Volume IV-4/W2
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-4/W2, 167-173, 2017
https://doi.org/10.5194/isprs-annals-IV-4-W2-167-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-4/W2, 167-173, 2017
https://doi.org/10.5194/isprs-annals-IV-4-W2-167-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.

  20 Oct 2017

20 Oct 2017

GLOBAL DIFFUSION PATTERN AND HOT SPOT ANALYSIS OF VACCINE-PREVENTABLE DISEASES

Y. Jiang1, F. Fan2, I. Holly Zanoni2, and Y. Li1 Y. Jiang et al.
  • 1NSF Spatiotemporal Innovation Center, George Mason University, Fairfax, VA 22030, USA
  • 2International Development, Community and Environment Department, Clark University, Worcester, MA 01610, USA

Keywords: Spatiotemporal Analysis, Hot Spot Analysis, Public Health, GWR

Abstract. Spatial characteristics reveal the concentration of vaccine-preventable disease in Africa and the Near East and that disease dispersion is variable depending on disease. The exception is whooping cough, which has a highly variable center of concentration from year to year. Measles exhibited the only statistically significant spatial autocorrelation among all the diseases under investigation. Hottest spots of measles are in Africa and coldest spots are in United States, warm spots are in Near East and cool spots are in Western Europe. Finally, cases of measles could not be explained by the independent variables, including Gini index, health expenditure, or rate of immunization. Since the literature confirms that each of the selected variables is considered determinants of disease dissemination, it is anticipated that the global dataset of disease cases was influenced by reporting bias.