Impact of spatial clustering on disease transmission and optimal control
| Autor: | Justine Allpress, Matthew James Keeling, Thomas House, Michael J. Tildesley, Gary Smith, Maggie O'Neil, Ross J. Curry, Mark Bruhn |
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| Rok vydání: | 2009 |
| Předmět: |
0106 biological sciences
Structure (mathematical logic) 0303 health sciences Multidisciplinary Control (management) Aggregate (data warehouse) Parameterized complexity Resolution (logic) Biology Optimal control 010603 evolutionary biology 01 natural sciences 03 medical and health sciences Range (mathematics) Commentaries Communicable Disease Control Disease Transmission Infectious Econometrics Cluster Analysis SF QA Spatial analysis 030304 developmental biology |
| Zdroj: | Proceedings of the National Academy of Sciences |
| ISSN: | 1091-6490 0027-8424 |
| DOI: | 10.1073/pnas.0909047107 |
| Popis: | Spatial heterogeneities and spatial separation of hosts are often seen as key factors when developing accurate predictive models of the spread of pathogens. The question we address in this paper is how coarse the resolution of the spatial data can be for a model to be a useful tool for informing control policies. We examine this problem using the specific case of foot-and-mouth disease spreading between farms using the formulation developed during the 2001 epidemic in the United Kingdom. We show that, if our model is carefully parameterized to match epidemic behavior, then using aggregate county-scale data from the United States is sufficient to closely determine optimal control measures (specifically ring culling). This result also holds when the approach is extended to theoretical distributions of farms where the spatial clustering can be manipulated to extremes. We have therefore shown that, although spatial structure can be critically important in allowing us to predict the emergent population-scale behavior from a knowledge of the individual-level dynamics, for this specific applied question, such structure is mostly subsumed in the parameterization allowing us to make policy predictions in the absence of high-quality spatial information. We believe that this approach will be of considerable benefit across a range of disciplines where data are only available at intermediate spatial scales. |
| Databáze: | OpenAIRE |
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