A new measles epidemic model is proposed and identified by using real data relative to the number of confirmed infected patients in Italy in the period 1970-2018. The possibility of predicting the number of new infection is important for an efficient resource scheduling. Only in the last years great attention has been devoted to reliable data collection; therefore, in general, the model parameters identification is not an easy task. Moreover, the available data are 'corrupted' by human intervention, such as prevention campaign, or, whenever possible, vaccination. In this paper, the measles model parameters are identified referring to the data of the period in which there wasn't a significant vaccination coverage; successively, the vaccination action has been identified. The results obtained appear encouraging, confirming the importance of available consistent data.

A new measles epidemic model: Analysis, identification and prediction / Di Giamberardino, P.; Iacoviello, D.. - (2020), pp. 484-489. (Intervento presentato al convegno 28th Mediterranean Conference on Control and Automation, MED 2020 tenutosi a Electr Network) [10.1109/MED48518.2020.9182861].

A new measles epidemic model: Analysis, identification and prediction

Di Giamberardino P.
;
Iacoviello D.
2020

Abstract

A new measles epidemic model is proposed and identified by using real data relative to the number of confirmed infected patients in Italy in the period 1970-2018. The possibility of predicting the number of new infection is important for an efficient resource scheduling. Only in the last years great attention has been devoted to reliable data collection; therefore, in general, the model parameters identification is not an easy task. Moreover, the available data are 'corrupted' by human intervention, such as prevention campaign, or, whenever possible, vaccination. In this paper, the measles model parameters are identified referring to the data of the period in which there wasn't a significant vaccination coverage; successively, the vaccination action has been identified. The results obtained appear encouraging, confirming the importance of available consistent data.
2020
28th Mediterranean Conference on Control and Automation, MED 2020
epidemic modelling; system identification; measles
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
A new measles epidemic model: Analysis, identification and prediction / Di Giamberardino, P.; Iacoviello, D.. - (2020), pp. 484-489. (Intervento presentato al convegno 28th Mediterranean Conference on Control and Automation, MED 2020 tenutosi a Electr Network) [10.1109/MED48518.2020.9182861].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1452862
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