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Emergency Medical Services Calls Analysis for Trend Prediction during Epidemic Outbreaks: Interrupted Time Series Analysis on 2020–2021 COVID-19 Epidemic in Lazio, Italy

Author

Listed:
  • Antonio Vinci

    (Local Health Authority “Roma 1”, 00193 Rome, Italy)

  • Amina Pasquarella

    (Azienda Regionale Emergenza Sanitaria ARES 118, 00149 Rome, Italy)

  • Maria Paola Corradi

    (Azienda Regionale Emergenza Sanitaria ARES 118, 00149 Rome, Italy)

  • Pelagia Chatzichristou

    (Azienda Regionale Emergenza Sanitaria ARES 118, 00149 Rome, Italy
    Department of Biomedicine and Prevention, Post-Graduate School of Specialization in Hygiene and Preventive Medicine, University of Rome “Tor Vergata”, 00166 Rome, Italy)

  • Gianluca D’Agostino

    (Azienda Regionale Emergenza Sanitaria ARES 118, 00149 Rome, Italy)

  • Stefania Iannazzo

    (Azienda Regionale Emergenza Sanitaria ARES 118, 00149 Rome, Italy)

  • Nicoletta Trani

    (Azienda Regionale Emergenza Sanitaria ARES 118, 00149 Rome, Italy)

  • Maria Annunziata Parafati

    (Azienda Regionale Emergenza Sanitaria ARES 118, 00149 Rome, Italy)

  • Leonardo Palombi

    (Department of Biomedicine and Prevention, University of Rome “Tor Vergata”, 00166 Rome, Italy)

  • Domenico Antonio Ientile

    (Azienda Regionale Emergenza Sanitaria ARES 118, 00149 Rome, Italy)

Abstract

(1) Background: During the COVID-19 outbreak in the Lazio region, a surge in emergency medical service (EMS) calls has been observed. The objective of present study is to investigate if there is any correlation between the variation in numbers of daily EMS calls, and the short-term evolution of the epidemic wave. (2) Methods: Data from the COVID-19 outbreak has been retrieved in order to draw the epidemic curve in the Lazio region. Data from EMS calls has been used in order to determine Excess of Calls (ExCa) in the 2020–2021 years, compared to the year 2019 (baseline). Multiple linear regression models have been run between ExCa and the first-order derivative (D’) of the epidemic wave in time, each regression model anticipating the epidemic progression (up to 14 days), in order to probe a correlation between the variables. (3) Results: EMS calls variation from baseline is correlated with the slope of the curve of ICU admissions, with the most fitting value found at 7 days (R 2 0.33, p < 0.001). (4) Conclusions: EMS calls deviation from baseline allows public health services to predict short-term epidemic trends in COVID-19 outbreaks, and can be used as validation of current data, or as an independent estimator of future trends.

Suggested Citation

  • Antonio Vinci & Amina Pasquarella & Maria Paola Corradi & Pelagia Chatzichristou & Gianluca D’Agostino & Stefania Iannazzo & Nicoletta Trani & Maria Annunziata Parafati & Leonardo Palombi & Domenico A, 2022. "Emergency Medical Services Calls Analysis for Trend Prediction during Epidemic Outbreaks: Interrupted Time Series Analysis on 2020–2021 COVID-19 Epidemic in Lazio, Italy," IJERPH, MDPI, vol. 19(10), pages 1-15, May.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:10:p:5951-:d:815211
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    References listed on IDEAS

    as
    1. Bruno Alessandro Rivieccio & Alessandra Micheletti & Manuel Maffeo & Matteo Zignani & Alessandro Comunian & Federica Nicolussi & Silvia Salini & Giancarlo Manzi & Francesco Auxilia & Mauro Giudici & G, 2021. "CoViD-19, learning from the past: A wavelet and cross-correlation analysis of the epidemic dynamics looking to emergency calls and Twitter trends in Italian Lombardy region," PLOS ONE, Public Library of Science, vol. 16(2), pages 1-20, February.
    2. Filomena Pietrantonio & Francesco Rosiello & Elena Alessi & Matteo Pascucci & Marianna Rainone & Enrica Cipriano & Alessandra Di Berardino & Antonio Vinci & Matteo Ruggeri & Serafino Ricci, 2021. "Burden of COVID-19 on Italian Internal Medicine Wards: Delphi, SWOT, and Performance Analysis after Two Pandemic Waves in the Local Health Authority “Roma 6” Hospital Structures," IJERPH, MDPI, vol. 18(11), pages 1-11, June.
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