The history of the Italian CoViD-19 epidemic began on 2020, February the 20th, in Lombardy region, which quickly became the most stricken geographical area of the world. This first outbreak caught national health system unprepared, and hospitals experienced patients overload, facing an unknown infectious disease. Thus, it is of primary importance to provide public health services with tools which can help to potentially prevent health system stress periods. To this aim, we performed a time-frequency analysis of regional emergency calls, CoViD-19-related Twitter data and daily new cases through wavelets, and a comparison of the signals in the time domain using cross-correlation. Our findings show that emergency calls could be a good predictor of health service burdens, while Twitter activity is more related to personal and emotional involvement in the emergency and to socio-political dynamics. Social media should therefore be used to improve institutional communication in order to prevent “infodemia".

Could Emergency Calls and Twitter Activity Help to Prevent Health System Overloads Due to CoViD-19 Epidemic? Wavelets and Cross- Correlation as Useful Tools for Time-Frequency Signal Analysis: Lessons from Italian Lombardy Region / B. Alessandro Rivieccio, A. Micheletti, M. Maffeo, M. Zignani, A. Comunian, F. Nicolussi, S. Salini, G. Manzi, F. Auxilia, M. Giudici, G. Naldi, S.T. Gaito, S. Castaldi, E. Biganzoli - In: Proceedings of the COVid-19 Empirical Research (COVER) / [a cura di] E. Biganzoli, Gi. Manzi, A. Micheletti, F. Nicolussi, S. Salini. - [s.l] : Milano University Press, 2022. - ISBN 9791280325457. - pp. 153-162 (( convegno COVid-19 Empirical Research [COVER] tenutosi a Milano nel 2020 [10.54103/milanoup.73.65].

Could Emergency Calls and Twitter Activity Help to Prevent Health System Overloads Due to CoViD-19 Epidemic? Wavelets and Cross- Correlation as Useful Tools for Time-Frequency Signal Analysis: Lessons from Italian Lombardy Region

Alessandra Micheletti;Manuel Maffeo;Matteo Zignani;Alessandro Comunian;Federica Nicolussi;Silvia Salini;Giancarlo Manzi;Francesco Auxilia;Mauro Giudici;Giovanni Naldi;Sabrina Gaito;Silvana Castaldi;Elia Biganzoli
2022

Abstract

The history of the Italian CoViD-19 epidemic began on 2020, February the 20th, in Lombardy region, which quickly became the most stricken geographical area of the world. This first outbreak caught national health system unprepared, and hospitals experienced patients overload, facing an unknown infectious disease. Thus, it is of primary importance to provide public health services with tools which can help to potentially prevent health system stress periods. To this aim, we performed a time-frequency analysis of regional emergency calls, CoViD-19-related Twitter data and daily new cases through wavelets, and a comparison of the signals in the time domain using cross-correlation. Our findings show that emergency calls could be a good predictor of health service burdens, while Twitter activity is more related to personal and emotional involvement in the emergency and to socio-political dynamics. Social media should therefore be used to improve institutional communication in order to prevent “infodemia".
Settore MED/01 - Statistica Medica
Settore INF/01 - Informatica
Settore MAT/06 - Probabilita' e Statistica Matematica
Settore SECS-S/01 - Statistica
Settore MED/42 - Igiene Generale e Applicata
Settore GEO/12 - Oceanografia e Fisica dell'Atmosfera
Settore MAT/08 - Analisi Numerica
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2434/905466
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