It has been shown for syndromic data that when exact geographic coordinates of individual patients are used as the spatial base for outbreak detection, higher detection rates and accuracy are achieved compared to when data are aggregated into administrative regions such as zip codes and census tracts.
This study illustrates the importance of considering spatial information for outbreak detection, and demonstrates that using an interpoint distance distribution and precise address locations is a powerful approach.
For example, findings from several countries demonstrate spatial links between outbreaks of HPAI in poultry and outbreaks in wild birds (e.g., 5– 6).
Most notably, the dynamic programming algorithm yields much better spatial precision for outbreaks with higher spatial coverage (and therefore more outbreak rectangles).
The spatial distribution of outbreaks in birds and human cases was studied through overlapping analysis.
Measles data from pre-vaccination England and Wales (1944 64) highlight spatial patterns in outbreaks and, therefore, also indirectly reveal patterns in human movement [6], [7].
To understand the role of bird migration in the spread of H5N1 virus, map layer of bird migration was created and overlapped on the map of spatial distribution of outbreaks in birds and human cases.
The objective of this research was to develop a transparent user-friendly method to simulate spatial-temporal disease outbreak data for outbreak detection algorithm evaluation.
The high level of biosecurity at European poultry farms, the broad spatial distribution of the outbreak locations, and the outbreak timing which coincided with temperature drops in the Black Sea region [18] have been used to suggest that infected wild birds carried virus hundreds of kilometers to the locations where the mortalities were documented [19], [20].
Another crucial factor in the outbreak is spatial heterogeneity (i.e., diversity in spatial dimension, brought on by the factor of distance).
Therefore, space-time disease-surveillance methods should be proposed as a dynamic supplement to purely spatial statistical methods for outbreak detection to detect and predict localized outbreaks before they spread to larger regions.
