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Need for speed: An optimized gridding approach for spatially explicit disease simulations
Linköping University, Department of Physics, Chemistry and Biology, Theoretical Biology. Linköping University, Faculty of Science & Engineering.
Colorado State Univ, CO 80523 USA.
Univ Warwick, England; Univ Warwick, England.
Linköping University, Department of Physics, Chemistry and Biology, Theoretical Biology. Linköping University, Faculty of Science & Engineering.
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2018 (English)In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 14, no 4, article id e1006086Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
PUBLIC LIBRARY SCIENCE , 2018. Vol. 14, no 4, article id e1006086
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-148397DOI: 10.1371/journal.pcbi.1006086ISI: 000432169600026PubMedID: 29624574OAI: oai:DiVA.org:liu-148397DiVA, id: diva2:1218572
Note

Funding Agencies|Foreign Animal Disease Modeling Program, Science and Technology Directorate, U.S. Department of Homeland Security [HSHQDC-13-C-B0028]; European research area: animal health and welfare (ANIHWA) [ANR-13-ANWA-0007-03]

Available from: 2018-06-14 Created: 2018-06-14 Last updated: 2018-08-02

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