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...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2018-04-01
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| Series: | PLoS Computational Biology |
| Online Access: | http://europepmc.org/articles/PMC5906030?pdf=render |
| _version_ | 1819114200265392128 |
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| author | Stefan Sellman Kimberly Tsao Michael J Tildesley Peter Brommesson Colleen T Webb Uno Wennergren Matt J Keeling Tom Lindström |
| author_facet | Stefan Sellman Kimberly Tsao Michael J Tildesley Peter Brommesson Colleen T Webb Uno Wennergren Matt J Keeling Tom Lindström |
| author_sort | Stefan Sellman |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-22T04:41:31Z |
| format | Article |
| id | doaj.art-948688c4249142e0aa29eafa96144a53 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-12-22T04:41:31Z |
| publishDate | 2018-04-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-948688c4249142e0aa29eafa96144a532022-12-21T18:38:44ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582018-04-01144e100608610.1371/journal.pcbi.1006086Need for speed: An optimized gridding approach for spatially explicit disease simulations.Stefan SellmanKimberly TsaoMichael J TildesleyPeter BrommessonColleen T WebbUno WennergrenMatt J KeelingTom LindströmNumerical 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.http://europepmc.org/articles/PMC5906030?pdf=render |
| spellingShingle | Stefan Sellman Kimberly Tsao Michael J Tildesley Peter Brommesson Colleen T Webb Uno Wennergren Matt J Keeling Tom Lindström Need for speed: An optimized gridding approach for spatially explicit disease simulations. PLoS Computational Biology |
| 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 |
| url | http://europepmc.org/articles/PMC5906030?pdf=render |
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