Abstract
AbstractThe field of software engineering is advancing at astonishing speed, with packages now available to support many stages of data science pipelines. These packages can support infectious disease modelling to be more robust, efficient and transparent, which has been particularly important during the COVID-19 pandemic. We developed a package for the construction of infectious disease models, integrated this with several open-source libraries and applied this pipeline to multiple data sources that provided insights into Australia’s 2022 COVID-19 epidemic. We aimed to identify the key processes relevant to COVID-19 transmission dynamics and thereby develop a model that could quantify relevant epidemiological parameters. Extending the base model to include mobility effects slightly improved model fit to data, but including the effect of 2022 vaccination programs on transmission did not. Our simulations suggested that one in every two to six COVID-19 episodes were detected, subsequently emerging Omicron subvariants escaped 30 to 60% of recently acquired natural immunity and that natural immunity lasted only one to eight months. We documented our analyses algorithmically and present our methods in conjunction with interactive online notebooks.
Publisher
Cold Spring Harbor Laboratory