Simulating Transmission Scenarios of the Delta Variant of SARS-CoV-2 in Australia.
Simulating Transmission Scenarios of the Delta Variant of SARS-CoV-2 in Australia.
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DOI：
10.3389/fpubh.2022.823043
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发表时间：
2022
影响因子：
5.2
通讯作者：
Prokopenko M
中科院分区：
文献类型：
--
作者：
Chang SL;Cliff OM;Zachreson C;Prokopenko M
2021年6月中旬左右，澳大利亚悉尼爆发了SARS-CoV-2的Delta（B.1.617.2）变种，并迅速发展成为全国性疫情。由于Delta变异病毒比2020年在澳大利亚传播的先前变异病毒更具传染性，因此持续的疫情备受关注。使用重新校准的基于代理人的模型，我们探索了一系列可行的非药物干预措施，包括病例隔离、家庭隔离、学校关闭和居家限制（即，“社会距离”）我们的模型表明，在悉尼和全国其他地区，工作场所和社区之间的相互作用减少的程度不足以控制疫情。一项反事实的分析表明，如果70%的人口遵守严格的居家限制，那么至少需要45天才能将每日新增病例从峰值降至每日10例以下。我们的模型预测，在逐步推广疫苗接种的情况下，如果40-50%的澳大利亚人口遵守居家限制，发病率将在2021年10月中旬达到峰值：全国发病率的峰值确实在10月中旬观察到。我们还量化了澳大利亚医疗系统的预期负担和潜在死亡人数。
An outbreak of the Delta (B.1.617.2) variant of SARS-CoV-2 that began around mid-June 2021 in Sydney, Australia, quickly developed into a nation-wide epidemic. The ongoing epidemic is of major concern as the Delta variant is more infectious than previous variants that circulated in Australia in 2020. Using a re-calibrated agent-based model, we explored a feasible range of non-pharmaceutical interventions, including case isolation, home quarantine, school closures, and stay-at-home restrictions (i.e., “social distancing.”) Our modelling indicated that the levels of reduced interactions in workplaces and across communities attained in Sydney and other parts of the nation were inadequate for controlling the outbreak. A counter-factual analysis suggested that if 70% of the population followed tight stay-at-home restrictions, then at least 45 days would have been needed for new daily cases to fall from their peak to below ten per day. Our model predicted that, under a progressive vaccination rollout, if 40–50% of the Australian population follow stay-at-home restrictions, the incidence will peak by mid-October 2021: the peak in incidence across the nation was indeed observed in mid-October. We also quantified an expected burden on the healthcare system and potential fatalities across Australia.
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影响因子：
5.5
作者：
Raina MacIntyre C;Costantino V;Chanmugam A
通讯作者：
Chanmugam A
影响因子：
4.2
作者：
Cliff, Oliver M.;Harding, Nathan;Prokopenko, Mikhail
通讯作者：
Prokopenko, Mikhail
影响因子：
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作者：
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影响因子：
16.6
作者：
Chang SL;Harding N;Zachreson C;Cliff OM;Prokopenko M
通讯作者：
Prokopenko M
影响因子：
3.7
作者：
Ajelli M;Gonçalves B;Balcan D;Colizza V;Hu H;Ramasco JJ;Merler S;Vespignani A
通讯作者：
Vespignani A