Purpose

The purpose of this paper is to develop a simple deterministic model that quantifies previously adopted preventive measures driven by the trend of the reported number of deaths in both Italy and India. In addition, the authors forecast the spread based on some selected quantified preventive measures. The optimal exiting policy is derived using the inverse dynamics of the model. Furthermore, the model developed by the authors is dependent on the daily number of deaths; as such, it is sensitive to the death rate but remains insensitive to trends in deaths.

Design/methodology/approach

In the wake of COVID-19, policymakers and health professionals realized the limitations and shortcomings of current healthcare systems and pandemic response policies. The need to revise global and national pandemic response mechanisms has been thrust into the public spotlight. To this end, the authors devise an approach to identify the most suitable governmental non-pharmaceutical intervention (NPI) policies, previously adopted in a community, country or region that serve as the foundation for most pandemic strategies.

Findings

Leveraging Italy, the authors compare the aftermath by considering three scenarios: (a) recently adopted preventive measures, (b) strictest preventive measures previously adopted, and (c) the optimal exiting policy. In comparison to the second scenario, the authors estimate about twice the number of recoveries and deaths within five months under the first scenario and about 80 times more under the optimal scenario. Whereas in India, the authors applied one scenario of recently adopted preventative measures to showcase the rapid turnaround of their model. According to the new timeline, almost 90% of all deaths in India could have been prevented if the policies implemented in April 2021 were put in place three months prior, i.e. in January 2021.

Originality/value

The novelty of the proposed approach is in the use of inverse dynamics of a simple deterministic model that allows capturing the trend of contact rate as a function of adopted NPIs, regardless of pandemic type.

中文翻译：

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设定 COVID-19 锁定放松措施的界限

目的

本文的目的是开发一个简单的确定性模型，该模型量化以前在意大利和印度报告的死亡人数趋势驱动下采取的预防措施。此外，作者根据一些选定的量化预防措施预测了传播。最佳退出策略是使用模型的逆动力学推导出来的。此外，作者开发的模型取决于每日死亡人数；因此，它对死亡率很敏感，但对死亡趋势仍然不敏感。

设计/方法/方法

在 COVID-19 之后，政策制定者和卫生专业人员意识到当前医疗保健系统和大流行应对政策的局限性和缺点。修订全球和国家大流行应对机制的必要性已成为公众关注的焦点。为此，作者设计了一种方法来确定最合适的政府非药物干预 (NPI) 政策，这些政策以前在社区、国家或地区采用，作为大多数大流行战略的基础。

发现

利用意大利，作者通过考虑三种情况来比较后果：(a) 最近采取的预防措施，(b) 以前采取的最严格的预防措施，以及 (c) 最佳退出政策。与第二种情况相比，作者估计在第一种情况下五个月内康复和死亡的人数大约是其两倍，而在最佳情况下则是大约 80 倍。而在印度，作者应用了最近采取的预防措施的一种情况来展示他们模型的快速转变。根据新的时间表，如果在 2021 年 4 月实施的政策在三个月前（即 2021 年 1 月）实施，印度几乎可以避免 90% 的死亡。

原创性/价值

所提议方法的新颖之处在于使用了简单确定性模型的逆动力学，该模型允许捕捉作为所采用 NPI 函数的接触率趋势，而不管大流行类型如何。