Estimating the optimal population upper bound for scan methods in retrospective disease surveillance

Meysami, Mohammad; French, Joshua P.; Lipner, Ettie M.

doi:10.1002/bimj.202000273

Cited by 3 publications

(1 citation statement)

References 28 publications

(78 reference statements)

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“…We successively add the nearest regions to the starting region until some percentage of the total population is reached to create a sequence of candidate zones. This percentage of the total population can be set by the user (the default value is 50%) or can be estimated using the Gini [21] or elbow method [22]. We then do the same process for all centroids in the study area to construct Z c .…”

Section: The Circular Scan Methodsmentioning

confidence: 99%

Flexible-Elliptical Spatial Scan Method

Meysami,

French,

Lipner

2023

Mathematics

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The detection of disease clusters in spatial data analysis plays a crucial role in public health, while the circular scan method is widely utilized for this purpose, accurately identifying non-circular (irregular) clusters remains challenging and reduces detection accuracy. To overcome this limitation, various extensions have been proposed to effectively detect arbitrarily shaped clusters. In this paper, we combine the strengths of two well-known methods, the flexible and elliptic scan methods, which are specifically designed for detecting irregularly shaped clusters. We leverage the unique characteristics of these methods to create candidate zones capable of accurately detecting irregularly shaped clusters, along with a modified likelihood ratio test statistic. By inheriting the advantages of the flexible and elliptic methods, our proposed approach represents a practical addition to the existing repertoire of spatial data analysis techniques.

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Section: The Circular Scan Methodsmentioning

confidence: 99%

Flexible-Elliptical Spatial Scan Method

Meysami,

French,

Lipner

2023

Mathematics

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Prefiltered component‐based greedy (PreCoG) scan method

French,

Meysami,

Lipner

2024

Statistics in Medicine

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The spatial distribution of disease cases can provide important insights into disease spread and its potential risk factors. Identifying disease clusters correctly can help us discover new risk factors and inform interventions to control and prevent the spread of disease as quickly as possible. In this study, we propose a novel scan method, the Prefiltered Component‐based Greedy (PreCoG) scan method, which efficiently and accurately detects irregularly shaped clusters using a prefiltered component‐based algorithm. The PreCoG scan method's flexibility allows it to perform well in detecting both regularly and irregularly‐shaped clusters. Additionally, it is fast to apply while providing high power, sensitivity, and positive predictive value for the detected clusters compared to other scan methods. To confirm the effectiveness of the PreCoG method, we compare its performance to many other scan methods. Additionally, we have implemented this method in the smerc R package to make it publicly available to other researchers. Our proposed PreCoG scan method presents a unique and innovative process for detecting disease clusters and can improve the accuracy of disease surveillance systems.

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Optimizing the maximum reported cluster size for the multinomial-based spatial scan statistic

Moon,

Kim,

Jung

2023

Int J Health Geogr

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Background Correctly identifying spatial disease cluster is a fundamental concern in public health and epidemiology. The spatial scan statistic is widely used for detecting spatial disease clusters in spatial epidemiology and disease surveillance. Many studies default to a maximum reported cluster size (MRCS) set at 50% of the total population when searching for spatial clusters. However, this default setting can sometimes report clusters larger than true clusters, which include less relevant regions. For the Poisson, Bernoulli, ordinal, normal, and exponential models, a Gini coefficient has been developed to optimize the MRCS. Yet, no measure is available for the multinomial model. Results We propose two versions of a spatial cluster information criterion (SCIC) for selecting the optimal MRCS value for the multinomial-based spatial scan statistic. Our simulation study suggests that SCIC improves the accuracy of reporting true clusters. Analysis of the Korea Community Health Survey (KCHS) data further demonstrates that our method identifies more meaningful small clusters compared to the default setting. Conclusions Our method focuses on improving the performance of the spatial scan statistic by optimizing the MRCS value when using the multinomial model. In public health and disease surveillance, the proposed method can be used to provide more accurate and meaningful spatial cluster detection for multinomial data, such as disease subtypes.

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Estimating the optimal population upper bound for scan methods in retrospective disease surveillance

Abstract: This article has earned an open data badge "Reproducible Research" for making publicly available the code necessary to reproduce the reported results. The results reported in this article could fully be reproduced.

Cited by 3 publications

References 28 publications

Flexible-Elliptical Spatial Scan Method

Flexible-Elliptical Spatial Scan Method

Prefiltered component‐based greedy (PreCoG) scan method

Optimizing the maximum reported cluster size for the multinomial-based spatial scan statistic

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