Setting the boundaries of COVID-19 lockdown relaxation measures

Abstract

PurposeThe 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/approachIn 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.FindingsLeveraging 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/valueThe 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.

Editorial: Special selection on contemporary bibliometric analytics

“…They discovered 14 research topics in this area which can help specialists in health and medical areas to grasp the structured morphology of the current COVID-19 research. Readers may also notice our wealth of articles on bibliometrics related to COVID-19 (Li et al ., 2021a; Shueb et al ., 2022; Loan and Shah, 2022; Riahinia et al ., 2022; Zhu and Lei, 2022; Danesh et al ., 2021; Saab et al ., 2021; Allen, 2021; Yari Eili and Rezaeenour, 2023; Nadi-Ravandi and Batooli, 2023) and healthcare (Balaei-Kahnamoei et al ., 2022).…”

Guest editorial: COVID-19 Pandemic and Health Informatics Part 2

“…Danesh et al (2021) reported the topic modelling of the global coronavirus publications in the last 50 years with applied text mining and Latent Dirichlet allocation. Saab et al (2021) developed a deterministic model that quantifies previously adopted preventive measures driven by the reported number of deaths in Italy and India and used it to derive the optimal exiting policy using the inverse dynamics of the model. Feng et al (2021b) found that the growth sequence of the number of new confirmed COVID-19 cases per day had a significant cluster of fluctuations.…”

Public information needs during the COVID-19 outbreak: a qualitative study in mainland China

“…Issues including frequent policy announcements, unknown virus nature and infection case diversity have all led to urgent public needs for epidemic information (Bento et al, 2020;Zhao et al, 2020;Nguyen and Le, 2021;Rocha et al, 2021). Further, such needs have been further accentuated on the internet in stringent "home isolation" epidemic control policies and various lockdowns of public services (Saab et al, 2021;Fasae et al, 2021;Meng et al, 2022;Huang et al, 2021Huang et al, , 2022.…”