Spatio-temporal analysis of Highly Pathogenic Avian Influenza HPAI (H5N1) in poultry in Menofia governorate, Egypt

Abstract

Abstract Background: Menofia governorate is one of Lower Egypt's governorates where the probability of zoonotic transmission of the H5N1 pandemic was high. This study aimed to investigate the spatiotemporal pattern, identify, and trace the highest risk clusters of HPAI H5N1 outbreaks at the subdistrict/village level in Menofia governorate, from 2006 to 2017 as a trial for tracking the HPAI H5N1 endemicity dynamics for better establishment of effective disease control strategies at that level. Results: The epidemic curve in Menofia was similar to the national curve. Although the poultry population in Menofia was affected earlier than other places, the 1st Epidemic Wave (EW) started one week after the initial outbreak in Egypt, the HPAI H5N1 outbreaks never initiated from Menofia in all EWs. The outbreaks' spatial risk increases at the northern governorate border with a decrease in the spatial risk by the 6th EW. The hot spot region in Menofia was found in rural districts, especially villages, while outbreak density decreased with increased urbanization. Observed smoothed densities describe epidemic spread dynamics where the infection spreads and connects many different locations inside the same city, before jumping to new areas and directly connecting the nearest neighbor cities. The primary clusters could be predicted since they occur in the same areas where the highest relative risk clusters were recorded in the previous wave. Identifying continuous pinpointing clusters that persist for a long time, possibly spanning months, indicates the local transmission of the virus among poultry due to contact and widespread circulation. It is crucial to take early measures to prevent outbreaks at the initial sites before the outbreak acceleration phase, in order to minimize the geographic spread and confine the infection to specific areas. That suggests the need for the establishment of effective disease control strategies at the subdistrict level based on a better understanding of the endemicity dynamics.