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Towards Identifying and Predicting Spatial Epidemics on Complex Meta-population Networks
In the past decade, the network science community has witnessed huge advances in the threshold theory, prediction and control of epidemic dynamics on complex networks. While along with the understanding of spatial epidemics on meta-population networks achieved so far, more challenges have opened the...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7122965/ http://dx.doi.org/10.1007/978-981-10-5287-3_6 |
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author | Li, Xiang Wang, Jian-Bo Li, Cong |
author_facet | Li, Xiang Wang, Jian-Bo Li, Cong |
author_sort | Li, Xiang |
collection | PubMed |
description | In the past decade, the network science community has witnessed huge advances in the threshold theory, prediction and control of epidemic dynamics on complex networks. While along with the understanding of spatial epidemics on meta-population networks achieved so far, more challenges have opened the door to identify, retrospect, and predict the epidemic invasion process. This chapter reviews the recent progress towards identifying susceptible-infected compartment parameters and spatial invasion pathways on a meta-population network as well as the minimal case of two-subpopulation version, which may also extend to the prediction of spatial epidemics as well. The artificial and empirical meta-population networks verify the effectiveness of our proposed solutions to the concerned problems. Finally, the whole chapter concludes with the outlook of future research. |
format | Online Article Text |
id | pubmed-7122965 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
record_format | MEDLINE/PubMed |
spelling | pubmed-71229652020-04-06 Towards Identifying and Predicting Spatial Epidemics on Complex Meta-population Networks Li, Xiang Wang, Jian-Bo Li, Cong Temporal Network Epidemiology Article In the past decade, the network science community has witnessed huge advances in the threshold theory, prediction and control of epidemic dynamics on complex networks. While along with the understanding of spatial epidemics on meta-population networks achieved so far, more challenges have opened the door to identify, retrospect, and predict the epidemic invasion process. This chapter reviews the recent progress towards identifying susceptible-infected compartment parameters and spatial invasion pathways on a meta-population network as well as the minimal case of two-subpopulation version, which may also extend to the prediction of spatial epidemics as well. The artificial and empirical meta-population networks verify the effectiveness of our proposed solutions to the concerned problems. Finally, the whole chapter concludes with the outlook of future research. 2017-10-05 /pmc/articles/PMC7122965/ http://dx.doi.org/10.1007/978-981-10-5287-3_6 Text en © Springer Nature Singapore Pte Ltd. 2017 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Article Li, Xiang Wang, Jian-Bo Li, Cong Towards Identifying and Predicting Spatial Epidemics on Complex Meta-population Networks |
title | Towards Identifying and Predicting Spatial Epidemics on Complex Meta-population Networks |
title_full | Towards Identifying and Predicting Spatial Epidemics on Complex Meta-population Networks |
title_fullStr | Towards Identifying and Predicting Spatial Epidemics on Complex Meta-population Networks |
title_full_unstemmed | Towards Identifying and Predicting Spatial Epidemics on Complex Meta-population Networks |
title_short | Towards Identifying and Predicting Spatial Epidemics on Complex Meta-population Networks |
title_sort | towards identifying and predicting spatial epidemics on complex meta-population networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7122965/ http://dx.doi.org/10.1007/978-981-10-5287-3_6 |
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