Dynamical intervention planning against COVID-19-like epidemics

Gabriele Oliva; Martin Schlueter; Masaharu Munetomo; Antonio Scala

doi:10.1371/journal.pone.0269830

Dynamical intervention planning against COVID-19-like epidemics

PLoS One. 2022 Jun 14;17(6):e0269830. doi: 10.1371/journal.pone.0269830. eCollection 2022.

Authors

Gabriele Oliva¹, Martin Schlueter², Masaharu Munetomo², Antonio Scala^{3

4

5

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Affiliations

¹ Unit of Automatic Control, Department of Engineering, Università Campus Bio-Medico di Roma, Rome, Italy.
² Information Initiative Center, Hokkaido University, Sapporo, Japan.
³ CNR-ISC, Applico Lab, Roma, Italy.
⁴ Centro Ricerche Enrico Fermi, Roma, Italy.
⁵ Big Data in Health Society, Roma, Italy.
⁶ Global Health Security Agenda - GHSA, Roma, Italy.

Abstract

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.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

COVID-19* / epidemiology
COVID-19* / prevention & control
Critical Care
Epidemics* / prevention & control
Hospitalization
Humans
Infant, Newborn
Vaccination

Grants and funding

Please note that prof. Masaharu Munetomo received funding by the Japan Society for the Promotion of Science (JSPS) under the funding scheme KAKENHI, Grant Number JP20K11967. We declare that the funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.