use allows stakeholders to make informed decisions about land use, urban planning, and community development projects that can help reduce crime and improve overall community safety and quality of life [5][6] [7] and [8]. This allows for a more targeted approach to crime prevention and intervention efforts [9][10] [11] [12][13] [14][15] [16] and [17].…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Movement Distribution Pattern of Violent Crime in Malaysia's Capital Region-Selangor, Kuala Lumpur, and Putrajaya

2024

IJG

View full text Add to dashboard Cite

Previous studies of violent crime have been focused on identifying hot spots areas, ignoring crime movement and distribution, particularly violent-related land use on four different time frames. Studies showed that they have used police station boundaries or sectors to detect crime hotspots without considering land use type and the distribution and movement of crime. Understanding the relationship between violent crime and land use allows stakeholders to make informed decisions about land use, urban planning, and community development projects that can help reduce crime and improve overall community safety and quality of life. The aims of this study to investigate the relationship between violent crimes, time and land use in Selangor State, Kuala Lumpur Federal Territory (KLFT) and Putrajaya Federal Territory (PFT) from 2015 to 2020 using Mean Center and Standard Deviational Ellipses (SDE). These methodologies can assist authorities such as the Royal MalaysiaPolice (RMP) and Local Authority (LA) to identify potential variables leading to the high prevalence of violent crime in specific locations, which can then be used by police and local authorities to collaborate, establish crime prevention initiatives, and more effectively allocate resources. This study considers four-time frames,

show abstract

Exploring the Relationship among Human Activities, COVID-19 Morbidity, and At-Risk Areas Using Location-Based Social Media Data: Knowledge about the Early Pandemic Stage in Wuhan

Cited by 5 publications

References 41 publications

Attributed Network Embedding Model for Exposing COVID-19 Spread Trajectory Archetypes

Attributed Network Embedding Model for Exposing COVID-19 Spread Trajectory Archetypes

Multi-Source Data and Machine Learning-Based Refined Governance for Responding to Public Health Emergencies in Beijing: A Case Study of COVID-19

Analysis of the Movement Distribution Pattern of Violent Crime in Malaysia's Capital Region-Selangor, Kuala Lumpur, and Putrajaya

Contact Info

Product

Resources

About