TY - ABST

T1 - Surveillance data and early warning models of highly pathogenic avian influenza in East Asia, 2020-2022

AU - Kjær, Lene Jung

AU - Kirkeby, Carsten Thure

AU - Boklund, Anette Ella

AU - Ward, Michael P.

PY - 2023

Y1 - 2023

N2 - Avian influenza poses a problem for animal welfare and potentially also for public health. In recent years, the numbers of disease outbreaks and cases of highly pathogenic avian influenza (HPAI) virus infection throughout the world has increased, resulting in many poultry flocks being culled. Preventive actions require timely knowledge of high-risk infection periods. Thus, there is an increasing need for early warning systems. We utilized readily available data from the World Organization for Animal Health and the Food and Agriculture Organization of the United Nations on HPAI H5 detections in wild and domestic birds together with a time-series modelling framework (Meyer et al., 2014) to predict HPAI detections within East Asia. This framework decomposes time series data into endemic and epidemic components, and we have previously used it successfully to model HPAI in Europe. We tested multiple model formulations, including seasonality, long-distance versus short-distance transmission, and covariates (coastline length and area of wetlands). Due to data constraints, we were only able to fit a model for Japan and South Korea for the years 2020-2022, as these two countries consistently reported outbreaks during the study period. We divided each country into regions based on geography and local administrative areas. The best performing model included seasonality in both the endemic and epidemic components, and covariates as offsets in the endemic component. Due to data constraints, we did not differentiate between H5 subtypes. The model was fitted to all HPAI records with good overall agreement. Interestingly, we found a clear cyclic seasonal component in the predicted occurrence of HPAI, whereas our previous model for Europe for the same time period predicted occurrence during the summer period as well, i.e., less cyclic seasonality. The model predicted that most reported detections (79.3%) were epidemic in nature and were dominated by within-region transmission (55.5%). Of the reported detections, 20.7% was 102 described as endemic transmission in the model, which could represent transmission from migratory birds coming from regions and countries not included in our study and/or endemic H5 HPAI virus circulating within the included regions. The modelling framework used in this study produced a model that was able to predict H5 HPAI detections in Japan and South Korea on a weekly basis and could be utilized by decision makers to predict periods with higher risk of HPAI within these countries. If possible, future modelling studies should obtain more data from other Asian countries to create a combined model for Asia/Oceania. Given enough surveillance data, future studies could also focus on modelling the specific HPAI genotypes and clades; this would potentially improve predictive ability and timeliness, as well as clarify endemic and epidemic components of HPAI occurrence. References Meyer, S., Held, L. & Hìhle, M. (2014). Spatio-Temporal Analysis of Epidemic Phenomena Using the R Package surveillance. Journal of Statistical Software. 77. https://doi.org/10.18637/jss.v077.i11

AB - Avian influenza poses a problem for animal welfare and potentially also for public health. In recent years, the numbers of disease outbreaks and cases of highly pathogenic avian influenza (HPAI) virus infection throughout the world has increased, resulting in many poultry flocks being culled. Preventive actions require timely knowledge of high-risk infection periods. Thus, there is an increasing need for early warning systems. We utilized readily available data from the World Organization for Animal Health and the Food and Agriculture Organization of the United Nations on HPAI H5 detections in wild and domestic birds together with a time-series modelling framework (Meyer et al., 2014) to predict HPAI detections within East Asia. This framework decomposes time series data into endemic and epidemic components, and we have previously used it successfully to model HPAI in Europe. We tested multiple model formulations, including seasonality, long-distance versus short-distance transmission, and covariates (coastline length and area of wetlands). Due to data constraints, we were only able to fit a model for Japan and South Korea for the years 2020-2022, as these two countries consistently reported outbreaks during the study period. We divided each country into regions based on geography and local administrative areas. The best performing model included seasonality in both the endemic and epidemic components, and covariates as offsets in the endemic component. Due to data constraints, we did not differentiate between H5 subtypes. The model was fitted to all HPAI records with good overall agreement. Interestingly, we found a clear cyclic seasonal component in the predicted occurrence of HPAI, whereas our previous model for Europe for the same time period predicted occurrence during the summer period as well, i.e., less cyclic seasonality. The model predicted that most reported detections (79.3%) were epidemic in nature and were dominated by within-region transmission (55.5%). Of the reported detections, 20.7% was 102 described as endemic transmission in the model, which could represent transmission from migratory birds coming from regions and countries not included in our study and/or endemic H5 HPAI virus circulating within the included regions. The modelling framework used in this study produced a model that was able to predict H5 HPAI detections in Japan and South Korea on a weekly basis and could be utilized by decision makers to predict periods with higher risk of HPAI within these countries. If possible, future modelling studies should obtain more data from other Asian countries to create a combined model for Asia/Oceania. Given enough surveillance data, future studies could also focus on modelling the specific HPAI genotypes and clades; this would potentially improve predictive ability and timeliness, as well as clarify endemic and epidemic components of HPAI occurrence. References Meyer, S., Held, L. & Hìhle, M. (2014). Spatio-Temporal Analysis of Epidemic Phenomena Using the R Package surveillance. Journal of Statistical Software. 77. https://doi.org/10.18637/jss.v077.i11

M3 - Conference abstract in proceedings

SP - 102

EP - 103

BT - Abstract book of the GeoVet 2023 International Conference

A2 - Conte, Annamaria

A2 - Ippoliti, Carla

A2 - Savini, Lara

PB - Edizioni IZSTe-press

Y2 - 19 September 2023 through 21 September 2023

ER -