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Porcu G, Chen YX, Bonaugurio AS, Villa S, Riva L, Messina V, Bagarella G, Maistrello M, Leoni O, Cereda D, Matone F, Gori A, Corrao G. Web-based surveillance of respiratory infection outbreaks: retrospective analysis of Italian COVID-19 epidemic waves using Google Trends. Front Public Health 2023; 11:1141688. [PMID: 37275497 PMCID: PMC10233021 DOI: 10.3389/fpubh.2023.1141688] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Accepted: 04/28/2023] [Indexed: 06/07/2023] Open
Abstract
Introduction Large-scale diagnostic testing has been proven insufficient to promptly monitor the spread of the Coronavirus disease 2019. Electronic resources may provide better insight into the early detection of epidemics. We aimed to retrospectively explore whether the Google search volume has been useful in detecting Severe Acute Respiratory Syndrome Coronavirus outbreaks early compared to the swab-based surveillance system. Methods The Google Trends website was used by applying the research to three Italian regions (Lombardy, Marche, and Sicily), covering 16 million Italian citizens. An autoregressive-moving-average model was fitted, and residual charts were plotted to detect outliers in weekly searches of five keywords. Signals that occurred during periods labelled as free from epidemics were used to measure Positive Predictive Values and False Negative Rates in anticipating the epidemic wave occurrence. Results Signals from "fever," "cough," and "sore throat" showed better performance than those from "loss of smell" and "loss of taste." More than 80% of true epidemic waves were detected early by the occurrence of at least an outlier signal in Lombardy, although this implies a 20% false alarm signals. Performance was poorer for Sicily and Marche. Conclusion Monitoring the volume of Google searches can be a valuable tool for early detection of respiratory infectious disease outbreaks, particularly in areas with high access to home internet. The inclusion of web-based syndromic keywords is promising as it could facilitate the containment of COVID-19 and perhaps other unknown infectious diseases in the future.
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Affiliation(s)
- Gloria Porcu
- Biostatistics, Epidemiology and Public Health Unit, Department of Statistics and Quantitative Methods, University of Milano-Bicocca, Milan, Italy
- National Centre for Healthcare Research and Pharmacoepidemiology, University of Milano-Bicocca, Milan, Italy
| | - Yu Xi Chen
- Biostatistics, Epidemiology and Public Health Unit, Department of Statistics and Quantitative Methods, University of Milano-Bicocca, Milan, Italy
- Directorate General for Health, Lombardy Region, Milan, Italy
| | - Andrea Stella Bonaugurio
- Biostatistics, Epidemiology and Public Health Unit, Department of Statistics and Quantitative Methods, University of Milano-Bicocca, Milan, Italy
- Directorate General for Health, Lombardy Region, Milan, Italy
| | - Simone Villa
- Centre for Multidisciplinary Research in Health Science, University of Milan, Milan, Italy
| | - Leonardo Riva
- Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy
- PoliS Lombardia, Milan, Italy
| | - Vincenzina Messina
- Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy
- PoliS Lombardia, Milan, Italy
| | - Giorgio Bagarella
- Directorate General for Health, Lombardy Region, Milan, Italy
- Agency for Health Protection of the Metropolitan Area of Milan, Lombardy Region, Milan, Italy
| | - Mauro Maistrello
- Directorate General for Health, Lombardy Region, Milan, Italy
- Local Health Unit of Melegnano and Martesana, Milan, Italy
| | - Olivia Leoni
- Directorate General for Health, Lombardy Region, Milan, Italy
| | - Danilo Cereda
- Directorate General for Health, Lombardy Region, Milan, Italy
| | | | - Andrea Gori
- ASST Fatebenefratelli-Sacco, Luigi Sacco Hospital – University of Milan, Milan, Italy
- Department of Pathophysiology and Transplantation, School of Medicine and Surgery, University of Milan, Milan, Italy
| | - Giovanni Corrao
- Biostatistics, Epidemiology and Public Health Unit, Department of Statistics and Quantitative Methods, University of Milano-Bicocca, Milan, Italy
- National Centre for Healthcare Research and Pharmacoepidemiology, University of Milano-Bicocca, Milan, Italy
- Directorate General for Health, Lombardy Region, Milan, Italy
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Bagarella G, Maistrello M, Minoja M, Leoni O, Bortolan F, Cereda D, Corrao G. Early Detection of SARS-CoV-2 Epidemic Waves: Lessons from the Syndromic Surveillance in Lombardy, Italy. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph191912375. [PMID: 36231672 PMCID: PMC9565943 DOI: 10.3390/ijerph191912375] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 09/23/2022] [Accepted: 09/26/2022] [Indexed: 06/07/2023]
Abstract
We evaluated the performance of the exponentially weighted moving average (EWMA) model for comparing two families of predictors (i.e., structured and unstructured data from visits to the emergency department (ED)) for the early detection of SARS-CoV-2 epidemic waves. The study included data from 1,282,100 ED visits between 1 January 2011 and 9 December 2021 to a local health unit in Lombardy, Italy. A regression model with an autoregressive integrated moving average (ARIMA) error term was fitted. EWMA residual charts were then plotted to detect outliers in the frequency of the daily ED visits made due to the presence of a respiratory syndrome (based on coded diagnoses) or respiratory symptoms (based on free text data). Alarm signals were compared with the number of confirmed SARS-CoV-2 infections. Overall, 150,300 ED visits were encoded as relating to respiratory syndromes and 87,696 to respiratory symptoms. Four strong alarm signals were detected in March and November 2020 and 2021, coinciding with the onset of the pandemic waves. Alarm signals generated for the respiratory symptoms preceded the occurrence of the first and last pandemic waves. We concluded that the EWMA model is a promising tool for predicting pandemic wave onset.
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Affiliation(s)
- Giorgio Bagarella
- Directorate General for Health, Lombardy Region, 20124 Milan, Italy
- Agency for Health Protection of the Metropolitan Area of Milan, Lombardy Region, 20122 Milan, Italy
| | - Mauro Maistrello
- Directorate General for Health, Lombardy Region, 20124 Milan, Italy
- Local Health Unit of Melegnano and Martesana, 20070 Milan, Italy
| | - Maddalena Minoja
- Directorate General for Health, Lombardy Region, 20124 Milan, Italy
| | - Olivia Leoni
- Directorate General for Health, Lombardy Region, 20124 Milan, Italy
| | | | - Danilo Cereda
- Directorate General for Health, Lombardy Region, 20124 Milan, Italy
| | - Giovanni Corrao
- Directorate General for Health, Lombardy Region, 20124 Milan, Italy
- Unit of Biostatistics, Epidemiology and Public Health, Department of Statistics and Quantitative Methods, University of Milano-Bicocca, 20126 Milan, Italy
- National Centre for Healthcare Research and Pharmacoepidemiology, University of Milano-Bicocca, 20126 Milan, Italy
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Malela-Majika JC, Graham MA. Design and implementation of distribution-free Phase-II charting schemes based on unconditional run-length percentiles. COMMUN STAT-THEOR M 2022. [DOI: 10.1080/03610926.2022.2077961] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Affiliation(s)
- Jean-Claude Malela-Majika
- Department of Statistics, Faculty of Natural and Agricultural Sciences, University of Pretoria, Hatfield, South Africa Pretoria
| | - Marien A. Graham
- Department of Science, Mathematics and Technology Education, University of Pretoria, Pretoria, South Africa
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Bruzda G, Rawlins F, Sumpter C, Garner HR. Evaluating disease outbreaks with syndromic surveillance using medical student clinical rotation patient encounter logs. J Osteopath Med 2021; 121:211-220. [PMID: 33567082 DOI: 10.1515/jom-2020-0129] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Context While the data generated by medical students at schools that require electronic patient encounter logs is primarily used to monitor their training progress, it can also be a great source of public health data. Specifically, it can be used for syndromic surveillance, a method used to analyze instantaneous health data for early detection of disease outbreaks. Objective To analyze how the International Classification of Diseases, 10th Revision (ICD-10) codes input by medical students at the Edward Via College of Osteopathic Medicine into the Clinical Rotation Evaluation and Documentation Organizer (CREDO) patient encounter logging system could act as a new syndromic surveillance tool. Methods A CREDO database query was conducted for ICD-10 codes entered between November 1, 2019 and March 13, 2020 using the World Health Organization's 2011 revised case definitions for Influenza Like Illness (ILI). During that period, medical students had an approximated mean of 3,000 patient encounters per day from over 1,500 clinical sites. A cumulative sum technique was applied to the data to generate alert thresholds. Breast cancer, a disease with a stable incidence during the specified timeframe, was used as a control. Results Total ILI daily ICD-10 counts that exceeded alert thresholds represented unusual levels of disease occurred 11 times from November 20, 2020 through February 28, 2020. This analysis is consistent with the COVID-19 pandemic timeline. The first statistically significant ILI increase occurred nine days prior to the first laboratory confirmed case in the country. Conclusion Syndromic surveillance can be timelier than traditional surveillance methods, which require laboratory testing to confirm disease. As a result of this study, we are installing a real-time alert for ILI into CREDO, so rates can be monitored continuously as an indicator of possible future new infectious disease outbreaks.
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Affiliation(s)
- Gabrielle Bruzda
- Edward Via College of Osteopathic Medicine , Blacksburg , VA , USA
| | - Fred Rawlins
- Edward Via College of Osteopathic Medicine , Blacksburg , VA , USA
- Simulation and Technology Center , Edward Via College of Osteopathic Medicine , Blacksburg , VA , USA
| | - Cameron Sumpter
- Edward Via College of Osteopathic Medicine , Blacksburg , VA , USA
- Gibbs Cancer Center and Research Institute , Spartanburg , SC , USA
- Center for Bioinformatics and Genetics , Primary Care Research Network, Edward Via College of Osteopathic Medicine , Blacksburg , VA , USA
| | - Harold R Garner
- Edward Via College of Osteopathic Medicine , Blacksburg , VA , USA
- Gibbs Cancer Center and Research Institute , Spartanburg , SC , USA
- Center for Bioinformatics and Genetics , Primary Care Research Network, Edward Via College of Osteopathic Medicine , Blacksburg , VA , USA
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