Persistence of the Omicron variant of SARS-CoV-2 in Australia: The impact of fluctuating social distancing
We modelled emergence and spread of the Omicron variant of SARS-CoV-2 in Australia between December 2021 and June 2022. This pandemic stage exhibited a diverse epidemiological profile with emergence of co-circulating sub-lineages of Omicron, further complicated by differences in social distancing behaviour which varied over time. Our study delineated distinct phases of the Omicron-associated pandemic stage, and retrospectively quantified the adoption of social distancing measures, fluctuating over different time periods in response to the observable incidence dynamics. We also modelled the corresponding disease burden, in terms of hospitalisations, intensive care unit occupancy, and mortality. Supported by good agreement between simulated and actual health data, our study revealed that the nonlinear dynamics observed in the daily incidence and disease burden were determined not only by introduction of sub-lineages of Omicron, but also by the fluctuating adoption of social distancing measures. Our high-resolution model can be used in design and evaluation of public health interventions during future crises..
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Preprint| Erscheinungsjahr: |
2022 |
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| Erschienen: |
2022 |
| Enthalten in: |
arXiv.org - (2022) vom: 20. Nov. Zur Gesamtaufnahme - year:2022 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
Chang, Sheryl L. [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| Themen: |
000 |
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XAR037940023 |
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| 100 | 1 | |a Chang, Sheryl L. |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Persistence of the Omicron variant of SARS-CoV-2 in Australia: The impact of fluctuating social distancing |
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| 520 | |a We modelled emergence and spread of the Omicron variant of SARS-CoV-2 in Australia between December 2021 and June 2022. This pandemic stage exhibited a diverse epidemiological profile with emergence of co-circulating sub-lineages of Omicron, further complicated by differences in social distancing behaviour which varied over time. Our study delineated distinct phases of the Omicron-associated pandemic stage, and retrospectively quantified the adoption of social distancing measures, fluctuating over different time periods in response to the observable incidence dynamics. We also modelled the corresponding disease burden, in terms of hospitalisations, intensive care unit occupancy, and mortality. Supported by good agreement between simulated and actual health data, our study revealed that the nonlinear dynamics observed in the daily incidence and disease burden were determined not only by introduction of sub-lineages of Omicron, but also by the fluctuating adoption of social distancing measures. Our high-resolution model can be used in design and evaluation of public health interventions during future crises. | ||
| 650 | 4 | |a Quantitative Biology - Populations and Evolution |7 (dpeaa)DE-84 | |
| 650 | 4 | |a Computer Science - Multiagent Systems |7 (dpeaa)DE-84 | |
| 650 | 4 | |a 570 |7 (dpeaa)DE-84 | |
| 650 | 4 | |a 000 |7 (dpeaa)DE-84 | |
| 700 | 1 | |a Nguyen, Quang Dang |4 aut | |
| 700 | 1 | |a Martiniuk, Alexandra |4 aut | |
| 700 | 1 | |a Sintchenko, Vitali |4 aut | |
| 700 | 1 | |a Sorrell, Tania C. |4 aut | |
| 700 | 1 | |a Prokopenko, Mikhail |4 aut | |
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