Modelling, prediction and design of COVID-19 lockdowns by stringency and duration.
Modelling, prediction and design of COVID-19 lockdowns by stringency and duration.
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DOI：
10.1038/s41598-021-95163-8
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发表时间：
2021-08-03
影响因子：
4.6
通讯作者：
Scarciotti G
中科院分区：
文献类型：
--
作者：
Mellone A;Gong Z;Scarciotti G
实施封锁是遏制COVID-19传播和控制感染人数的一项关键政策。然而，根据封锁的严格程度和持续时间提前定量预测封锁的影响是一项复杂的任务，这反过来又使政府难以制定有效的战略来遏制这种疾病。利用一种新颖的数学“混合”方法，我们提出了一种新的流行病模型，该模型能够在执行不同严格程度或持续时间的封锁时预测未来的活跃病例和死亡人数。关键的观察结果是，传统的平均场区隔模型可能无法捕捉到封锁引起的社会习惯的改变，因为这些模型假设人群之间的社会互动是均匀的，而在封锁期间这一假设失败了。我们的模型能够捕捉到由封锁引起的突然的社会习惯变化。通过预测过去的封锁情况，并提供不同封锁情况（不同严格程度和持续时间）的预测，对以色列和德国的数据进行了验证。研究结果表明，该模型可以有效地支持封锁策略的严格性和持续时间设计，并定量预测封锁期间的疫情进程。
The implementation of lockdowns has been a key policy to curb the spread of COVID-19 and to keep under control the number of infections. However, quantitatively predicting in advance the effects of lockdowns based on their stringency and duration is a complex task, in turn making it difficult for governments to design effective strategies to stop the disease. Leveraging a novel mathematical “hybrid” approach, we propose a new epidemic model that is able to predict the future number of active cases and deaths when lockdowns with different stringency levels or durations are enforced. The key observation is that lockdown-induced modifications of social habits may not be captured by traditional mean-field compartmental models because these models assume uniformity of social interactions among the population, which fails during lockdown. Our model is able to capture the abrupt social habit changes caused by lockdowns. The results are validated on the data of Israel and Germany by predicting past lockdowns and providing predictions in alternative lockdown scenarios (different stringency and duration). The findings show that our model can effectively support the design of lockdown strategies by stringency and duration, and quantitatively forecast the course of the epidemic during lockdown.
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