The SARS-CoV-2 pandemic has galvanized the interest of the scientific community toward methodologies apt at predicting the trend of the epidemiological curve, namely, the daily number of infected individuals in the population. One of the critical issues, is providing reliable predictions based on interventions enacted by policy-makers, which is of crucial relevance to assess their effectiveness. In this paper, we provide a novel data-driven application incorporating sub-symbolic knowledge to forecast the spreading of an epidemic depending on a set of interventions. More specifically, we focus on the embedding of classical epidemiological approaches, i.e., compartmental models, into Deep Learning models, to enhance the learning process and provide higher predictive accuracy.

Informed Deep Learning for Epidemics Forecasting / Federico Baldo, Michele Iannello, Michele Lombardi, Michela Milano. - ELETTRONICO. - 351:(2022), pp. 86-99. (Intervento presentato al convegno 11th Conference on Prestigious Applications of Artificial Intelligence, PAIS 2022, co-located with the 31st International Joint Conference on Artificial Intelligence and the 25th European Conference on Artificial Intelligence, IJCAI-ECAI 2022 tenutosi a aut nel 2022) [10.3233/FAIA220067].

Informed Deep Learning for Epidemics Forecasting

Federico Baldo
Primo
Software
;
Michele Iannello
Secondo
Software
;
Michele Lombardi
Penultimo
Methodology
;
Michela Milano
Ultimo
Conceptualization
2022

Abstract

The SARS-CoV-2 pandemic has galvanized the interest of the scientific community toward methodologies apt at predicting the trend of the epidemiological curve, namely, the daily number of infected individuals in the population. One of the critical issues, is providing reliable predictions based on interventions enacted by policy-makers, which is of crucial relevance to assess their effectiveness. In this paper, we provide a novel data-driven application incorporating sub-symbolic knowledge to forecast the spreading of an epidemic depending on a set of interventions. More specifically, we focus on the embedding of classical epidemiological approaches, i.e., compartmental models, into Deep Learning models, to enhance the learning process and provide higher predictive accuracy.
2022
11th Conference on Prestigious Applications of Artificial Intelligence, PAIS 2022
86
99
Informed Deep Learning for Epidemics Forecasting / Federico Baldo, Michele Iannello, Michele Lombardi, Michela Milano. - ELETTRONICO. - 351:(2022), pp. 86-99. (Intervento presentato al convegno 11th Conference on Prestigious Applications of Artificial Intelligence, PAIS 2022, co-located with the 31st International Joint Conference on Artificial Intelligence and the 25th European Conference on Artificial Intelligence, IJCAI-ECAI 2022 tenutosi a aut nel 2022) [10.3233/FAIA220067].
Federico Baldo, Michele Iannello, Michele Lombardi, Michela Milano
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/907947
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