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Dettaglio pubblicazione

2023, DIGITAL HEALTH, Pages 1-20 (volume: 9)

A predictive decision support system for coronavirus disease 2019 response management and medical logistic planning (01a Articolo in rivista)

Atek S., Bianchini F., De Vito C., Cardinale V., Novelli S., Pesaresi C., Eugeni M., Mecella M., Rescio A., Petronzio L., Vincenzi A., Pistillo P., Giusto G., Pasquali G., Alvaro D., Villari P., Mancini M., Gaudenzi P.

Objective: Coronavirus disease 2019 demonstrated the inconsistencies in adequately responding to biological threats on a global scale due to a lack of powerful tools for assessing various factors in the formation of the epidemic situation and its forecasting. Decision support systems have a role in overcoming the challenges in health monitoring systems in light of current or future epidemic outbreaks. This paper focuses on some applied examples of logistic planning, a key service of the Earth Cognitive System for Coronavirus Disease 2019 project, here presented, evidencing the added value of artificial intelligence algorithms towards predictive hypotheses in tackling health emergencies. Methods: Earth Cognitive System for Coronavirus Disease 2019 is a decision support system designed to support healthcare institutions in monitoring, management and forecasting activities through artificial intelligence, social media analytics, geo- spatial analysis and satellite imaging. The monitoring, management and prediction of medical equipment logistic needs rely on machine learning to predict the regional risk classification colour codes, the emergency rooms attendances, and the fore- cast of regional medical supplies, synergically enhancing geospatial and temporal dimensions. Results: The overall performance of the regional risk colour code classifier yielded a high value of the macro-average F1-score (0.82) and an accuracy of 85%. The prediction of the emergency rooms attendances for the Lazio region yielded a very low root mean square error (<11 patients) and a high positive correlation with the actual values for the major hos- pitals of the Lazio region which admit about 90% of the region’s patients. The prediction of the medicinal purchases for the regions of Lazio and Piemonte has yielded a low root mean squared percentage error of 16%. Conclusions: Accurate forecasting of the evolution of new cases and drug utilisation enables the resulting excess demand throughout the supply chain to be managed more effectively. Forecasting during a pandemic becomes essential for effective government decision-making, managing supply chain resources, and for informing tough policy decisions.
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