Does syndromic surveillance assist public health practice in early detecting respiratory epidemics? Evidence from a wide Italian retrospective experience
Background: Large-scale diagnostic testing has been proven ineffective for prompt monitoring of the spread of COVID-19. Electronic resources may facilitate enhanced early detection of epidemics. Here, we aimed to retrospectively explore whether examining trends in the use of emergency and healthcare...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-02-01
|
| Series: | Journal of Infection and Public Health |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1876034124003551 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832592826539442176 |
|---|---|
| author | Giovanni Corrao Andrea Stella Bonaugurio Giorgio Bagarella Mauro Maistrello Olivia Leoni Danilo Cereda Andrea Gori |
| author_facet | Giovanni Corrao Andrea Stella Bonaugurio Giorgio Bagarella Mauro Maistrello Olivia Leoni Danilo Cereda Andrea Gori |
| author_sort | Giovanni Corrao |
| collection | DOAJ |
| description | Background: Large-scale diagnostic testing has been proven ineffective for prompt monitoring of the spread of COVID-19. Electronic resources may facilitate enhanced early detection of epidemics. Here, we aimed to retrospectively explore whether examining trends in the use of emergency and healthcare services and the Google search engine is useful in detecting Severe Acute Respiratory Syndrome Coronavirus outbreaks early compared with the currently used swab-based surveillance system. Methods: Healthcare Utilization databases of the Italian region of Lombardy and the Google Trends website were used to measure the weekly utilization of emergency and healthcare services and determining the volume of Google searches from 2020 to 2022. Improved Farrington algorithm (IMPF) and Exponentially Weighted Moving Average (EWMA) control chart were both fitted to detect outliers in weekly searches of nine syndromic tracers. AND/OR Boolean operators were tested aimed for joint using tracers and models. Signals that occurred during periods labelled as free from epidemics were used to measure positive predictive values (PPV) and false negative values (FNV) in anticipating the epidemic wave. Results: Out of the 156 weeks of interest, 70 (45 %) were affected by epidemic waves. Overall, 54 syndromic signals were obtained from any one of the 7 healthcare or Google tracers, generating an outlier from both the EWMA and IMPF models. PPV values of 0.95, 1.00, 0.96 admitting a delay of 0, 1, and 2 weeks, respectively, between signal and epidemic wave. The values of FNP ranged from 0.19 to 0.21. Conclusions: High predictive power for anticipating COVID-19 epidemic waves, even two weeks ahead of the official reports, was obtained from electronic syndromic tracers of healthcare-seeking trends and Google search engine use. Following verification via a prospective approach, public health organizations are encouraged to take advantage of this free forecasting system to anticipate and effectively manage respiratory outbreaks. |
| format | Article |
| id | doaj-art-f188d176da0d4c33aa4b8510e0b92394 |
| institution | Kabale University |
| issn | 1876-0341 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Infection and Public Health |
| spelling | doaj-art-f188d176da0d4c33aa4b8510e0b923942025-01-21T04:12:55ZengElsevierJournal of Infection and Public Health1876-03412025-02-01182102621Does syndromic surveillance assist public health practice in early detecting respiratory epidemics? Evidence from a wide Italian retrospective experienceGiovanni Corrao0Andrea Stella Bonaugurio1Giorgio Bagarella2Mauro Maistrello3Olivia Leoni4Danilo Cereda5Andrea Gori6University of Milano-Bicocca, Milan, Italy; National Centre for Healthcare Research and Pharmacoepidemiology, University of Milan-Bicocca, Milan, Italy; Welfare Department, Operative Centre for Health Data, Lombardy Region, Milan, ItalyNational Centre for Healthcare Research and Pharmacoepidemiology, University of Milan-Bicocca, Milan, Italy; Biostatistics, Epidemiology and Public Health Unit, Department of Statistics and Quantitative Methods, University of Milano-Bicocca, Milan, Italy; Correspondence to: Dipartimento di Statistica e Metodi Quantitativi, Università degli Studi di Milano-Bicocca, Via Bicocca degli Arcimboldi, 8, Edificio U7, Milano 20126, Italy.Welfare Department, Operative Centre for Health Data, Lombardy Region, Milan, Italy; Agency for Health Protection of the Metropolitan Area of Milan, Lombardy Region, Milan, ItalyWelfare Department, Operative Centre for Health Data, Lombardy Region, Milan, Italy; Local Health Unit of Melegnano and Martesana, Milan, ItalyWelfare Department, Operative Centre for Health Data, Lombardy Region, Milan, ItalyWelfare Department, Operative Centre for Health Data, Lombardy Region, Milan, ItalyFirst Division of Infectious Diseases, ASST Fatebenefratelli Sacco, Luigi Sacco Hospital, Milan, Italy; Centre for Multidisciplinary Research in Health Science (MACH), University of Milan, ItalyBackground: Large-scale diagnostic testing has been proven ineffective for prompt monitoring of the spread of COVID-19. Electronic resources may facilitate enhanced early detection of epidemics. Here, we aimed to retrospectively explore whether examining trends in the use of emergency and healthcare services and the Google search engine is useful in detecting Severe Acute Respiratory Syndrome Coronavirus outbreaks early compared with the currently used swab-based surveillance system. Methods: Healthcare Utilization databases of the Italian region of Lombardy and the Google Trends website were used to measure the weekly utilization of emergency and healthcare services and determining the volume of Google searches from 2020 to 2022. Improved Farrington algorithm (IMPF) and Exponentially Weighted Moving Average (EWMA) control chart were both fitted to detect outliers in weekly searches of nine syndromic tracers. AND/OR Boolean operators were tested aimed for joint using tracers and models. Signals that occurred during periods labelled as free from epidemics were used to measure positive predictive values (PPV) and false negative values (FNV) in anticipating the epidemic wave. Results: Out of the 156 weeks of interest, 70 (45 %) were affected by epidemic waves. Overall, 54 syndromic signals were obtained from any one of the 7 healthcare or Google tracers, generating an outlier from both the EWMA and IMPF models. PPV values of 0.95, 1.00, 0.96 admitting a delay of 0, 1, and 2 weeks, respectively, between signal and epidemic wave. The values of FNP ranged from 0.19 to 0.21. Conclusions: High predictive power for anticipating COVID-19 epidemic waves, even two weeks ahead of the official reports, was obtained from electronic syndromic tracers of healthcare-seeking trends and Google search engine use. Following verification via a prospective approach, public health organizations are encouraged to take advantage of this free forecasting system to anticipate and effectively manage respiratory outbreaks.http://www.sciencedirect.com/science/article/pii/S1876034124003551COVID-19Exponentially weighted moving average control chartGoogle trendsHealthcare utilization dataImproved farrington algorithmSyndromic surveillance |
| spellingShingle | Giovanni Corrao Andrea Stella Bonaugurio Giorgio Bagarella Mauro Maistrello Olivia Leoni Danilo Cereda Andrea Gori Does syndromic surveillance assist public health practice in early detecting respiratory epidemics? Evidence from a wide Italian retrospective experience Journal of Infection and Public Health COVID-19 Exponentially weighted moving average control chart Google trends Healthcare utilization data Improved farrington algorithm Syndromic surveillance |
| title | Does syndromic surveillance assist public health practice in early detecting respiratory epidemics? Evidence from a wide Italian retrospective experience |
| title_full | Does syndromic surveillance assist public health practice in early detecting respiratory epidemics? Evidence from a wide Italian retrospective experience |
| title_fullStr | Does syndromic surveillance assist public health practice in early detecting respiratory epidemics? Evidence from a wide Italian retrospective experience |
| title_full_unstemmed | Does syndromic surveillance assist public health practice in early detecting respiratory epidemics? Evidence from a wide Italian retrospective experience |
| title_short | Does syndromic surveillance assist public health practice in early detecting respiratory epidemics? Evidence from a wide Italian retrospective experience |
| title_sort | does syndromic surveillance assist public health practice in early detecting respiratory epidemics evidence from a wide italian retrospective experience |
| topic | COVID-19 Exponentially weighted moving average control chart Google trends Healthcare utilization data Improved farrington algorithm Syndromic surveillance |
| url | http://www.sciencedirect.com/science/article/pii/S1876034124003551 |
| work_keys_str_mv | AT giovannicorrao doessyndromicsurveillanceassistpublichealthpracticeinearlydetectingrespiratoryepidemicsevidencefromawideitalianretrospectiveexperience AT andreastellabonaugurio doessyndromicsurveillanceassistpublichealthpracticeinearlydetectingrespiratoryepidemicsevidencefromawideitalianretrospectiveexperience AT giorgiobagarella doessyndromicsurveillanceassistpublichealthpracticeinearlydetectingrespiratoryepidemicsevidencefromawideitalianretrospectiveexperience AT mauromaistrello doessyndromicsurveillanceassistpublichealthpracticeinearlydetectingrespiratoryepidemicsevidencefromawideitalianretrospectiveexperience AT olivialeoni doessyndromicsurveillanceassistpublichealthpracticeinearlydetectingrespiratoryepidemicsevidencefromawideitalianretrospectiveexperience AT danilocereda doessyndromicsurveillanceassistpublichealthpracticeinearlydetectingrespiratoryepidemicsevidencefromawideitalianretrospectiveexperience AT andreagori doessyndromicsurveillanceassistpublichealthpracticeinearlydetectingrespiratoryepidemicsevidencefromawideitalianretrospectiveexperience |