Data from Emergency Medical Service Activities: A Novel Approach to Monitoring COVID-19 and Other Infectious Diseases

被引:1
作者
del Re, Daniele [1 ]
Palla, Luigi [2 ]
Meridiani, Paolo [3 ]
Soffi, Livia [3 ]
Loiudice, Michele Tancredi [4 ]
Antinozzi, Martina [2 ]
Cattaruzza, Maria Sofia [2 ]
机构
[1] Sapienza Univ Rome, Dept Phys, I-00185 Rome, Italy
[2] Sapienza Univ Rome, Dept Publ Hlth & Infect Dis, I-00185 Rome, Italy
[3] INFN Ist Nazl Fis Nucl, Sez Roma, I-00146 Rome, Italy
[4] Sapienza Univ Rome, Fac Med & Psychol, Dept Dev & Social Psychol, I-00185 Rome, Italy
关键词
COVID-19; emergency medical services; pandemic; excess mortality estimation; infectious diseases; CALLS; MORTALITY;
D O I
10.3390/diagnostics15020181
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Background: Italy, particularly the northern region of Lombardy, has experienced very high rates of COVID-19 cases and deaths. Several indicators, i.e., the number of new positive cases, deaths and hospitalizations, have been used to monitor virus spread, but all suffer from biases. The aim of this study was to evaluate an alternative data source from Emergency Medical Service (EMS) activities for COVID-19 monitoring. Methods: Calls to the emergency number (112) in Lombardy (years 2015-2022) were studied and their overlap with the COVID-19 pandemic, influenza and official mortality peaks were evaluated. Modeling it as a counting process, a specific cause contribution (i.e., COVID-19 symptoms, the "signal") was identified and enucleated from all other contributions (the "background"), and the latter was subtracted from the total observed number of calls using statistical methods for excess event estimation. Results: A total of 6,094,502 records were analyzed and filtered for respiratory and cardiological symptoms to identify potential COVID-19 patients, yielding 742,852 relevant records. Results show that EMS data mirrored the time series of cases or deaths in Lombardy, with good agreement also being found with seasonal flu outbreaks. Conclusions: This novel approach, combined with a machine learning predictive approach, could be a powerful public health tool to signal the start of disease outbreaks and monitor the spread of infectious diseases.
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页数:14
相关论文
共 43 条
[1]   Covid-19: Open-Data Resources for Monitoring, Modeling, and Forecasting the Epidemic [J].
Alamo, Teodoro ;
Reina, Daniel G. ;
Mammarella, Martina ;
Abella, Alberto .
ELECTRONICS, 2020, 9 (05)
[2]  
[Anonymous], 2020, Sorveglianza Integrata COVID-19 in Italia
[3]  
[Anonymous], 2023, WHO Director-Generals opening remarks at the media briefing - 5 May 2023
[4]  
Areu Lombardia-Soreu, About Us
[5]  
Bezzini Daiana, 2021, Adv Exp Med Biol, V1353, P91, DOI 10.1007/978-3-030-85113-2_6
[6]   Comparison of surveillance systems for monitoring COVID-19 in England: a retrospective observational study [J].
Brainard, Julii ;
Lake, Iain R. ;
Morbey, Roger A. ;
Jones, Natalia R. ;
Elliot, Alex J. ;
Hunter, Paul R. .
LANCET PUBLIC HEALTH, 2023, 8 (11) :E850-E858
[7]   ROOT - An object oriented data analysis framework [J].
Brun, R ;
Rademakers, F .
NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT, 1997, 389 (1-2) :81-86
[8]   Estimating COVID-19 mortality in Italy early in the COVID-19 pandemic [J].
Buchmann, B. ;
Engelbrecht, L. K. ;
Fernandez, P. ;
Hutterer, F. P. ;
Raich, M. K. ;
Scheel, C. H. ;
Bausch, A. R. .
NATURE COMMUNICATIONS, 2021, 12 (01)
[9]  
Delgado RC, 2021, EMERGENCIAS, V33, P368
[10]  
Ceccarelli Emiliano, 2023, Int J Environ Res Public Health, V20, DOI 10.3390/ijerph20042812