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• W4220741352 abstract "The COVID-19 pandemic has had an unprecedented impact on global health and the economy since its inception in December, 2019 in Wuhan, China. Non-pharmaceutical interventions (NPI) like lockdowns and curfews have been deployed by affected countries for controlling the spread of infections. In this paper, we develop a Mixed Integer Non-Linear Programming (MINLP) epidemic model for computing the optimal sequence of NPIs over a planning horizon, considering shortages in doctors and hospital beds, under three different lockdown scenarios. We analyse two strategies - centralised (homogeneous decisions at the national level) and decentralised (decisions differentiated across regions), for two objectives separately - minimization of infections and deaths, using actual pandemic data of France. We linearize the quadratic constraints and objective functions in the MINLP model and convert it to a Mixed Integer Linear Programming (MILP) model. A major result that we show analytically is that under the epidemic model used, the optimal sequence of NPIs always follows a decreasing severity pattern. Using this property, we further simplify the MILP model into an Integer Linear Programming (ILP) model, reducing computational time up to 99%. Our numerical results show that a decentralised strategy is more effective in controlling infections for a given severity budget, yielding up to 20% lesser infections, 15% lesser deaths and 60% lesser shortages in healthcare resources. These results hold without considering logistics aspects and for a given level of compliance of the population." @default.
• W4220741352 created "2022-04-03" @default.
• W4220741352 creator A5050551153 @default.
• W4220741352 creator A5052029573 @default.
• W4220741352 date "2022-12-01" @default.
• W4220741352 modified "2023-09-26" @default.
• W4220741352 title "Designing an optimal sequence of non‐pharmaceutical interventions for controlling COVID-19" @default.
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• W4220741352 doi "https://doi.org/10.1016/j.ejor.2022.03.052" @default.
• W4220741352 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35382429" @default.
• W4220741352 hasPublicationYear "2022" @default.
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