Towards a new, ecologically targeted approach to monitoring wild bird populations for avian influenza viruses

A highly pathogenic avian influenza virus H5N1 clade 2.3.4.4 detected in Samara Oblast, Russian Federation

Avian influenza (AI) is a global problem impacting birds and mammals, causing economic losses in commercial poultry farms and backyard settings. In 2022, over 8,500 AI cases were reported worldwide, with the H5 subtype being responsible for many outbreaks in wild and domestic birds. In the territory of the Russian Federation, outbreaks of AI have been massively reported since 2020, both among domestic bird species and wild bird species. Wild migratory birds often serve as natural reservoirs for AI viruses, and interactions between bird species can lead to the emergence of new, highly pathogenic variants through genetic recombination between strains. In order to combat the widespread outbreaks of the disease and potential risks of further spread in 2021, monitoring studies were conducted in the Samara Oblast, the southeastern region of European Russian Federation. These studies aimed to diagnose and characterize circulating AI virus variants among wild migratory birds during waterfowl hunting in areas of mass nesting. Among the 98 shot birds, a highly pathogenic A/H5N1 AI virus was detected in a Eurasian Teal from the Bolshechernigovsky district. It was classified into clade 2.3.4.4 based on the cleavage site structure of HA. Phylogenetic analysis showed a high relatedness of the identified strain in the Samara Oblast with field isolates from Russia, Nigeria, Bangladesh, and Benin. The article emphasizes the importance of monitoring AI virus spread in both wild and poultry, highlighting the need for timely information exchange to assess risks. Further comprehensive studies are necessary to understand virus dissemination pathways.

Fireworks-like surveillance approach: The case of HPAI H5N1 in wild birds in Europe

Highly pathogenic avian influenza (HPAI) risk management requires efficient surveillance of the infection in wild birds for early warning purposes. In this study, our aim was to describe the spread of continent-wide infection cases using a fireworks model and therefore improve current surveillance systems. The fireworks model is a metaphor illustrating the spread of HPAI as a point source epizootic. The approach is based on early detection of the outbreak seeds (sparks from the fireworks) and uses a predictive model of the probability of the occurrence of new cases following a seed introduction; this then determines the spatiotemporal perimeter for intense surveillance investigations. For a case study, we used surveillance data on HPAI H5N1 in wild birds across Europe between 2005 and 2010 to describe the outbreaks and determine the success of the case detection used to inform management of the disease. The fireworks description assumes simultaneous introductions of 'seeds' of cases, which then 'explode' in local foci but do not merge into a progressive disease wave. This model fits the data well. Using this predictive approach for HPAI cases in EU countries, we found that the investigation radius needed to achieve a detection level of 90% of new cases after an outbreak ranged from 10 km to more than 300 km, depending on the outbreak pattern. Based on these findings, the fireworks approach can be a valuable method for identifying the perimeters and risk areas to be targeted for enhanced surveillance. The rationale of the fireworks approach is quite generic and can easily be adapted to different situations and contexts.

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.