NDLI: Risk Estimation in Spatial Disease Clusters: An RBF Network Approach

Content Provider	IEEE Xplore Digital Library
Author	Takahashi, F.C. Takahashi, R.H.C.
Copyright Year	2012
Abstract	This paper proposes a method which is suitable for the estimation of the probability of occurrence of a syndrome, as a function of the geographical coordinates of the individuals under risk. The data describing the location of syndrome cases over the population suffers a moving-average filtering, and the resulting values are fitted by an RBF network performing a regression. Some contour curves of the RBF network are then employed in order to establish the boundaries between four kinds of regions: regions of high-incidence, regions of medium incidence, regions of slightly-abnormal incidence, and regions of normal prevalence. In each region, the risk is estimated with three indicators: a nominal risk, an upper bound risk and a lower bound risk. Those indicators are obtained by adjusting the probability employed for the Monte Carlo simulation of syndrome scenarios over the population. The nominal risk is the probability which produces Monte Carlo simulations for which the empirical number of syndrome cases corresponds to the median. The upper bound and the lower bound risks are the probabilities which produce Monte Carlo simulations for which the empirical values of syndrome cases correspond respectively to the 25% percentile and the 75% percentile. The proposed method constitutes an advance in relation to the currently known techniques of spatial cluster detection, which are dedicated to finding clusters of abnormal occurrence of a syndrome, without quantifying the probability associated to such an abnormality, and without performing a stratification of different sub-regions with different associated risks. The proposed method was applied on data which were studied formerly in a paper that was intended to find a cluster of dengue fever. The result determined here is compatible with the cluster that was found in that reference.
Starting Page	133
Ending Page	138
File Size	355786
Page Count	6
File Format	PDF
ISBN	9781467346511
DOI	10.1109/ICMLA.2012.233
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-12-12
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Monte Carlo methods Upper bound disease cluster hypothesis testing Sociology Estimation Radial basis function networks neural networks uncertainty interval Diseases
Content Type	Text
Resource Type	Article

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