The Use of Spatial and Spatiotemporal Modeling for Surveillance of H5N1 Highly Pathogenic Avian Influenza in Poultry in the Middle East

Since 2005, H5N1 highly pathogenic avian influenza virus (HPAIV) has severely impacted the economy and public health in the Middle East (ME) with Egypt as the most affected country. Understanding the high-risk areas and spatiotemporal distribution of the H5N1 HPAIV in poultry is prerequisite for establishing risk-based surveillance activities at a regional level in the ME. Here, we aimed to predict the geographic range of H5N1 HPAIV outbreaks in poultry in the ME using a set of environmental variables and to investigate the spatiotemporal clustering of outbreaks in the region. Data from the ME for the period 2005-14 were analyzed using maximum entropy ecological niche modeling and the permutation model of the scan statistics. The predicted range of high-risk areas (P > 0.60) for H5N1 HPAIV in poultry included parts of the ME northeastern countries, whereas the Egyptian Nile delta and valley were estimated to be the most suitable locations for occurrence of H5N1 HPAIV outbreaks. The most important environmental predictor that contributed to risk for H5N1 HPAIV was the precipitation of the warmest quarter (47.2%), followed by the type of global livestock production system (18.1%). Most significant spatiotemporal clusters (P < 0.001) were detected in Egypt, Turkey, Kuwait, Saudi Arabia, and Sudan. Results suggest that more information related to poultry holding demographics is needed to further improve prediction of risk for H5N1 HPAIV in the ME, whereas the methodology presented here may be useful in guiding the design of surveillance programs and in identifying areas in which underreporting may have occurred.