Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (1)
  • Open Access


    An Eigenspace Method for Detecting Space-Time Disease Clusters with Unknown Population-Data

    Sami Ullah1,*, Nurul Hidayah Mohd Nor1, Hanita Daud1, Nooraini Zainuddin1, Hadi Fanaee-T2, Alamgir Khalil3

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1945-1953, 2022, DOI:10.32604/cmc.2022.019029

    Abstract 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… More >

Displaying 1-10 on page 1 of 1. Per Page