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

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.

Effects of regional differences and demography in modelling foot-and-mouth disease in cattle at the national scale

“…However, due to limited premises location information, the data were aggregated to the county level, and county-to-county transmission was simulated, tracking which counties contained one or more infectious premises. This enhanced version of the US Disease Outbreak Simulation (USDOS) expands on our previous model by increasing the granularity of simulations to the premises level, using a combination of generated premises locations [14] and a shortcut computational technique [15]. We then infer outcomes at larger geographical scales.…”

Challenges to the Application of Spatially Explicit Stochastic Simulation Models for Foot-and-Mouth Disease Control in Endemic Settings: A Systematic Review

“…The Warwick model has been used to understand predictors of FMD transmission risk [ 53 ], identify high-risk areas [ 54 ], understand spatiotemporal process [ 55 ], evaluate mitigation strategies [ 56 , 57 ], determine optimal control strategies [ 58 , 59 ], guide policymakers [ 60 ], assist in real-time policy-making [ 61 ], understand the effect of vaccine availability constraints on epidemiologic and economic outcomes [ 62 ], estimate prevalence of asymptomatic carriers [ 63 ], understand the effect of livestock density vs. farm density [ 64 ], assess agreement between model outputs and epidemic data [ 65 ], understand the impact of the resolution of spatial data to inform control policies [ 66 ], and determine the predictor of final epidemic size [ 67 ] and computational advancement [ 68 ].…”

A model exploration of carrier and movement transmission as potential explanatory causes for the persistence of foot‐and‐mouth disease in endemic regions

“…For the purpose of computational speed, local spread is done in a grid, using the algorithm outlined in Sellman et al (2018). If infection was adjudged to happen, the susceptible farm has a number of susceptible animals proceed to the exposed/latent stage, drawn from a binomial distribution.…”

“…Mechanistic mathematical modelling can be a useful technique for exploratory analysis of the emergent dynamics of diseases, without the ethical considerations or expense of real‐world animal experiments, and allowing the dynamics of the system to emerge from the known characteristics of the disease in question. Much work has been done modelling FMD in epidemic settings (Björnham et al., 2020; Ferguson et al., 2001; Hayama et al., 2013; Kao, 2002; Keeling, 2001; Schley et al., 2009; Tildesley et al., 2008; Wada et al., 2017); however, relatively few studies have looked at FMD in endemic settings (Kim et al., 2016; McLachlan et al, 2019; Pomeroy et al., 2017; Ringa et al., 2014; Schnell et al., 2019). Of these, only Schnell et al.…”