In early 2020 Northern Italy had to reorganize its whole public health system, in an extremely short amount of time to prevent collapse. A home health care service for mild COVID-19 patients (COD-19) was activated to relieve the pressure on hospital wards, while simultaneously improving the level of service to these patients. The COD-19 service is designed to rely on a dedicated software platform, which serves for both operational support during emergencies and for strategic and tactical planning. We report our actions to provide institutional decision makers with advanced planning and operational tools which integrate the COD-19 platform. Three mathematical models allow for quantitative data analysis. They are designed to provide support both in a proactive way during early pandemic stages, and in a reactive way during and after emergency peaks. We also present experiments on real data from the COVID-19 pandemic emergency in Northern Italy, which indicate our approach to be effective.

Dealing with the COVID-19 emergency by mathematical models: the COD-19 platform / M. Barbato, C. Carlevaro, A. Ceselli, G. Confessore, G. De Luca, M. Premoli. - [s.l] : Università degli Studi di Milano, 2022.

Dealing with the COVID-19 emergency by mathematical models: the COD-19 platform

M. Barbato
Primo
;
A. Ceselli;M. Premoli
2022

Abstract

In early 2020 Northern Italy had to reorganize its whole public health system, in an extremely short amount of time to prevent collapse. A home health care service for mild COVID-19 patients (COD-19) was activated to relieve the pressure on hospital wards, while simultaneously improving the level of service to these patients. The COD-19 service is designed to rely on a dedicated software platform, which serves for both operational support during emergencies and for strategic and tactical planning. We report our actions to provide institutional decision makers with advanced planning and operational tools which integrate the COD-19 platform. Three mathematical models allow for quantitative data analysis. They are designed to provide support both in a proactive way during early pandemic stages, and in a reactive way during and after emergency peaks. We also present experiments on real data from the COVID-19 pandemic emergency in Northern Italy, which indicate our approach to be effective.
Planning; Data Analysis; Emergency Management; COVID-19
Settore INF/01 - Informatica
Working Paper
Dealing with the COVID-19 emergency by mathematical models: the COD-19 platform / M. Barbato, C. Carlevaro, A. Ceselli, G. Confessore, G. De Luca, M. Premoli. - [s.l] : Università degli Studi di Milano, 2022.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/929875
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