Persistence of the Omicron variant of SARS-CoV-2 in Australia: The impact of fluctuating social distancing

Abstract

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.

Fig 1. Incidence and disease burden of COVID-19 in Australia between 26th November 2021 and 13th June 2022.

Shaded areas in grey and blue show the emergence of variants of concern and sub-lineages over time, identified in weekly genomic surveillance reports (NSW Health). Top (a): Black (y-axis, left): a 7-day moving average of reported COVID-19 daily incidence. Orange (y-axis, right): COVID-19 related deaths. Middle (b): Black (y-axis, left): COVID-19 hospitalisations (bed occupancy). Orange (y-axis, right): COVID-19 ICU cases (bed occupancy). Bottom (c): Black (y-axis, left): COVID-19 ICU cases (bed occupancy); Orange (y-axis, right): Daily COVID-19 related deaths (solid), daily COVID-19 deaths (dashed). The data for COVID-19 cases, hospitalisation and ICU occupancy, and mortality are published by the Australian government.

Fig 2. A comparison of incidence produced by different social distancing (SD) profiles.

The plot contrasts dynamic social distancing (SD) adoption (shown in solid black) and static SD-adoption fractions (SD₁ = 0.2, shown in red; SD₂ = 0.7, shown in green). Coloured shaded areas around the solid line show standard deviation. Changes in dynamic SD-adoption are marked by vertical dashed black lines. Traces corresponding to each simulated scenario are computed as the average over 20 runs. Simulated SD adoption is combined with other interventions (i.e., school closures, case isolation, and home quarantine). A 7-day moving average of the actual time series (black crosses) is shown for the period between 26th November 2021 and 13th June 2022 (x-axis, bottom). The simulated incidence is scaled up by 10% to reflect the population increase from 23.4 million (2016 census data, model input) to 25.8 million (2021 census data). The simulated incidence is offset by 12 days (x-axis, top) to align with the observed incidence peak. Shaded areas in grey and blue show the emergence of variants of concern and sub-lineages over time, identified in weekly genomic surveillance reports (NSW Health).

Fig 3. A comparison of hospitalisations (bed occupancy) produced by different social distancing (SD) profiles.

The plot contrasts dynamic social distancing (SD) levels (shown in solid black) and static SD levels (SD₁ = 0.2, shown in red; SD₂ = 0.7, shown in green). The simulated hospitalisations are offset by 7 days. Coloured shaded areas around the solid line show standard deviation. Changes in dynamic SD-adoption are marked by vertical dashed black lines. Traces corresponding to each simulated scenario are computed as the average over 20 runs. SD adoption is combined with other interventions (i.e., school closures, case isolation, and home quarantine). The actual time series (black crosses), shown from 26th November 2021, aligns with the start of the Omicron outbreak in Australia. Shaded areas in grey and blue show the emergence of variants of concern and sub-lineages over time, identified in weekly genomic surveillance reports (NSW Health).

Fig 4. A comparison of ICU (bed occupancy) produced by different social distancing (SD) profiles.

The plot contrasts (i) dynamic SD levels, adjusted for phase 6 by scaling with 0.77 to capture ICU cases due to COVID-19 (solid black), (ii) non-adjusted dynamic SD levels to represent all ICU cases (dotted black), and (iii) static SD levels (SD₁ = 0.2, shown in red; SD₂ = 0.7, shown in green). The simulated ICUs are offset by 7 days. Coloured shaded areas around the solid line show standard deviation. Changes in dynamic SD-adoption are marked by vertical dashed black lines. Traces corresponding to each simulated scenario are computed as the average over 20 runs. SD adoption is combined with other interventions (i.e., school closures, case isolation, and home quarantine). The actual time series (black crosses), shown from 26th November 2021, aligns with the start of the Omicron outbreak in Australia. Shaded areas in grey and blue show the emergence of variants of concern and sub-lineages over time, identified in weekly genomic surveillance reports (NSW Health).

Fig 5. A comparison of daily deaths produced by different social distancing (SD) profiles.

The plot contrasts dynamic social distancing (SD) levels (shown in solid black) and static SD levels (SD₁ = 0.2, shown in red; SD₂ = 0.7, shown in green). The simulated daily deaths are offset by 14 days. Coloured shaded areas around the solid line show standard deviation. Changes in dynamic SD-adoption are marked by vertical dashed black lines. Traces corresponding to each simulated scenario are computed as the average over 20 runs. SD adoption is combined with other interventions (i.e., school closures, case isolation, and home quarantine). Actual daily deaths are derived from reported weekly deaths (shown in orange; solid: COVID-19 related deaths; dashed: COVID-19 deaths). Shaded areas in grey and blue show the emergence of variants of concern and sub-lineages over time, identified in weekly genomic surveillance reports (NSW Health). The timeline is divided into 6 phases as follows: 1) BA.1 detected, 2) Delta and BA.1 co-exist, 3) BA.1 dominant, 4) BA.2 detected, 5) BA.1 and BA.2 co-exist, and 6) BA.2 dominant.