Cover Image

An eigenspace method for detecting space-time disease clusters with unknown population-data

Space-time disease cluster detection assists in conducting disease surveillance and implementing control strategies. The state-of-the-art method for this kind of problem is the Space-time Scan Statistics (SaTScan) which has limitations for non-traditional/non-clinical data sources due to its paramet...

Full description

Saved in:
Bibliographic Details
Main Authors: Ullah, S., Nor, N.H.M., Daud, H., Zainuddin, N., Hadi Fanaee, T., Khalil, A.
Format: Article
Published: Tech Science Press 2021
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114558730&doi=10.32604%2fcmc.2022.019029&partnerID=40&md5=1556495b604ef99633600d64485dd333
http://eprints.utp.edu.my/29434/
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.utp.eprints.29434
record_format eprints
spelling my.utp.eprints.294342022-03-25T02:06:42Z An eigenspace method for detecting space-time disease clusters with unknown population-data Ullah, S. Nor, N.H.M. Daud, H. Zainuddin, N. Hadi Fanaee, T. Khalil, A. Space-time disease cluster detection assists in conducting disease surveillance and implementing control strategies. The state-of-the-art method for this kind of problem is the Space-time Scan Statistics (SaTScan) which has limitations for non-traditional/non-clinical data sources due to its parametric model assumptions such as Poisson or Gaussian counts. Addressing this problem, an Eigenspace-based method called Multi-EigenSpot has recently been proposed as a nonparametric solution. However, it is based on the population counts data which are not always available in the least developed countries. In addition, the population counts are difficult to approximate for some surveillance data such as emergency department visits and over-the-counter drug sales, where the catchment area for each hospital/pharmacy is undefined. We extend the population-based Multi-EigenSpot method to approximate the potential disease clusters from the observed/reported disease counts only with no need for the population counts. The proposed adaptation uses an estimator of expected disease count that does not depend on the population counts. The proposed method was evaluated on the real-world dataset and the results were compared with the population-based methods: Multi-EigenSpot and SaTScan. The result shows that the proposed adaptation is effective in approximating the important outputs of the population-based methods. © 2021 Tech Science Press. All rights reserved. Tech Science Press 2021 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114558730&doi=10.32604%2fcmc.2022.019029&partnerID=40&md5=1556495b604ef99633600d64485dd333 Ullah, S. and Nor, N.H.M. and Daud, H. and Zainuddin, N. and Hadi Fanaee, T. and Khalil, A. (2021) An eigenspace method for detecting space-time disease clusters with unknown population-data. Computers, Materials and Continua, 70 (1). pp. 1945-1953. http://eprints.utp.edu.my/29434/
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description Space-time disease cluster detection assists in conducting disease surveillance and implementing control strategies. The state-of-the-art method for this kind of problem is the Space-time Scan Statistics (SaTScan) which has limitations for non-traditional/non-clinical data sources due to its parametric model assumptions such as Poisson or Gaussian counts. Addressing this problem, an Eigenspace-based method called Multi-EigenSpot has recently been proposed as a nonparametric solution. However, it is based on the population counts data which are not always available in the least developed countries. In addition, the population counts are difficult to approximate for some surveillance data such as emergency department visits and over-the-counter drug sales, where the catchment area for each hospital/pharmacy is undefined. We extend the population-based Multi-EigenSpot method to approximate the potential disease clusters from the observed/reported disease counts only with no need for the population counts. The proposed adaptation uses an estimator of expected disease count that does not depend on the population counts. The proposed method was evaluated on the real-world dataset and the results were compared with the population-based methods: Multi-EigenSpot and SaTScan. The result shows that the proposed adaptation is effective in approximating the important outputs of the population-based methods. © 2021 Tech Science Press. All rights reserved.
format Article
author Ullah, S.
Nor, N.H.M.
Daud, H.
Zainuddin, N.
Hadi Fanaee, T.
Khalil, A.
spellingShingle Ullah, S.
Nor, N.H.M.
Daud, H.
Zainuddin, N.
Hadi Fanaee, T.
Khalil, A.
An eigenspace method for detecting space-time disease clusters with unknown population-data
author_facet Ullah, S.
Nor, N.H.M.
Daud, H.
Zainuddin, N.
Hadi Fanaee, T.
Khalil, A.
author_sort Ullah, S.
title An eigenspace method for detecting space-time disease clusters with unknown population-data
title_short An eigenspace method for detecting space-time disease clusters with unknown population-data
title_full An eigenspace method for detecting space-time disease clusters with unknown population-data
title_fullStr An eigenspace method for detecting space-time disease clusters with unknown population-data
title_full_unstemmed An eigenspace method for detecting space-time disease clusters with unknown population-data
title_sort eigenspace method for detecting space-time disease clusters with unknown population-data
publisher Tech Science Press
publishDate 2021
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114558730&doi=10.32604%2fcmc.2022.019029&partnerID=40&md5=1556495b604ef99633600d64485dd333
http://eprints.utp.edu.my/29434/
_version_ 1738656965235572736
score 13.211869

Similar Items