Unit of Automatic Control, Department of Engineering, Università Campus Bio-Medico di Roma, Rome, Italy.
Information Initiative Center, Hokkaido University, Sapporo, Japan.
PLoS One. 2022 Jun 14;17(6):e0269830. doi: 10.1371/journal.pone.0269830. eCollection 2022.
COVID-19 has got us to face a new situation where, for the lack of ready-to-use vaccines, it is necessary to support vaccination with complex non-pharmaceutical strategies. In this paper, we provide a novel Mixed Integer Nonlinear Programming formulation for fine-grained optimal intervention planning (i.e., at the level of the single day) against newborn epidemics like COVID-19, where a modified SIR model accounting for heterogeneous population classes, social distancing and several types of vaccines (each with its efficacy and delayed effects), allows us to plan an optimal mixed strategy (both pharmaceutical and non-pharmaceutical) that takes into account both the vaccine availability in limited batches at selected time instants and the need for second doses while keeping hospitalizations and intensive care occupancy below a threshold and requiring that new infections die out at the end of the planning horizon. In order to show the effectiveness of the proposed formulation, we analyze a case study for Italy with realistic parameters.
COVID-19 使我们面临一种新情况，由于缺乏现成的疫苗，有必要通过复杂的非药物策略来支持疫苗接种。在本文中，我们提供了一种新的混合整数非线性规划公式，用于针对 COVID-19 等新生儿传染病进行精细的干预规划（即，在单日水平上），其中一个修改后的 SIR 模型考虑了异质人群类别、社交距离和几种类型的疫苗（每种疫苗都有其疗效和延迟效果），使我们能够规划出一种最佳的混合策略（药物和非药物），既要考虑在选定时间点分批有限供应的疫苗，又要考虑第二剂的需求，同时将住院和重症监护占用率保持在阈值以下，并要求在规划期结束时消除新的感染。为了展示所提出的公式的有效性，我们使用具有现实参数的意大利案例研究进行了分析。
