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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 thesmercR 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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Estimating the optimal population upper bound for scan methods in retrospective disease surveillance
Abstract Correctly and quickly identifying disease patterns and clusters is a vital aspect of public health and epidemiology so that disease outbreaks can be mitigated as effectively as possible. The circular scan method is one of the most commonly used methods for detecting disease outbreaks and clusters in retrospective and prospective disease surveillance. The circular scan method requires a population upper bound in order to construct the set of candidate zones to be scanned, which is usually set to 50% of the total population. The performance of the circular scan method is affected by the choice of the population upper bound, and choosing an upper bound different from the default value can improve the method's performance. Recently, the Gini coefficient based on the Lorenz curve, which was originally used in economics, was proposed to determine a better population upper bound. We present the elbow method, a new method for choosing the population upper bound, which seeks to address some of the limitations of the Gini‐based method while improving the performance of the circular scan method over the default value. To evaluate the performance of the proposed approach, we evaluate the sensitivity and positive predictive value of the circular scan method for publicly‐available benchmark data for the default value, the Gini coefficient method, and the elbow method.
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- Award ID(s):
- 1915277
- PAR ID:
- 10446475
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Biometrical Journal
- Volume:
- 63
- Issue:
- 8
- ISSN:
- 0323-3847
- Page Range / eLocation ID:
- p. 1633-1651
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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