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Need for speed: An optimized gridding approach for spatially explicit disease simulations

Numerical models for simulating outbreaks of infectious diseases are powerful tools for informing surveillance and control strategy decisions. However, large-scale spatially explicit models can be limited by the amount of computational resources they require, which poses a problem when multiple scen...

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Autores principales: Sellman, Stefan, Tsao, Kimberly, Tildesley, Michael J., Brommesson, Peter, Webb, Colleen T., Wennergren, Uno, Keeling, Matt J., Lindström, Tom
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5906030/
https://www.ncbi.nlm.nih.gov/pubmed/29624574
http://dx.doi.org/10.1371/journal.pcbi.1006086
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author Sellman, Stefan
Tsao, Kimberly
Tildesley, Michael J.
Brommesson, Peter
Webb, Colleen T.
Wennergren, Uno
Keeling, Matt J.
Lindström, Tom
author_facet Sellman, Stefan
Tsao, Kimberly
Tildesley, Michael J.
Brommesson, Peter
Webb, Colleen T.
Wennergren, Uno
Keeling, Matt J.
Lindström, Tom
author_sort Sellman, Stefan
collection PubMed
description Numerical models for simulating outbreaks of infectious diseases are powerful tools for informing surveillance and control strategy decisions. However, large-scale spatially explicit models can be limited by the amount of computational resources they require, which poses a problem when multiple scenarios need to be explored to provide policy recommendations. We introduce an easily implemented method that can reduce computation time in a standard Susceptible-Exposed-Infectious-Removed (SEIR) model without introducing any further approximations or truncations. It is based on a hierarchical infection process that operates on entire groups of spatially related nodes (cells in a grid) in order to efficiently filter out large volumes of susceptible nodes that would otherwise have required expensive calculations. After the filtering of the cells, only a subset of the nodes that were originally at risk are then evaluated for actual infection. The increase in efficiency is sensitive to the exact configuration of the grid, and we describe a simple method to find an estimate of the optimal configuration of a given landscape as well as a method to partition the landscape into a grid configuration. To investigate its efficiency, we compare the introduced methods to other algorithms and evaluate computation time, focusing on simulated outbreaks of foot-and-mouth disease (FMD) on the farm population of the USA, the UK and Sweden, as well as on three randomly generated populations with varying degree of clustering. The introduced method provided up to 500 times faster calculations than pairwise computation, and consistently performed as well or better than other available methods. This enables large scale, spatially explicit simulations such as for the entire continental USA without sacrificing realism or predictive power.
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spelling pubmed-59060302018-05-04 Need for speed: An optimized gridding approach for spatially explicit disease simulations Sellman, Stefan Tsao, Kimberly Tildesley, Michael J. Brommesson, Peter Webb, Colleen T. Wennergren, Uno Keeling, Matt J. Lindström, Tom PLoS Comput Biol Research Article Numerical models for simulating outbreaks of infectious diseases are powerful tools for informing surveillance and control strategy decisions. However, large-scale spatially explicit models can be limited by the amount of computational resources they require, which poses a problem when multiple scenarios need to be explored to provide policy recommendations. We introduce an easily implemented method that can reduce computation time in a standard Susceptible-Exposed-Infectious-Removed (SEIR) model without introducing any further approximations or truncations. It is based on a hierarchical infection process that operates on entire groups of spatially related nodes (cells in a grid) in order to efficiently filter out large volumes of susceptible nodes that would otherwise have required expensive calculations. After the filtering of the cells, only a subset of the nodes that were originally at risk are then evaluated for actual infection. The increase in efficiency is sensitive to the exact configuration of the grid, and we describe a simple method to find an estimate of the optimal configuration of a given landscape as well as a method to partition the landscape into a grid configuration. To investigate its efficiency, we compare the introduced methods to other algorithms and evaluate computation time, focusing on simulated outbreaks of foot-and-mouth disease (FMD) on the farm population of the USA, the UK and Sweden, as well as on three randomly generated populations with varying degree of clustering. The introduced method provided up to 500 times faster calculations than pairwise computation, and consistently performed as well or better than other available methods. This enables large scale, spatially explicit simulations such as for the entire continental USA without sacrificing realism or predictive power. Public Library of Science 2018-04-06 /pmc/articles/PMC5906030/ /pubmed/29624574 http://dx.doi.org/10.1371/journal.pcbi.1006086 Text en © 2018 Sellman et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Sellman, Stefan
Tsao, Kimberly
Tildesley, Michael J.
Brommesson, Peter
Webb, Colleen T.
Wennergren, Uno
Keeling, Matt J.
Lindström, Tom
Need for speed: An optimized gridding approach for spatially explicit disease simulations
title Need for speed: An optimized gridding approach for spatially explicit disease simulations
title_full Need for speed: An optimized gridding approach for spatially explicit disease simulations
title_fullStr Need for speed: An optimized gridding approach for spatially explicit disease simulations
title_full_unstemmed Need for speed: An optimized gridding approach for spatially explicit disease simulations
title_short Need for speed: An optimized gridding approach for spatially explicit disease simulations
title_sort need for speed: an optimized gridding approach for spatially explicit disease simulations
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5906030/
https://www.ncbi.nlm.nih.gov/pubmed/29624574
http://dx.doi.org/10.1371/journal.pcbi.1006086
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