Temporospatial clustering of foot-and-mouth disease outbreaks in Israel and Palestine, 2006-2007

M. A. Alkhamis, A. M. Perez, H. Yadin, N. J. Knowles

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20 Scopus citations


Foot-and-mouth disease (FMD) is endemic to the Middle East and there is a perception that political instability and limited resources have led to the uncontrolled circulation of FMD virus throughout the region. Certain aspects of FMD epidemiology in the Middle East remain unknown. The goal of this study was to identify the geographical location, temporal extent and direction of spread of clusters of 70 FMD outbreaks reported in Israel and Palestine from February 4, 2006, through July 15, 2007. The space-time permutation model of the scan statistic test detected nine significant (P < 0.1) clusters. Significant (P < 0.05) direction of spread was identified in four of the nine clusters. The Gaza Strip, where no outbreaks were reported, or a nearby location, seemed to be the origin of a cluster of outbreaks located in Hadarom (April 2007); a cluster of outbreaks centered in West Bank (February 2006) may be linked with spread from Northern Israel; a cluster in Hazafon (January 2007) seemed to have originated from nearby the Jordan borders; and a cluster located in Northern Hazafon was likely related to areas next to the Lebanon and Syrian borders. The association between the clusters in West Bank and earlier Israeli samples and between the cluster in Hazafon and Jordan was also supported (P < 0.05) by phylogenetic analysis of samples collected from the outbreaks. These results suggest that the FMD outbreaks reported in Israel and Palestine in 2006 and 2007 were likely a consequence of different epidemics associated with the circulation and spread of FMD virus strains from different regions of the Middle East.

Original languageEnglish (US)
Pages (from-to)99-107
Number of pages9
JournalTransboundary and Emerging Diseases
Issue number3
StatePublished - Apr 2009


  • Epidemiology
  • Foot-and-mouth disease
  • Israel
  • Middle East
  • Palestine
  • Temporospatial clusters


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