Incorporation of Spatial Interactions in Location Networks to Identify Critical Geo-Referenced Routes for Assessing Disease Control Measures on a Large-Scale Campus
Bibliographic Data
| ID | 15488663 |
|---|---|
| Authors | Tzai-Hung Wen (0000-0002-3526-0203, National Taiwan University), Tzai‐hung Wen (0000-0002-9151-8336, National Taiwan University, corresponding author), Wei Chien Benny Chin (0000-0001-7215-3303, National Taiwan University), Wei Chin (National Taiwan University) |
| Year | 2015 |
| Volume | 12 |
| Issue | 4 |
| Pages | 4170-4184 |
| Publication date | 2015-04-14 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Environmental Research and Public Health (JOURNAL) |
| Journal identifiers | ISSN: 1661-7827 • E-ISSN: 1660-4601 |
| Publisher | Multidisciplinary Digital Publishing Institute (PUBLISHER • CH) |
| DOI | 10.3390/ijerph120404170 |
| PMID | 25874686 |
| PMCID | PMC4410240 |
| OpenAlex | W2132914037 |
| Language | EN |
| References cited | 30 |
Respiratory diseases mainly spread through interpersonal contact. Class suspension is the most direct strategy to prevent the spread of disease through elementary or secondary schools by blocking the contact network. However, as university students usually attend courses in different buildings, the daily contact patterns on a university campus are complicated, and once disease clusters have occurred, suspending classes is far from an efficient strategy to control disease spread. The purpose of this study is to propose a methodological framework for generating campus location networks from a routine administration database, analyzing the community structure of the network, and identifying the critical links and nodes for blocking respiratory disease transmission. The data comes from the student enrollment records of a major comprehensive university in Taiwan. We combined the social network analysis and spatial interaction model to establish a geo-referenced community structure among the classroom buildings. We also identified the critical links among the communities that were acting as contact bridges and explored the changes in the location network after the sequential removal of the high-risk buildings. Instead of conducting a questionnaire survey, the study established a standard procedure for constructing a location network on a large-scale campus from a routine curriculum database. We also present how a location network structure at a campus could function to target the high-risk buildings as the bridges connecting communities for blocking disease transmission
Blocking (statistics · Cartography · Class (philosophy · Community structure · Computer network · Contact tracing · Coronavirus disease 2019 (COVID-19 · Disease · Geography · Pathology · Scale (ratio · Social media · Social network (sociolinguistics · Telecommunications · Transmission (telecommunications · World Wide Web · Complex Network Analysis Techniques · Computer Science · COVID-19 epidemiological studies · Data-Driven Disease Surveillance · Medicine · Artificial Intelligence · Ecology
Discovering Spatial Interaction Communities from Mobile Phone D ata
Uncovering the overlapping community structure of complex networks in nature and society
Epidemic Spreading in Scale-Free Networks
The worldwide air transportation network
Collective dynamics of ‘small-world’ networks
Community structure in social and biological networks
Centrality in social networks conceptual clarification
The uses of spatial analysis in medical geography
Spatializing Social Networks
Complex Contagions and the Weakness of Long Ties
The Strength of Weak Ties
| Highly cited | No |
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