Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/90640
Title: | Compliance and containment in social distancing : mathematical modeling of COVID-19 across townships | Authors: | Chen, X Zhang, A Wang, H Gallaher, A Zhu, X |
Issue Date: | 2021 | Source: | International journal of geographical information science, 2021, v. 35, no. 3, p. 446-465 | Abstract: | In the early development of COVID-19, large-scale preventive measures, such as border control and air travel restrictions, were implemented to slow international and domestic transmissions. When these measures were in full effect, new cases of infection would be primarily induced by community spread, such as the human interaction within and between neighboring cities and towns, which is generally known as the meso-scale. Existing studies of COVID-19 using mathematical models are unable to accommodate the need for meso-scale modeling, because of the unavailability of COVID-19 data at this scale and the different timings of local intervention policies. In this respect, we propose a meso-scale mathematical model of COVID-19, named the meso-scale Susceptible, Exposed, Infectious, Recovered (MSEIR) model, using town-level infection data in the state of Connecticut. We consider the spatial interaction in terms of the inter-town travel in the model. Based on the developed model, we evaluated how different strengths of social distancing policy enforcement may impact epi curves based on two evaluative metrics: compliance and containment. The developed model and the simulation results help to establish the foundation for community-level assessment and better preparedness for COVID-19. | Keywords: | COVID-19 Epidemic model Mobility Social distancing Spatial interaction |
Publisher: | Taylor & Francis | Journal: | International journal of geographical information science | ISSN: | 1365-8816 | EISSN: | 1362-3087 | DOI: | 10.1080/13658816.2021.1873999 | Rights: | © 2021 Informa UK Limited, trading as Taylor & Francis Group This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Geographical Information Science on 22 Jan 2021 (Published online), available online: http://www.tandfonline.com/10.1080/13658816.2021.1873999. |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
IJGIS-2020-0307.R3.pdf | Pre-Published version | 3.03 MB | Adobe PDF | View/Open |
Page views
18
Last Week
1
1
Last month
Citations as of Jun 4, 2023
Downloads
32
Citations as of Jun 4, 2023
SCOPUSTM
Citations
22
Citations as of Jun 2, 2023
WEB OF SCIENCETM
Citations
18
Citations as of Jun 1, 2023

Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.