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Mathematical models for predicting human mobility in the context of infectious disease spread: introducing the impedance model
BACKGROUND: Mathematical models of human mobility have demonstrated a great potential for infectious disease epidemiology in contexts of data scarcity. While the commonly used gravity model involves parameter tuning and is thus difficult to implement without reference data, the more recent radiation...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5700689/ https://www.ncbi.nlm.nih.gov/pubmed/29166908 http://dx.doi.org/10.1186/s12942-017-0115-7 |
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author | Sallah, Kankoé Giorgi, Roch Bengtsson, Linus Lu, Xin Wetter, Erik Adrien, Paul Rebaudet, Stanislas Piarroux, Renaud Gaudart, Jean |
author_facet | Sallah, Kankoé Giorgi, Roch Bengtsson, Linus Lu, Xin Wetter, Erik Adrien, Paul Rebaudet, Stanislas Piarroux, Renaud Gaudart, Jean |
author_sort | Sallah, Kankoé |
collection | PubMed |
description | BACKGROUND: Mathematical models of human mobility have demonstrated a great potential for infectious disease epidemiology in contexts of data scarcity. While the commonly used gravity model involves parameter tuning and is thus difficult to implement without reference data, the more recent radiation model based on population densities is parameter-free, but biased. In this study we introduce the new impedance model, by analogy with electricity. Previous research has compared models on the basis of a few specific available spatial patterns. In this study, we use a systematic simulation-based approach to assess the performances. METHODS: Five hundred spatial patterns were generated using various area sizes and location coordinates. Model performances were evaluated based on these patterns. For simulated data, comparison measures were average root mean square error (aRMSE) and bias criteria. Modeling of the 2010 Haiti cholera epidemic with a basic susceptible–infected–recovered (SIR) framework allowed an empirical evaluation through assessing the goodness-of-fit of the observed epidemic curve. RESULTS: The new, parameter-free impedance model outperformed previous models on simulated data according to average aRMSE and bias criteria. The impedance model achieved better performances with heterogeneous population densities and small destination populations. As a proof of concept, the basic compartmental SIR framework was used to confirm the results obtained with the impedance model in predicting the spread of cholera in Haiti in 2010. CONCLUSIONS: The proposed new impedance model provides accurate estimations of human mobility, especially when the population distribution is highly heterogeneous. This model can therefore help to achieve more accurate predictions of disease spread in the context of an epidemic. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12942-017-0115-7) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5700689 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-57006892017-12-01 Mathematical models for predicting human mobility in the context of infectious disease spread: introducing the impedance model Sallah, Kankoé Giorgi, Roch Bengtsson, Linus Lu, Xin Wetter, Erik Adrien, Paul Rebaudet, Stanislas Piarroux, Renaud Gaudart, Jean Int J Health Geogr Research BACKGROUND: Mathematical models of human mobility have demonstrated a great potential for infectious disease epidemiology in contexts of data scarcity. While the commonly used gravity model involves parameter tuning and is thus difficult to implement without reference data, the more recent radiation model based on population densities is parameter-free, but biased. In this study we introduce the new impedance model, by analogy with electricity. Previous research has compared models on the basis of a few specific available spatial patterns. In this study, we use a systematic simulation-based approach to assess the performances. METHODS: Five hundred spatial patterns were generated using various area sizes and location coordinates. Model performances were evaluated based on these patterns. For simulated data, comparison measures were average root mean square error (aRMSE) and bias criteria. Modeling of the 2010 Haiti cholera epidemic with a basic susceptible–infected–recovered (SIR) framework allowed an empirical evaluation through assessing the goodness-of-fit of the observed epidemic curve. RESULTS: The new, parameter-free impedance model outperformed previous models on simulated data according to average aRMSE and bias criteria. The impedance model achieved better performances with heterogeneous population densities and small destination populations. As a proof of concept, the basic compartmental SIR framework was used to confirm the results obtained with the impedance model in predicting the spread of cholera in Haiti in 2010. CONCLUSIONS: The proposed new impedance model provides accurate estimations of human mobility, especially when the population distribution is highly heterogeneous. This model can therefore help to achieve more accurate predictions of disease spread in the context of an epidemic. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12942-017-0115-7) contains supplementary material, which is available to authorized users. BioMed Central 2017-11-22 /pmc/articles/PMC5700689/ /pubmed/29166908 http://dx.doi.org/10.1186/s12942-017-0115-7 Text en © The Author(s) 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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 Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Sallah, Kankoé Giorgi, Roch Bengtsson, Linus Lu, Xin Wetter, Erik Adrien, Paul Rebaudet, Stanislas Piarroux, Renaud Gaudart, Jean Mathematical models for predicting human mobility in the context of infectious disease spread: introducing the impedance model |
title | Mathematical models for predicting human mobility in the context of infectious disease spread: introducing the impedance model |
title_full | Mathematical models for predicting human mobility in the context of infectious disease spread: introducing the impedance model |
title_fullStr | Mathematical models for predicting human mobility in the context of infectious disease spread: introducing the impedance model |
title_full_unstemmed | Mathematical models for predicting human mobility in the context of infectious disease spread: introducing the impedance model |
title_short | Mathematical models for predicting human mobility in the context of infectious disease spread: introducing the impedance model |
title_sort | mathematical models for predicting human mobility in the context of infectious disease spread: introducing the impedance model |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5700689/ https://www.ncbi.nlm.nih.gov/pubmed/29166908 http://dx.doi.org/10.1186/s12942-017-0115-7 |
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