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A Synthetic Population for Modelling the Dynamics of Infectious Disease Transmission in American Samoa
Agent-based modelling is a useful approach for capturing heterogeneity in disease transmission. In this study, a synthetic population was developed for American Samoa using an iterative approach based on population census, questionnaire survey and land use data. The population will be used as the ba...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5711879/ https://www.ncbi.nlm.nih.gov/pubmed/29196679 http://dx.doi.org/10.1038/s41598-017-17093-8 |
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author | Xu, Zhijing Glass, Kathryn Lau, Colleen L. Geard, Nicholas Graves, Patricia Clements, Archie |
author_facet | Xu, Zhijing Glass, Kathryn Lau, Colleen L. Geard, Nicholas Graves, Patricia Clements, Archie |
author_sort | Xu, Zhijing |
collection | PubMed |
description | Agent-based modelling is a useful approach for capturing heterogeneity in disease transmission. In this study, a synthetic population was developed for American Samoa using an iterative approach based on population census, questionnaire survey and land use data. The population will be used as the basis for a new agent-based model, intended specifically to fill the knowledge gaps about lymphatic filariasis transmission and elimination, but also to be readily adaptable to model other infectious diseases. The synthetic population was characterized by the statistically realistic population and household structure, and high-resolution geographic locations of households. The population was simulated over 40 years from 2010 to 2050. The simulated population was compared to estimates and projections of the U.S. Census Bureau. The results showed the total population would continuously decrease due to the observed large number of emigrants. Population ageing was observed, which was consistent with the latest two population censuses and the Bureau’s projections. The sex ratios by age groups were analysed and indicated an increase in the proportion of males in age groups 0–14 and 15–64. The household size followed a Gaussian distribution with an average size of around 5.0 throughout the simulation, slightly less than the initial average size 5.6. |
format | Online Article Text |
id | pubmed-5711879 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-57118792017-12-06 A Synthetic Population for Modelling the Dynamics of Infectious Disease Transmission in American Samoa Xu, Zhijing Glass, Kathryn Lau, Colleen L. Geard, Nicholas Graves, Patricia Clements, Archie Sci Rep Article Agent-based modelling is a useful approach for capturing heterogeneity in disease transmission. In this study, a synthetic population was developed for American Samoa using an iterative approach based on population census, questionnaire survey and land use data. The population will be used as the basis for a new agent-based model, intended specifically to fill the knowledge gaps about lymphatic filariasis transmission and elimination, but also to be readily adaptable to model other infectious diseases. The synthetic population was characterized by the statistically realistic population and household structure, and high-resolution geographic locations of households. The population was simulated over 40 years from 2010 to 2050. The simulated population was compared to estimates and projections of the U.S. Census Bureau. The results showed the total population would continuously decrease due to the observed large number of emigrants. Population ageing was observed, which was consistent with the latest two population censuses and the Bureau’s projections. The sex ratios by age groups were analysed and indicated an increase in the proportion of males in age groups 0–14 and 15–64. The household size followed a Gaussian distribution with an average size of around 5.0 throughout the simulation, slightly less than the initial average size 5.6. Nature Publishing Group UK 2017-12-01 /pmc/articles/PMC5711879/ /pubmed/29196679 http://dx.doi.org/10.1038/s41598-017-17093-8 Text en © The Author(s) 2017 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Xu, Zhijing Glass, Kathryn Lau, Colleen L. Geard, Nicholas Graves, Patricia Clements, Archie A Synthetic Population for Modelling the Dynamics of Infectious Disease Transmission in American Samoa |
title | A Synthetic Population for Modelling the Dynamics of Infectious Disease Transmission in American Samoa |
title_full | A Synthetic Population for Modelling the Dynamics of Infectious Disease Transmission in American Samoa |
title_fullStr | A Synthetic Population for Modelling the Dynamics of Infectious Disease Transmission in American Samoa |
title_full_unstemmed | A Synthetic Population for Modelling the Dynamics of Infectious Disease Transmission in American Samoa |
title_short | A Synthetic Population for Modelling the Dynamics of Infectious Disease Transmission in American Samoa |
title_sort | synthetic population for modelling the dynamics of infectious disease transmission in american samoa |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5711879/ https://www.ncbi.nlm.nih.gov/pubmed/29196679 http://dx.doi.org/10.1038/s41598-017-17093-8 |
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