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Al Hossain F, Tonmoy MTH, Nuvvula S, Chapman BP, Gupta RK, Lover AA, Dinglasan RR, Carreiro S, Rahman T. Syndromic surveillance of population-level COVID-19 burden with cough monitoring in a hospital emergency waiting room. Front Public Health 2024; 12:1279392. [PMID: 38605877 PMCID: PMC11007176 DOI: 10.3389/fpubh.2024.1279392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 03/11/2024] [Indexed: 04/13/2024] Open
Abstract
Syndromic surveillance is an effective tool for enabling the timely detection of infectious disease outbreaks and facilitating the implementation of effective mitigation strategies by public health authorities. While various information sources are currently utilized to collect syndromic signal data for analysis, the aggregated measurement of cough, an important symptom for many illnesses, is not widely employed as a syndromic signal. With recent advancements in ubiquitous sensing technologies, it becomes feasible to continuously measure population-level cough incidence in a contactless, unobtrusive, and automated manner. In this work, we demonstrate the utility of monitoring aggregated cough count as a syndromic indicator to estimate COVID-19 cases. In our study, we deployed a sensor-based platform (Syndromic Logger) in the emergency room of a large hospital. The platform captured syndromic signals from audio, thermal imaging, and radar, while the ground truth data were collected from the hospital's electronic health record. Our analysis revealed a significant correlation between the aggregated cough count and positive COVID-19 cases in the hospital (Pearson correlation of 0.40, p-value < 0.001). Notably, this correlation was higher than that observed with the number of individuals presenting with fever (ρ = 0.22, p = 0.04), a widely used syndromic signal and screening tool for such diseases. Furthermore, we demonstrate how the data obtained from our Syndromic Logger platform could be leveraged to estimate various COVID-19-related statistics using multiple modeling approaches. Aggregated cough counts and other data, such as people density collected from our platform, can be utilized to predict COVID-19 patient visits related metrics in a hospital waiting room, and SHAP and Gini feature importance-based metrics showed cough count as the important feature for these prediction models. Furthermore, we have shown that predictions based on cough counting outperform models based on fever detection (e.g., temperatures over 39°C), which require more intrusive engagement with the population. Our findings highlight that incorporating cough-counting based signals into syndromic surveillance systems can significantly enhance overall resilience against future public health challenges, such as emerging disease outbreaks or pandemics.
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Affiliation(s)
- Forsad Al Hossain
- Manning College of Information and Computer Sciences, University of Massachusetts-Amherst, Amherst, MA, United States
| | - M. Tanjid Hasan Tonmoy
- Halıcıoǧlu Data Science Institute, University of California, San Diego, San Diego, CA, United States
| | - Sri Nuvvula
- Department of Emergency Medicine, UMass Chan Medical School, Worcester, MA, United States
| | - Brittany P. Chapman
- Department of Emergency Medicine, UMass Chan Medical School, Worcester, MA, United States
| | - Rajesh K. Gupta
- Halıcıoǧlu Data Science Institute, University of California, San Diego, San Diego, CA, United States
| | - Andrew A. Lover
- School of Public Health & Health Sciences, University of Massachusetts Amherst, Amherst, MA, United States
| | - Rhoel R. Dinglasan
- Infectious Diseases and Immunology, University of Florida, Gainesville, FL, United States
| | - Stephanie Carreiro
- Department of Emergency Medicine, UMass Chan Medical School, Worcester, MA, United States
| | - Tauhidur Rahman
- Halıcıoǧlu Data Science Institute, University of California, San Diego, San Diego, CA, United States
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Clark EC, Neumann S, Hopkins S, Kostopoulos A, Hagerman L, Dobbins M. Changes to Public Health Surveillance Methods Due to the COVID-19 Pandemic: Scoping Review. JMIR Public Health Surveill 2024; 10:e49185. [PMID: 38241067 PMCID: PMC10837764 DOI: 10.2196/49185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 09/06/2023] [Accepted: 12/07/2023] [Indexed: 01/23/2024] Open
Abstract
BACKGROUND Public health surveillance plays a vital role in informing public health decision-making. The onset of the COVID-19 pandemic in early 2020 caused a widespread shift in public health priorities. Global efforts focused on COVID-19 monitoring and contact tracing. Existing public health programs were interrupted due to physical distancing measures and reallocation of resources. The onset of the COVID-19 pandemic intersected with advancements in technologies that have the potential to support public health surveillance efforts. OBJECTIVE This scoping review aims to explore emergent public health surveillance methods during the early COVID-19 pandemic to characterize the impact of the pandemic on surveillance methods. METHODS A scoping search was conducted in multiple databases and by scanning key government and public health organization websites from March 2020 to January 2022. Published papers and gray literature that described the application of new or revised approaches to public health surveillance were included. Papers that discussed the implications of novel public health surveillance approaches from ethical, legal, security, and equity perspectives were also included. The surveillance subject, method, location, and setting were extracted from each paper to identify trends in surveillance practices. Two public health epidemiologists were invited to provide their perspectives as peer reviewers. RESULTS Of the 14,238 unique papers, a total of 241 papers describing novel surveillance methods and changes to surveillance methods are included. Eighty papers were review papers and 161 were single studies. Overall, the literature heavily featured papers detailing surveillance of COVID-19 transmission (n=187). Surveillance of other infectious diseases was also described, including other pathogens (n=12). Other public health topics included vaccines (n=9), mental health (n=11), substance use (n=4), healthy nutrition (n=1), maternal and child health (n=3), antimicrobial resistance (n=2), and misinformation (n=6). The literature was dominated by applications of digital surveillance, for example, by using big data through mobility tracking and infodemiology (n=163). Wastewater surveillance was also heavily represented (n=48). Other papers described adaptations to programs or methods that existed prior to the COVID-19 pandemic (n=9). The scoping search also found 109 papers that discuss the ethical, legal, security, and equity implications of emerging surveillance methods. The peer reviewer public health epidemiologists noted that additional changes likely exist, beyond what has been reported and available for evidence syntheses. CONCLUSIONS The COVID-19 pandemic accelerated advancements in surveillance and the adoption of new technologies, especially for digital and wastewater surveillance methods. Given the investments in these systems, further applications for public health surveillance are likely. The literature for surveillance methods was dominated by surveillance of infectious diseases, particularly COVID-19. A substantial amount of literature on the ethical, legal, security, and equity implications of these emerging surveillance methods also points to a need for cautious consideration of potential harm.
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Affiliation(s)
- Emily C Clark
- National Collaborating Centre for Methods and Tools, Hamilton, ON, Canada
| | - Sophie Neumann
- National Collaborating Centre for Methods and Tools, Hamilton, ON, Canada
| | - Stephanie Hopkins
- National Collaborating Centre for Methods and Tools, Hamilton, ON, Canada
| | - Alyssa Kostopoulos
- National Collaborating Centre for Methods and Tools, Hamilton, ON, Canada
| | - Leah Hagerman
- National Collaborating Centre for Methods and Tools, Hamilton, ON, Canada
| | - Maureen Dobbins
- National Collaborating Centre for Methods and Tools, Hamilton, ON, Canada
- School of Nursing, McMaster University, Hamilton, ON, Canada
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Al Hossain F, Tonmoy TH, Nuvvula S, Chapman BP, Gupta RK, Lover AA, Dinglasan RR, Carreiro S, Rahman T. Passive Monitoring of Crowd-Level Cough Counts in Waiting Areas produces Reliable Syndromic Indicator for Total COVID-19 Burden in a Hospital Emergency Clinic. RESEARCH SQUARE 2023:rs.3.rs-3084318. [PMID: 37461489 PMCID: PMC10350162 DOI: 10.21203/rs.3.rs-3084318/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/27/2023]
Abstract
Syndromic surveillance is an effective tool for enabling the timely detection of infectious disease outbreaks and facilitating the implementation of effective mitigation strategies by public health authorities. While various information sources are currently utilized to collect syndromic signal data for analysis, the aggregated measurement of cough, an important symptom for many illnesses, is not widely employed as a syndromic signal. With recent advancements in ubiquitous sensing technologies, it becomes feasible to continuously measure population-level cough incidence in a contactless, unobtrusive, and automated manner. In this work, we demonstrate the utility of monitoring aggregated cough count as a syndromic indicator to estimate COVID-19 cases. In our study, we deployed a sensor-based platform (Syndromic Logger) in the emergency room of a large hospital. The platform captured syndromic signals from audio, thermal imaging, and radar, while the ground truth data were collected from the hospital's electronic health record. Our analysis revealed a significant correlation between the aggregated cough count and positive COVID-19 cases in the hospital (Pearson correlation of 0.40, p-value < 0.001). Notably, this correlation was higher than that observed with the number of individuals presenting with fever (ρ = 0.22, p = 0.04), a widely used syndromic signal and screening tool for such diseases. Furthermore, we demonstrate how the data obtained from our Syndromic Logger platform could be leveraged to estimate various COVID-19-related statistics using multiple modeling approaches. Our findings highlight the efficacy of aggregated cough count as a valuable syndromic indicator associated with the occurrence of COVID-19 cases. Incorporating this signal into syndromic surveillance systems for such diseases can significantly enhance overall resilience against future public health challenges, such as emerging disease outbreaks or pandemics.
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Affiliation(s)
- Forsad Al Hossain
- College of Information and Computer Sciences, University of Massachusetts Amherst, Amherst, MA, USA
| | - Tanjid Hasan Tonmoy
- Halıcıoğlu Data Science Institute, University of California, San Diego, San Diego, CA, USA
| | - Sri Nuvvula
- Department of Emergency Medicine, UMass Chan Medical School, Worcester, MA, USA
| | - Brittany P. Chapman
- Department of Emergency Medicine, UMass Chan Medical School, Worcester, MA, USA
| | - Rajesh K. Gupta
- Halıcıoğlu Data Science Institute, University of California, San Diego, San Diego, CA, USA
| | - Andrew A. Lover
- School of Public Health & Health Sciences, University of Massachusetts Amherst, Amherst, MA, USA
| | - Rhoel R. Dinglasan
- Infectious Diseases and Immunology, University of Florida, Gainesville, FL, USA
| | - Stephanie Carreiro
- Department of Emergency Medicine, UMass Chan Medical School, Worcester, MA, USA
| | - Tauhidur Rahman
- Halıcıoğlu Data Science Institute, University of California, San Diego, San Diego, CA, USA
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Merlo I, Crea M, Berta P, Ieva F, Carle F, Rea F, Porcu G, Savaré L, De Maio R, Villa M, Cereda D, Leoni O, Bortolan F, Sechi GM, Bella A, Pezzotti P, Brusaferro S, Blangiardo GC, Fedeli M, Corrao G. Detecting early signals of COVID-19 outbreaks in 2020 in small areas by monitoring healthcare utilisation databases: first lessons learned from the Italian Alert_CoV project. Euro Surveill 2023; 28:2200366. [PMID: 36695448 PMCID: PMC9817206 DOI: 10.2807/1560-7917.es.2023.28.1.2200366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 10/02/2022] [Indexed: 01/07/2023] Open
Abstract
BackgroundDuring the COVID-19 pandemic, large-scale diagnostic testing and contact tracing have proven insufficient to promptly monitor the spread of infections.AimTo develop and retrospectively evaluate a system identifying aberrations in the use of selected healthcare services to timely detect COVID-19 outbreaks in small areas.MethodsData were retrieved from the healthcare utilisation (HCU) databases of the Lombardy Region, Italy. We identified eight services suggesting a respiratory infection (syndromic proxies). Count time series reporting the weekly occurrence of each proxy from 2015 to 2020 were generated considering small administrative areas (i.e. census units of Cremona and Mantua provinces). The ability to uncover aberrations during 2020 was tested for two algorithms: the improved Farrington algorithm and the generalised likelihood ratio-based procedure for negative binomial counts. To evaluate these algorithms' performance in detecting outbreaks earlier than the standard surveillance, confirmed outbreaks, defined according to the weekly number of confirmed COVID-19 cases, were used as reference. Performances were assessed separately for the first and second semester of the year. Proxies positively impacting performance were identified.ResultsWe estimated that 70% of outbreaks could be detected early using the proposed approach, with a corresponding false positive rate of ca 20%. Performance did not substantially differ either between algorithms or semesters. The best proxies included emergency calls for respiratory or infectious disease causes and emergency room visits.ConclusionImplementing HCU-based monitoring systems in small areas deserves further investigations as it could facilitate the containment of COVID-19 and other unknown infectious diseases in the future.
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Affiliation(s)
- Ivan Merlo
- Department of Statistics and Quantitative Methods, University of Milano-Bicocca, Milan, Italy
| | - Mariano Crea
- Italian National Institute of Statistics, Rome, Italy
| | - Paolo Berta
- Department of Statistics and Quantitative Methods, University of Milano-Bicocca, Milan, Italy
| | - Francesca Ieva
- MOX, Department of Mathematics, Politecnico di Milano, Milan, Italy
- National Centre for Healthcare Research and Pharmacoepidemiology, University of Milano-Bicocca, Milan, Italy
- Center for Health Data Science, Human Technopole, Milan, Italy
| | - Flavia Carle
- National Centre for Healthcare Research and Pharmacoepidemiology, University of Milano-Bicocca, Milan, Italy
- Center of Epidemiology and Biostatistics, Polytechnic University of Marche, Ancona, Italy
| | - Federico Rea
- 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
| | - Gloria Porcu
- Department of Statistics and Quantitative Methods, University of Milano-Bicocca, Milan, Italy
| | - Laura Savaré
- MOX, Department of Mathematics, Politecnico di Milano, Milan, Italy
- National Centre for Healthcare Research and Pharmacoepidemiology, University of Milano-Bicocca, Milan, Italy
- Center for Health Data Science, Human Technopole, Milan, Italy
| | | | - Marco Villa
- Agency for Health Protection of Val Padana, Lombardy Region, Cremona, Italy
| | - Danilo Cereda
- Directorate General for Health, Lombardy Region, Milan, Italy
| | - Olivia Leoni
- Directorate General for Health, Lombardy Region, Milan, Italy
| | | | | | | | | | | | | | | | - Giovanni Corrao
- 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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Cohen Zion M, Gescheit I, Levy N, Yom-Tov E. Identifying Sleep Disorders From Search Engine Activity: Combining User-Generated Data With a Clinically Validated Questionnaire. J Med Internet Res 2022; 24:e41288. [DOI: 10.2196/41288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Revised: 10/25/2022] [Accepted: 11/15/2022] [Indexed: 11/24/2022] Open
Abstract
Background
Sleep disorders are experienced by up to 40% of the population but their diagnosis is often delayed by the availability of specialists.
Objective
We propose the use of search engine activity in conjunction with a validated web-based sleep questionnaire to facilitate wide-scale screening of prevalent sleep disorders.
Methods
Search advertisements offering a web-based sleep disorder screening questionnaire were shown on the Bing search engine to individuals who indicated an interest in sleep disorders. People who clicked on the advertisements and completed the sleep questionnaire were identified as being at risk for 1 of 4 common sleep disorders. A machine learning algorithm was applied to previous search engine queries to predict their suspected sleep disorder, as identified by the questionnaire.
Results
A total of 397 users consented to participate in the study and completed the questionnaire. Of them, 132 had sufficient past query data for analysis. Our findings show that diurnal patterns of people with sleep disorders were shifted by 2-3 hours compared to those of the controls. Past query activity was predictive of sleep disorders, approaching an area under the receiver operating characteristic curve of 0.62-0.69, depending on the sleep disorder.
Conclusions
Targeted advertisements can be used as an initial screening tool for people with sleep disorders. However, search engine data are seemingly insufficient as a sole method for screening. Nevertheless, we believe that evaluable web-based information, easily collected and processed with little effort on part of the physician and with low burden on the individual, can assist in the diagnostic process and possibly drive people to seek sleep assessment and diagnosis earlier than they currently do.
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Hong S, Son WS, Park B, Choi BY. Forecasting Hospital Visits Due to Influenza Based on Emergency Department Visits for Fever: A Feasibility Study on Emergency Department-Based Syndromic Surveillance. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph191912954. [PMID: 36232253 PMCID: PMC9566228 DOI: 10.3390/ijerph191912954] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 10/02/2022] [Accepted: 10/04/2022] [Indexed: 05/04/2023]
Abstract
This study evaluated the use of chief complaint data from emergency departments (EDs) to detect the increment of influenza cases identified from the nationwide medical service usage and developed a forecast model to predict the number of patients with influenza using the daily number of ED visits due to fever. The National Health Insurance Service (NHIS) and the National Emergency Department Information System (NEDIS) databases from 2015 to 2019 were used. The definition of fever included having an initial body temperature ≥ 38.0 °C at an ED department or having a report of fever as a patient's chief complaint. The moving average number of visits to the ED due to fever for the previous seven days was used. Patients in the NHIS with the International Classification of Diseases-10 codes of J09, J10, or J11 were classified as influenza cases, with a window duration of 100 days, assuming the claims were from the same season. We developed a forecast model according to an autoregressive integrated moving average (ARIMA) method using the data from 2015 to 2017 and validated it using the data from 2018 to 2019. Of the 29,142,229 ED visits from 2015 to 2019, 39.9% reported either a fever as a chief complaint or a ≥38.0 °C initial body temperature at the ED. ARIMA (1,1,1) (0,0,1)7 was the most appropriate model for predicting ED visits due to fever. The mean absolute percentage error (MAPE) value showed the prediction accuracy of the model. The correlation coefficient between the number of ED visits and the number of patients with influenza in the NHIS up to 14 days before the forecast, with the exceptions of the eighth, ninth, and twelfth days, was higher than 0.70 (p-value = 0.001). ED-based syndromic surveillances of fever were feasible for the early detection of hospital visits due to influenza.
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Affiliation(s)
- Sunghee Hong
- Department of Preventive Medicine, Hanyang University College of Medicine, Seoul 04763, Korea
- Department of Statistics and Data Science, Graduate School, Dongguk University, Seoul 04620, Korea
| | - Woo-Sik Son
- National Institute for Mathematical Sciences, Daejeon 34047, Korea
| | - Boyoung Park
- Department of Preventive Medicine, Hanyang University College of Medicine, Seoul 04763, Korea
- Correspondence: ; Tel.: + 82-2-2220-0682
| | - Bo Youl Choi
- Department of Preventive Medicine, Hanyang University College of Medicine, Seoul 04763, Korea
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Duchesne J, Coubard OA. First-wave COVID-19 daily cases obey gamma law. Infect Dis Model 2022; 7:64-74. [PMID: 35291224 PMCID: PMC8912979 DOI: 10.1016/j.idm.2022.02.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2021] [Revised: 01/29/2022] [Accepted: 02/27/2022] [Indexed: 11/08/2022] Open
Abstract
Modelling how a pandemic is spreading over time is a challenging issue. The new coronavirus disease called COVID-19 does not escape this rule as it has embraced over two hundred countries. As for previous pandemics, several studies have attempted to model the occurrence of cases caused by COVID-19. However, no study has succeeded in accurately modelling the impact of the infectious agent. Here we show that COVID-19 daily case distribution in humans obeys a Gamma law, which two new parameters can describe without any adjustment. Though the Gamma law has been exploited for nearly two centuries to describe the statistical distribution of spatial or temporal quantities, the goodness-of-fit rationale using two or three parameters has remained enigmatic. The new Gamma law approach we demonstrate here emerges from actual data and sheds light on the underlying mechanisms of the observed phenomenon. This finding has promising applicability in the epidemiological domain and in all disciplines involving branching systems, for which our Gamma law approach may bring a solution to hitherto unsolved problems.
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