Abstract Title
Using sequence data to quantify spatial scales of interactions driving spread of Highly Pathogenic Avian Influenza in Great Britain
Abstract
Quantifying H5N1 virus circulation risks is critical for understanding the long-term impact of the virus and facilitate development of efficient intervention strategies. As pathogen genetic proximity correlates with transmission chain length, these relationships indicate the scale and resolution of interactions driving virus circulation. Here, we develop a novel approach using machine learning models to quantify the relationship between (i) spatially-aggregated environmental, farm demographic and epidemiological variables with (ii) viral phylogenetic data from Great Britain, considering December-May 2021/22, and June-October 2022. We test different scales (from 9 km2 to >10,000 km2) and types (grid or administrative unit) of aggregation and consider all available samples from both wildlife and domesticated poultry infections.
For Dec-May, game bird abundance aggregated in 81 km2 areas and geographical distance were the strongest predictors of genetic distance between samples. For June-October, sample collection date and farm count (by local council area) were most informative. The differences in predictors may be related to differences in the virus, geographical areas of spread, and seasonal factors, such as the large numbers of game birds released into the wild during the winter.
This is the first phylogenetic evidence of a possible role for game birds in the circulation of HPAI in GB, and it is consistent with experimental studies about the potential for pheasants to become infected and with high potential to excrete virus into the environment. However, further investigation is still required to establish causation. Our approach will be useful anywhere that spatio-temporally explicit virus sampling data are available at sufficient density.
For Dec-May, game bird abundance aggregated in 81 km2 areas and geographical distance were the strongest predictors of genetic distance between samples. For June-October, sample collection date and farm count (by local council area) were most informative. The differences in predictors may be related to differences in the virus, geographical areas of spread, and seasonal factors, such as the large numbers of game birds released into the wild during the winter.
This is the first phylogenetic evidence of a possible role for game birds in the circulation of HPAI in GB, and it is consistent with experimental studies about the potential for pheasants to become infected and with high potential to excrete virus into the environment. However, further investigation is still required to establish causation. Our approach will be useful anywhere that spatio-temporally explicit virus sampling data are available at sufficient density.
Co-Author(s)
A. Gamza, University of Edinburgh
S.J. Lycett, University of Edinburgh
A. Sanchez, University of Edinburgh
W. Harvey, University of Edinburgh
S. Vickers, Royal Veterinary College
R.R. Kao, University of Edinburgh
Abstract Category
Notable outbreaks, field and molecular epidemiology, and surveillance in wild birds