A data science pipeline applied to Australia's 2022 COVID-19 Omicron waves
The 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 CO...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2025-03-01
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| Series: | Infectious Disease Modelling |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2468042724000988 |
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| author | James M. Trauer Angus E. Hughes David S. Shipman Michael T. Meehan Alec S. Henderson Emma S. McBryde Romain Ragonnet |
| author_facet | James M. Trauer Angus E. Hughes David S. Shipman Michael T. Meehan Alec S. Henderson Emma S. McBryde Romain Ragonnet |
| author_sort | James M. Trauer |
| collection | DOAJ |
| description | The 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 it with several open-source libraries and applied this composite 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.The pipeline's advantages include markedly increased speed, an expressive application programming interface, the transparency of open-source development, easy access to a broad range of calibration and optimisation tools and consideration of the full workflow from input manipulation through to algorithmic generation of the publication materials. Extending the base model to include mobility effects slightly improved model fit to data, with this approach selected as the model configuration for further epidemiological inference. Under our assumption of widespread immunity against severe outcomes from recent vaccination, incorporating an additional effect of the main vaccination programs rolled out during 2022 on transmission did not further improve model fit. Our simulations suggested that one in every two to six COVID-19 episodes were detected, subsequently emerging Omicron subvariants escaped 30–60% of recently acquired natural immunity and that natural immunity lasted only one to eight months on average. We documented our analyses algorithmically and present our methods in conjunction with interactive online code notebooks and plots.We demonstrate the feasibility of integrating a flexible domain-specific syntax library with state-of-the-art packages in high performance computing, calibration, optimisation and visualisation to create an end-to-end pipeline for infectious disease modelling. We used the resulting platform to demonstrate key epidemiological characteristics of the transition from the emergency to the endemic phase of the COVID-19 pandemic. |
| format | Article |
| id | doaj-art-cde9b5ea88bc4835a0002570ff38d7a7 |
| institution | Kabale University |
| issn | 2468-0427 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Infectious Disease Modelling |
| spelling | doaj-art-cde9b5ea88bc4835a0002570ff38d7a72024-12-21T04:28:44ZengKeAi Communications Co., Ltd.Infectious Disease Modelling2468-04272025-03-0110199109A data science pipeline applied to Australia's 2022 COVID-19 Omicron wavesJames M. Trauer0Angus E. Hughes1David S. Shipman2Michael T. Meehan3Alec S. Henderson4Emma S. McBryde5Romain Ragonnet6School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia; Corresponding author.School of Public Health and Preventive Medicine, Monash University, Melbourne, AustraliaSchool of Public Health and Preventive Medicine, Monash University, Melbourne, AustraliaAustralian Institute of Tropical Health and Medicine, James Cook University, Townsville, AustraliaAustralian Institute of Tropical Health and Medicine, James Cook University, Townsville, AustraliaAustralian Institute of Tropical Health and Medicine, James Cook University, Townsville, AustraliaSchool of Public Health and Preventive Medicine, Monash University, Melbourne, AustraliaThe 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 it with several open-source libraries and applied this composite 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.The pipeline's advantages include markedly increased speed, an expressive application programming interface, the transparency of open-source development, easy access to a broad range of calibration and optimisation tools and consideration of the full workflow from input manipulation through to algorithmic generation of the publication materials. Extending the base model to include mobility effects slightly improved model fit to data, with this approach selected as the model configuration for further epidemiological inference. Under our assumption of widespread immunity against severe outcomes from recent vaccination, incorporating an additional effect of the main vaccination programs rolled out during 2022 on transmission did not further improve model fit. Our simulations suggested that one in every two to six COVID-19 episodes were detected, subsequently emerging Omicron subvariants escaped 30–60% of recently acquired natural immunity and that natural immunity lasted only one to eight months on average. We documented our analyses algorithmically and present our methods in conjunction with interactive online code notebooks and plots.We demonstrate the feasibility of integrating a flexible domain-specific syntax library with state-of-the-art packages in high performance computing, calibration, optimisation and visualisation to create an end-to-end pipeline for infectious disease modelling. We used the resulting platform to demonstrate key epidemiological characteristics of the transition from the emergency to the endemic phase of the COVID-19 pandemic.http://www.sciencedirect.com/science/article/pii/S2468042724000988Computational simulationEpidemiologySoftware designCOVID-19 |
| spellingShingle | James M. Trauer Angus E. Hughes David S. Shipman Michael T. Meehan Alec S. Henderson Emma S. McBryde Romain Ragonnet A data science pipeline applied to Australia's 2022 COVID-19 Omicron waves Infectious Disease Modelling Computational simulation Epidemiology Software design COVID-19 |
| title | A data science pipeline applied to Australia's 2022 COVID-19 Omicron waves |
| title_full | A data science pipeline applied to Australia's 2022 COVID-19 Omicron waves |
| title_fullStr | A data science pipeline applied to Australia's 2022 COVID-19 Omicron waves |
| title_full_unstemmed | A data science pipeline applied to Australia's 2022 COVID-19 Omicron waves |
| title_short | A data science pipeline applied to Australia's 2022 COVID-19 Omicron waves |
| title_sort | data science pipeline applied to australia s 2022 covid 19 omicron waves |
| topic | Computational simulation Epidemiology Software design COVID-19 |
| url | http://www.sciencedirect.com/science/article/pii/S2468042724000988 |
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