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Need for speed : an optimized gridding approach for spatially explicit disease simulations
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Sellman, Stefan, Tsao, Kimberly, Tildesley, Michael J., Brommesson, Peter, Webb, Colleen T., Wennergren, Uno, Keeling, Matthew James and Lindström, Tom (2018) Need for speed : an optimized gridding approach for spatially explicit disease simulations. PLOS Computational Biology, 14 (4). e1006086. doi:10.1371/journal.pcbi.1006086 ISSN 1553-7358.
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Official URL: http://doi.org/10.1371/journal.pcbi.1006086
Abstract
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.
| Item Type: | Journal Article | |||||||||
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| Subjects: | S Agriculture > SF Animal culture | |||||||||
| Divisions: | Faculty of Science, Engineering and Medicine > Research Centres > Centre for Complexity Science Faculty of Science, Engineering and Medicine > Science > Mathematics |
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| SWORD Depositor: | Library Publications Router | |||||||||
| Library of Congress Subject Headings (LCSH): | Communicable diseases -- Mathematical models, Livestock -- Diseases -- Mathematical models, Foot-and-mouth disease -- Transmission -- Mathematical models | |||||||||
| Journal or Publication Title: | PLOS Computational Biology | |||||||||
| Publisher: | Public Library of Science (PLoS) | |||||||||
| ISSN: | 1553-7358 | |||||||||
| Official Date: | 6 April 2018 | |||||||||
| Dates: |
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| Volume: | 14 | |||||||||
| Number: | 4 | |||||||||
| Article Number: | e1006086 | |||||||||
| DOI: | 10.1371/journal.pcbi.1006086 | |||||||||
| Status: | Peer Reviewed | |||||||||
| Publication Status: | Published | |||||||||
| Reuse Statement (publisher, data, author rights): | ** From Crossref via Jisc Publications Router. ** Licence for AM version of this article starting on 06-04-2018: http://creativecommons.org/licenses/by/4.0/ | |||||||||
| Access rights to Published version: | Open Access (Creative Commons) | |||||||||
| RIOXX Funder/Project Grant: |
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