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

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Listed:
  • Stefan Sellman
  • Kimberly Tsao
  • Michael J Tildesley
  • Peter Brommesson
  • Colleen T Webb
  • Uno Wennergren
  • Matt J Keeling
  • Tom Lindström

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.Author summary: Numerical models for simulating the outbreak of infectious disease are powerful tools that can be used to inform policy decisions by simulating outbreaks and control actions. However, they rely on considerable computational power to explore all outcomes and scenarios of interest. Focusing on model types commonly used for livestock diseases, we here introduce novel algorithms for efficient computation, alongside techniques to optimize them based on simplifying assumptions. Through simulations of FMD outbreak in the US, the UK and Sweden, as well as in computer generated landscapes, we test how these methods perform under realistic conditions. We find that our optimization techniques works well, and when the introduced algorithms are implemented with these optimizations, computation time can be reduced by more than two orders of magnitude compared to pairwise calculations. We propose that the considered algorithms—which are straight forward to implement—will be useful for simulation of a wide range of diseases, and will promote the use of simulation models for policy recommendation.

Suggested Citation

  • Stefan Sellman & Kimberly Tsao & Michael J Tildesley & Peter Brommesson & Colleen T Webb & Uno Wennergren & Matt J Keeling & Tom Lindström, 2018. "Need for speed: An optimized gridding approach for spatially explicit disease simulations," PLOS Computational Biology, Public Library of Science, vol. 14(4), pages 1-27, April.
  • Handle: RePEc:plo:pcbi00:1006086
    DOI: 10.1371/journal.pcbi.1006086
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    References listed on IDEAS

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    1. Christopher L Burdett & Brian R Kraus & Sarah J Garza & Ryan S Miller & Kathe E Bjork, 2015. "Simulating the Distribution of Individual Livestock Farms and Their Populations in the United States: An Example Using Domestic Swine (Sus scrofa domesticus) Farms," PLOS ONE, Public Library of Science, vol. 10(11), pages 1-21, November.
    2. Tom Lindström & Daniel A Grear & Michael Buhnerkempe & Colleen T Webb & Ryan S Miller & Katie Portacci & Uno Wennergren, 2013. "A Bayesian Approach for Modeling Cattle Movements in the United States: Scaling up a Partially Observed Network," PLOS ONE, Public Library of Science, vol. 8(1), pages 1-11, January.
    3. Carol Y. Lin, 2008. "Modeling Infectious Diseases in Humans and Animals by KEELING, M. J. and ROHANI, P," Biometrics, The International Biometric Society, vol. 64(3), pages 993-993, September.
    4. M. J. Keeling & M. E. J. Woolhouse & R. M. May & G. Davies & B. T. Grenfell, 2003. "Modelling vaccination strategies against foot-and-mouth disease," Nature, Nature, vol. 421(6919), pages 136-142, January.
    5. Michael J. Tildesley & Nicholas J. Savill & Darren J. Shaw & Rob Deardon & Stephen P. Brooks & Mark E. J. Woolhouse & Bryan T. Grenfell & Matt J. Keeling, 2006. "Optimal reactive vaccination strategies for a foot-and-mouth outbreak in the UK," Nature, Nature, vol. 440(7080), pages 83-86, March.
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