Setting the boundaries of COVID-19 lockdown relaxation measures

Abstract

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.

Citation

Saab, S., Al Abbas, M., Samaha, R.N., Jaafar, R., Saab, K.K. and Saab Jr, S.S. (2021), "Setting the boundaries of COVID-19 lockdown relaxation measures", Library Hi Tech, Vol. 39 No. 3, pp. 873-887. https://doi.org/10.1108/LHT-03-2021-0095