Multi-objective dynamic programming for spatial cluster detection.
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Date
2015
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Abstract
The detection and inference of arbitrarily shaped spatial clusters in aggregated
geographical areas is described here as a multi-objective combinatorial optimization
problem. A multi-objective dynamic programming algorithm, the Geo Dynamic
Scan, is proposed for this formulation, finding a collection of Pareto-optimal solutions.
It takes into account the geographical proximity between areas, thus allowing a
disconnected subset of aggregated areas to be included in the efficient solutions set. It
is shown that the collection of efficient solutions generated by this approach contains
all the solutions maximizing the spatial scan statistic. The plurality of the efficient
solutions set is potentially useful to analyze variations of the most likely cluster and
to investigate covariates. Numerical simulations are conducted to evaluate the algorithm.
A study case with Chagas’ disease clusters in Brazil is presented, with covariate
analysis showing strong correlation of disease occurrence with environmental data.
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Keywords
Arbitrarily shaped spatial cluster, Chagas’ disease, Dynamic programming, Multi-objective optimization, Spatial scan statistic
Citation
MOREIRA, G. J. P. et al. Multi-objective dynamic programming for spatial cluster detection. Environmental and Ecological Statistics, v. 22, n. 2, p. 369-391, jun. 2015. Disponível em: <https://link.springer.com/article/10.1007/s10651-014-0302-7>. Acesso em: 29 mar. 2017.