An Empirical Spatial Network Model Based on Human Mobility for Epidemiological Research
A Case Study
Dados Bibliográficos
| ID | 3775703 |
|---|---|
| Autores | Chen Xu (0000-0002-5225-5350, University of Wyoming), Libao Jin (University of Wyoming), Long Lee (0000-0002-1137-5866, University of Wyoming) |
| Ano | 2023 |
| Volume | 113 |
| Fascículo | 6 |
| Páginas | 1461-1482 |
| Data de publicação | 2023-07-03 |
| Peer Reviewed | Sim |
| Open Access | Não |
| Tipo | ARTICLE |
| Periódico | Annals of the American Association of Geographers (JOURNAL) |
| Identificadores do periódico | ISSN: 2469-4452 • E-ISSN: 2469-4460 |
| Editora | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/24694452.2023.2187339 |
| OpenAlex | W4366723330 |
| Idioma | EN |
| Citações recebidas | 5 |
| Referências citadas | 63 |
Constructing a data-driven spatial contact network model is challenging in epidemiological research. In this study, we examine the applicability of geotagged Twitter data as an instrumental data source for tackling such a challenge. Geotagged Twitter data carrying geolocations of the account users have the strength for longitudinal data collection at a massive scale. Still, the unstructured nature of the data exerts significant methodological and computational difficulties. We focus on methodological solutions and develop a novelty approach that lets a spatial contact network emerge naturally from the massive amount of geospatial tweets. We show that such a data-driven network has reflected the assumptions made by network models regarding human behaviors and has the potential of being used for epidemiological research. To this end, we investigate the network properties and study the spread of pathogens on the proposed spatial contact network by using the homogeneous and heterogeneous susceptible–infectious–recovered (SIR) network models and the event-driven Gillespie’s algorithm. Our simulation results strongly suggest that it is feasible to explicitly construct data-driven spatial models using massive longitudinal Twitter data for public health research
Big data · Cartography · Data mining · Data modeling · Data science · Database · Geography · Geospatial analysis · Novelty · Spatial analysis · Spatial epidemiology · Spatial network · Complex Network Analysis Techniques · Computer Science · Data-Driven Disease Surveillance · Human Mobility and Location-Based Analysis
Spatial modelling to identify high-risk zones for the transmission of cutaneous leishmaniasis in hyperendemic urban environments
A multi-activity view of intra-urban travel networks
Urban Mobility and Knowledge Extraction from Chaotic Time Series Data
The Impact of Urban Scaling Structure on the Local-Scale Transmission of Covid-19
The Networked Community of Urban Mobility during the Pandemic
Epidemic processes in complex networks
Measuring segregation
Understanding individual human mobility patterns
Modeling social networks from sampled data
Scale-free networks are rare
Complex networks
Limits of Predictability in Human Mobility
Human mobility and socioeconomic status
Spread of epidemic disease on networks
Mobility network models of Covid-19 explain inequities and inform reopening
Modelling disease outbreaks in realistic urban social networks
Epidemic Spreading in Scale-Free Networks
Networks and epidemic models
Identification of influential spreaders in complex networks
Power-Law Distributions in Empirical Data
Collective dynamics of ‘small-world’ networks
The Structure and Function of Complex Networks
A Vector Field Approach to Estimating Environmental Exposure Using Human Activity Data
Ethics of instantaneous contact tracing using mobile phone apps in the control of the Covid-19 pandemic
Activity space estimation with longitudinal observations of social media data
Understanding demographic and socioeconomic biases of geotagged Twitter users at the county level
The Standard Deviational Ellipse; An Updated Tool for Spatial Description
Urban rhythms and travel behaviour
Social Sensing
Modeling Individual Vulnerability to Communicable Diseases
Spatial Behavior
Uncertainties in the Assessment of Covid-19 Risk
A Location-Centric Network Approach to Analyzing Epidemic Dynamics
| Obras citantes distintas | 5 |
|---|---|
| Citações por ano | 1,67 |
| Intervalo de citações | 2023 - 2024 (2) |
| Velocidade de citação | recent |
| Altamente citado | Não |
| Tipos de citação | Neutras: 3 |