Harmonizing Community Science Datasets to Model Highly Pathogenic Avian Influenza (HPAI) in Birds in the Subantarctic
This work addresses data integration problems for epidemiologists and ecologists studying avian influenza in the subantarctic, but it is incremental as it focuses on applying existing methods to new data.
The authors tackled the challenge of standardizing heterogeneous community science datasets by developing a data workflow, which they applied to model the impact of highly pathogenic avian influenza on bird populations in the subantarctic, resulting in predictions of population sizes and novel mortality rate estimates for several species.
Community science observational datasets are useful in epidemiology and ecology for modeling species distributions, but the heterogeneous nature of the data presents significant challenges for standardization, data quality assurance and control, and workflow management. In this paper, we present a data workflow for cleaning and harmonizing multiple community science datasets, which we implement in a case study using eBird, iNaturalist, GBIF, and other datasets to model the impact of highly pathogenic avian influenza in populations of birds in the subantarctic. We predict population sizes for several species where the demographics are not known, and we present novel estimates for potential mortality rates from HPAI for those species, based on a novel aggregated dataset of mortality rates in the subantarctic.