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 / Alessandro Rivieccio, Bruno; Micheletti, Alessandra; Maffeo, Manuel; Zignani, Matteo; Comunian, Alessandro; Nicolussi, Federica; Salini, Silvia; Manzi, Giancarlo; Auxilia, Francesco; Giudici, Mauro; Naldi, Giovanni; Gaito, Sabrina; Castaldi, Silvana; Biganzoli, Elia. - (2022), pp. 153-162. (Intervento presentato al convegno COVid-19 Empirical Research [COVER] tenutosi a Milano) [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
Silvia Salini;Giancarlo Manzi;Giovanni Naldi;
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".I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
