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Detecting a trend change in cross-border epidemic transmission
A method for a system of Langevin equations is developed for detecting a trend change in cross-border epidemic transmission. The equations represent a standard epidemiological SIR compartment model and a meta-population network model. The method analyzes a time series of the number of new cases repo...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier B.V.
2016
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7126868/ https://www.ncbi.nlm.nih.gov/pubmed/32288099 http://dx.doi.org/10.1016/j.physa.2016.03.039 |
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author | Maeno, Yoshiharu |
author_facet | Maeno, Yoshiharu |
author_sort | Maeno, Yoshiharu |
collection | PubMed |
description | A method for a system of Langevin equations is developed for detecting a trend change in cross-border epidemic transmission. The equations represent a standard epidemiological SIR compartment model and a meta-population network model. The method analyzes a time series of the number of new cases reported in multiple geographical regions. The method is applicable to investigating the efficacy of the implemented public health intervention in managing infectious travelers across borders. It is found that the change point of the probability of travel movements was one week after the WHO worldwide alert on the SARS outbreak in 2003. The alert was effective in managing infectious travelers. On the other hand, it is found that the probability of travel movements did not change at all for the flu pandemic in 2009. The pandemic did not affect potential travelers despite the WHO alert. |
format | Online Article Text |
id | pubmed-7126868 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Elsevier B.V. |
record_format | MEDLINE/PubMed |
spelling | pubmed-71268682020-04-08 Detecting a trend change in cross-border epidemic transmission Maeno, Yoshiharu Physica A Article A method for a system of Langevin equations is developed for detecting a trend change in cross-border epidemic transmission. The equations represent a standard epidemiological SIR compartment model and a meta-population network model. The method analyzes a time series of the number of new cases reported in multiple geographical regions. The method is applicable to investigating the efficacy of the implemented public health intervention in managing infectious travelers across borders. It is found that the change point of the probability of travel movements was one week after the WHO worldwide alert on the SARS outbreak in 2003. The alert was effective in managing infectious travelers. On the other hand, it is found that the probability of travel movements did not change at all for the flu pandemic in 2009. The pandemic did not affect potential travelers despite the WHO alert. Elsevier B.V. 2016-09-01 2016-04-01 /pmc/articles/PMC7126868/ /pubmed/32288099 http://dx.doi.org/10.1016/j.physa.2016.03.039 Text en © 2016 Elsevier B.V. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Maeno, Yoshiharu Detecting a trend change in cross-border epidemic transmission |
title | Detecting a trend change in cross-border epidemic transmission |
title_full | Detecting a trend change in cross-border epidemic transmission |
title_fullStr | Detecting a trend change in cross-border epidemic transmission |
title_full_unstemmed | Detecting a trend change in cross-border epidemic transmission |
title_short | Detecting a trend change in cross-border epidemic transmission |
title_sort | detecting a trend change in cross-border epidemic transmission |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7126868/ https://www.ncbi.nlm.nih.gov/pubmed/32288099 http://dx.doi.org/10.1016/j.physa.2016.03.039 |
work_keys_str_mv | AT maenoyoshiharu detectingatrendchangeincrossborderepidemictransmission |