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Zareie B, Poorolajal J, Roshani A, Menbari A, Karami M. Outbreak detector: a web application to boost disease surveillance systems and timely detection of infectious disease epidemics. BMC Res Notes 2024; 17:229. [PMID: 39164780 PMCID: PMC11334589 DOI: 10.1186/s13104-024-06892-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2024] [Accepted: 08/06/2024] [Indexed: 08/22/2024] Open
Abstract
OBJECTIVE Digital technologies have improved the performance of surveillance systems through early detection of outbreaks and epidemic control. The aim of this study is to introduce an outbreak detection web application called OBDETECTOR (Outbreak Detector), which as a professional web application has the ability to process weekly or daily reported data from disease surveillance systems and facilitates the early detection of disease outbreaks. RESULTS OBDETECTOR generates a histogram that exhibits the trend of infection within a time range selected by the user. The output comprises red triangles and plus signs, where the former denotes outbreak days determined by the algorithm applied to the data, and the latter represents days identified as outbreaks by the researcher. The graph also displays threshold values and its symbols enable researchers to compute evaluation criteria for outbreak detection algorithms, including sensitivity and specificity. OBDETECTOR allows users to modify algorithm parameters based on their research objectives immediately after loading data. The implementation of automatic web applications results in immediate reporting, precise analysis, and prompt alert notification. Moreover, Public Health authorities and other stakeholders of surveillance can benefit from the widespread accessibility and user-friendliness of these tools, enhancing their knowledge and skills for better engagement in surveillance programs.
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Affiliation(s)
- Bushra Zareie
- Department of Epidemiology, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran
- Bone and Joint Reconstruction Research Center, Department of Orthopedics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Jalal Poorolajal
- Department of Epidemiology, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Amin Roshani
- Department of Statistics, Lorestan University, Khorramabad, Iran
| | - Ahmed Menbari
- Department of Computer Engineering, Sannandaj Branch, Islamic Azad University, Sanandaj, Iran
| | - Manoochehr Karami
- Department of Epidemiology, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
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Lu C, Wang L, Barr I, Lambert S, Mengersen K, Yang W, Li Z, Si X, McClymont H, Haque S, Gan T, Vardoulakis S, Bambrick H, Hu W. Developing a Research Network of Early Warning Systems for Infectious Diseases Transmission Between China and Australia. China CDC Wkly 2024; 6:740-753. [PMID: 39114314 PMCID: PMC11301605 DOI: 10.46234/ccdcw2024.166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Accepted: 06/18/2024] [Indexed: 08/10/2024] Open
Abstract
This article offers a thorough review of current early warning systems (EWS) and advocates for establishing a unified research network for EWS in infectious diseases between China and Australia. We propose that future research should focus on improving infectious disease surveillance by integrating data from both countries to enhance predictive models and intervention strategies. The article highlights the need for standardized data formats and terminologies, improved surveillance capabilities, and the development of robust spatiotemporal predictive models. It concludes by examining the potential benefits and challenges of this collaborative approach and its implications for global infectious disease surveillance. This is particularly relevant to the ongoing project, early warning systems for Infectious Diseases between China and Australia (NetEWAC), which aims to use seasonal influenza as a case study to analyze influenza trends, peak activities, and potential inter-hemispheric transmission patterns. The project seeks to integrate data from both hemispheres to improve outbreak predictions and develop a spatiotemporal predictive modeling system for seasonal influenza transmission based on socio-environmental factors.
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Affiliation(s)
- Cynthia Lu
- Ecosystem Change and Population Health Research Group, School of Public Health and Social Work, Queensland University of Technology, Brisbane, Australia
| | - Liping Wang
- Division of Infectious Disease, National Key Laboratory of Intelligent Tracking and Forcasting for Infectious Diseases, Chinese Centre for Disease Control and Prevention, Beijing, China
| | - Ian Barr
- WHO Collaborating Centre for Reference and Research on Influenza, VIDRL, Doherty Institute, Melbourne, Australia
- Department of Microbiology and Immunology, University of Melbourne, Victoria, Australia
| | - Stephen Lambert
- Communicable Disease Branch, Queensland Health, Brisbane, Queensland, Australia
- National Centre for Immunisation Research and Surveillance, Sydney Children’s Hospitals Network, Westmead, NSW, Australia
| | - Kerrie Mengersen
- School of Mathematical Sciences, Faculty of Science, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Weizhong Yang
- School of Population Medicine & Public Health, Chinese Academy of Medical Science/Peking Union Medical College, Beijing, China
| | - Zhongjie Li
- School of Population Medicine & Public Health, Chinese Academy of Medical Science/Peking Union Medical College, Beijing, China
| | - Xiaohan Si
- Ecosystem Change and Population Health Research Group, School of Public Health and Social Work, Queensland University of Technology, Brisbane, Australia
| | - Hannah McClymont
- Ecosystem Change and Population Health Research Group, School of Public Health and Social Work, Queensland University of Technology, Brisbane, Australia
| | - Shovanur Haque
- Ecosystem Change and Population Health Research Group, School of Public Health and Social Work, Queensland University of Technology, Brisbane, Australia
| | - Ting Gan
- Ecosystem Change and Population Health Research Group, School of Public Health and Social Work, Queensland University of Technology, Brisbane, Australia
| | - Sotiris Vardoulakis
- HEAL Global Research Centre, Health Research Institute, University of Canberra, Australian Capital Territory, Australia
| | - Hilary Bambrick
- National Centre for Epidemiology and Population Health, College of Health and Medicine, The Australian National University, Canberra, Australian Capital Territory, Australia
| | - Wenbiao Hu
- Ecosystem Change and Population Health Research Group, School of Public Health and Social Work, Queensland University of Technology, Brisbane, Australia
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Meckawy R, Stuckler D, Mehta A, Al-Ahdal T, Doebbeling BN. Effectiveness of early warning systems in the detection of infectious diseases outbreaks: a systematic review. BMC Public Health 2022; 22:2216. [PMCID: PMC9707072 DOI: 10.1186/s12889-022-14625-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 11/14/2022] [Indexed: 11/30/2022] Open
Abstract
Abstract
Background
Global pandemics have occurred with increasing frequency over the past decade reflecting the sub-optimum operationalization of surveillance systems handling human health data. Despite the wide array of current surveillance methods, their effectiveness varies with multiple factors. Here, we perform a systematic review of the effectiveness of alternative infectious diseases Early Warning Systems (EWSs) with a focus on the surveillance data collection methods, and taking into consideration feasibility in different settings.
Methods
We searched PubMed and Scopus databases on 21 October 2022. Articles were included if they covered the implementation of an early warning system and evaluated infectious diseases outbreaks that had potential to become pandemics. Of 1669 studies screened, 68 were included in the final sample. We performed quality assessment using an adapted CASP Checklist.
Results
Of the 68 articles included, 42 articles found EWSs successfully functioned independently as surveillance systems for pandemic-wide infectious diseases outbreaks, and 16 studies reported EWSs to have contributing surveillance features through complementary roles. Chief complaints from emergency departments’ data is an effective EWS but it requires standardized formats across hospitals. Centralized Public Health records-based EWSs facilitate information sharing; however, they rely on clinicians’ reporting of cases. Facilitated reporting by remote health settings and rapid alarm transmission are key advantages of Web-based EWSs. Pharmaceutical sales and laboratory results did not prove solo effectiveness. The EWS design combining surveillance data from both health records and staff was very successful. Also, daily surveillance data notification was the most successful and accepted enhancement strategy especially during mass gathering events. Eventually, in Low Middle Income Countries, working to improve and enhance existing systems was more critical than implementing new Syndromic Surveillance approaches.
Conclusions
Our study was able to evaluate the effectiveness of Early Warning Systems in different contexts and resource settings based on the EWSs’ method of data collection. There is consistent evidence that EWSs compiling pre-diagnosis data are more proactive to detect outbreaks. However, the fact that Syndromic Surveillance Systems (SSS) are more proactive than diagnostic disease surveillance should not be taken as an effective clue for outbreaks detection.
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Hyllestad S, Amato E, Nygård K, Vold L, Aavitsland P. The effectiveness of syndromic surveillance for the early detection of waterborne outbreaks: a systematic review. BMC Infect Dis 2021; 21:696. [PMID: 34284731 PMCID: PMC8290622 DOI: 10.1186/s12879-021-06387-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Accepted: 07/06/2021] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND Waterborne outbreaks are still a risk in high-income countries, and their early detection is crucial to limit their societal consequences. Although syndromic surveillance is widely used for the purpose of detecting outbreaks days earlier than traditional surveillance systems, evidence of the effectiveness of such systems is lacking. Thus, our objective was to conduct a systematic review of the effectiveness of syndromic surveillance to detect waterborne outbreaks. METHOD We searched the Cochrane Library, Medline/PubMed, EMBASE, Scopus, and Web of Science for relevant published articles using a combination of the keywords 'drinking water', 'surveillance', and 'waterborne disease' for the period of 1990 to 2018. The references lists of the identified articles for full-text record assessment were screened, and searches in Google Scholar using the same key words were conducted. We assessed the risk of bias in the included articles using the ROBINS-I tool and PRECEPT for the cumulative body of evidence. RESULTS From the 1959 articles identified, we reviewed 52 articles, of which 18 met the eligibility criteria. Twelve were descriptive/analytical studies, whereas six were simulation studies. There is no clear evidence for syndromic surveillance in terms of the ability to detect waterborne outbreaks (low sensitivity and high specificity). However, one simulation study implied that multiple sources of signals combined with spatial information may increase the timeliness in detecting a waterborne outbreak and reduce false alarms. CONCLUSION This review demonstrates that there is no conclusive evidence on the effectiveness of syndromic surveillance for the detection of waterborne outbreaks, thus suggesting the need to focus on primary prevention measures to reduce the risk of waterborne outbreaks. Future studies should investigate methods for combining health and environmental data with an assessment of needed financial and human resources for implementing such surveillance systems. In addition, a more critical thematic narrative synthesis on the most promising sources of data, and an assessment of the basis for arguments that joint analysis of different data or dimensions of data (e.g. spatial and temporal) might perform better, should be carried out. TRIAL REGISTRATION PROSPERO: International prospective register of systematic reviews. 2019. CRD42019122332 .
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Affiliation(s)
- Susanne Hyllestad
- Department of Infection Control and Preparedness, Norwegian Institute of Public Health, Oslo, Norway.
- Faculty of Medicine, University of Oslo, Institute of Health and Society, Oslo, Norway.
| | - Ettore Amato
- Department of Infection Control and Preparedness, Norwegian Institute of Public Health, Oslo, Norway
| | - Karin Nygård
- Department of Infection Control and Preparedness, Norwegian Institute of Public Health, Oslo, Norway
| | - Line Vold
- Department of Infection Control and Preparedness, Norwegian Institute of Public Health, Oslo, Norway
| | - Preben Aavitsland
- Department of Infection Control and Preparedness, Norwegian Institute of Public Health, Oslo, Norway
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Quinn E, Hsiao KH, Maitland-Scott I, Gomez M, Baysari MT, Najjar Z, Gupta L. Web-Based Apps for Responding to Acute Infectious Disease Outbreaks in the Community: Systematic Review. JMIR Public Health Surveill 2021; 7:e24330. [PMID: 33881406 PMCID: PMC8100883 DOI: 10.2196/24330] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Revised: 11/08/2020] [Accepted: 12/24/2020] [Indexed: 11/26/2022] Open
Abstract
Background Web-based technology has dramatically improved our ability to detect communicable disease outbreaks, with the potential to reduce morbidity and mortality because of swift public health action. Apps accessible through the internet and on mobile devices create an opportunity to enhance our traditional indicator-based surveillance systems, which have high specificity but issues with timeliness. Objective The aim of this study is to describe the literature on web-based apps for indicator-based surveillance and response to acute communicable disease outbreaks in the community with regard to their design, implementation, and evaluation. Methods We conducted a systematic search of the published literature across four databases (MEDLINE via OVID, Web of Science Core Collection, ProQuest Science, and Google Scholar) for peer-reviewed journal papers from January 1998 to October 2019 using a keyword search. Papers with the full text available were extracted for review, and exclusion criteria were applied to identify eligible papers. Results Of the 6649 retrieved papers, 23 remained, describing 15 web-based apps. Apps were primarily designed to improve the early detection of disease outbreaks, targeted government settings, and comprised either complex algorithmic or statistical outbreak detection mechanisms or both. We identified a need for these apps to have more features to support secure information exchange and outbreak response actions, with a focus on outbreak verification processes and staff and resources to support app operations. Evaluation studies (6 out of 15 apps) were mostly cross-sectional, with some evidence of reduction in time to notification of outbreak; however, studies lacked user-based needs assessments and evaluation of implementation. Conclusions Public health officials designing new or improving existing disease outbreak web-based apps should ensure that outbreak detection is automatic and signals are verified by users, the app is easy to use, and staff and resources are available to support the operations of the app and conduct rigorous and holistic evaluations.
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Affiliation(s)
- Emma Quinn
- Sydney Local Health District, Camperdown Public Health Unit, Royal Prince Alfred Hospital Campus, Camperdown, Sydney, NSW, Australia.,School of Public Health, Faculty of Medicine and Health, University of Sydney, Camperdown, Sydney, NSW, Australia
| | - Kai Hsun Hsiao
- Sydney Local Health District, Camperdown Public Health Unit, Royal Prince Alfred Hospital Campus, Camperdown, Sydney, NSW, Australia
| | - Isis Maitland-Scott
- Sydney Local Health District, Camperdown Public Health Unit, Royal Prince Alfred Hospital Campus, Camperdown, Sydney, NSW, Australia
| | - Maria Gomez
- Sydney Local Health District, Camperdown Public Health Unit, Royal Prince Alfred Hospital Campus, Camperdown, Sydney, NSW, Australia
| | - Melissa T Baysari
- Charles Perkins Centre, Faculty of Medicine and Health, University of Sydney, Camperdown, Sydney, NSW, Australia
| | - Zeina Najjar
- Sydney Local Health District, Camperdown Public Health Unit, Royal Prince Alfred Hospital Campus, Camperdown, Sydney, NSW, Australia
| | - Leena Gupta
- Sydney Local Health District, Camperdown Public Health Unit, Royal Prince Alfred Hospital Campus, Camperdown, Sydney, NSW, Australia.,School of Public Health, Faculty of Medicine and Health, University of Sydney, Camperdown, Sydney, NSW, Australia
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Marbus S, van der Hoek W, van Dissel J, van Gageldonk-Lafeber A. Experience of establishing severe acute respiratory surveillance in the Netherlands: Evaluation and challenges. PUBLIC HEALTH IN PRACTICE 2020; 1:100014. [PMID: 34171043 PMCID: PMC7260511 DOI: 10.1016/j.puhip.2020.100014] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2019] [Revised: 05/02/2020] [Accepted: 05/12/2020] [Indexed: 11/24/2022] Open
Abstract
The 2009 influenza A (H1N1) pandemic prompted the World Health Organization (WHO) to recommend countries to establish a national severe acute respiratory infections (SARI) surveillance system for preparedness and emergency response. However, setting up or maintaining a robust SARI surveillance system has been challenging. Similar to other countries, surveillance data on hospitalisations for SARI in the Netherlands are still limited, in contrast to the robust surveillance data in primary care. The objective of this narrative review is to provide an overview, evaluation, and challenges of already available surveillance systems or datasets in the Netherlands, which might be used for near real-time surveillance of severe respiratory infections. Seven available surveillance systems or datasets in the Netherlands were reviewed. The evaluation criteria, including data quality, timeliness, representativeness, simplicity, flexibility, acceptability and stability were based on United States Centers for Disease Control and Prevention (CDC) and European Centre for Disease Prevention and Control (ECDC) guidelines for public health surveillance. We added sustainability as additional evaluation criterion. The best evaluated surveillance system or dataset currently available for SARI surveillance is crude mortality monitoring, although it lacks specificity. In contrast to influenza-like illness (ILI) in primary care, there is currently no gold standard for SARI surveillance in the Netherlands. Based on our experience with sentinel SARI surveillance, a fully or semi-automated, passive surveillance system seems most suited for a sustainable SARI surveillance system. An important future challenge remains integrating SARI surveillance into existing hospital programs in order to make surveillance data valuable for public health, as well as hospital quality of care management and individual patient care.
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Affiliation(s)
- S.D. Marbus
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, the Netherlands
| | - W. van der Hoek
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, the Netherlands
| | - J.T. van Dissel
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, the Netherlands
- Department of Infectious Diseases and Internal Medicine, Leiden University Medical Center, Leiden, the Netherlands
| | - A.B. van Gageldonk-Lafeber
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, the Netherlands
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Duijster JW, Doreleijers SDA, Pilot E, van der Hoek W, Kommer GJ, van der Sande MAB, Krafft T, van Asten LCHI. Utility of emergency call centre, dispatch and ambulance data for syndromic surveillance of infectious diseases: a scoping review. Eur J Public Health 2020; 30:639-647. [PMID: 31605491 PMCID: PMC7446941 DOI: 10.1093/eurpub/ckz177] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023] Open
Abstract
BACKGROUND Syndromic surveillance can supplement conventional health surveillance by analyzing less-specific, near-real-time data for an indication of disease occurrence. Emergency medical call centre dispatch and ambulance data are examples of routinely and efficiently collected syndromic data that might assist in infectious disease surveillance. Scientific literature on the subject is scarce and an overview of results is lacking. METHODS A scoping review including (i) review of the peer-reviewed literature, (ii) review of grey literature and (iii) interviews with key informants. RESULTS Forty-four records were selected: 20 peer reviewed and 24 grey publications describing 44 studies and systems. Most publications focused on detecting respiratory illnesses or on outbreak detection at mass gatherings. Most used retrospective data; some described outcomes of temporary systems; only two described continuously active dispatch- and ambulance-based syndromic surveillance. Key informants interviewed valued dispatch- and ambulance-based syndromic surveillance as a potentially useful addition to infectious disease surveillance. Perceived benefits were its potential timeliness, standardization of data and clinical value of the data. CONCLUSIONS Various dispatch- and ambulance-based syndromic surveillance systems for infectious diseases have been reported, although only roughly half are documented in peer-reviewed literature and most concerned retrospective research instead of continuously active surveillance systems. Dispatch- and ambulance-based syndromic data were mostly assessed in relation to respiratory illnesses; reported use for other infectious disease syndromes is limited. They are perceived by experts in the field of emergency surveillance to achieve time gains in detection of infectious disease outbreaks and to provide a useful addition to traditional surveillance efforts.
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Affiliation(s)
- Janneke W Duijster
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment (Rijksinstituut voor Volksgezondheid en Milieu, RIVM), Bilthoven, The Netherlands
| | - Simone D A Doreleijers
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment (Rijksinstituut voor Volksgezondheid en Milieu, RIVM), Bilthoven, The Netherlands
- Department of Health, Ethics and Society, Care and Public Health Research Institute (CAPHRI), Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
| | - Eva Pilot
- Department of Health, Ethics and Society, Care and Public Health Research Institute (CAPHRI), Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
| | - Wim van der Hoek
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment (Rijksinstituut voor Volksgezondheid en Milieu, RIVM), Bilthoven, The Netherlands
| | - Geert Jan Kommer
- Centre for Nutrition, Prevention and Health Services, National Institute for Public Health and the Environment (Rijksinstituut voor Volksgezondheid en Milieu, RIVM), Bilthoven, The Netherlands
| | - Marianne A B van der Sande
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment (Rijksinstituut voor Volksgezondheid en Milieu, RIVM), Bilthoven, The Netherlands
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
- Department of Public Health, Institute of Tropical Medicine, Antwerp, Belgium
| | - Thomas Krafft
- Department of Health, Ethics and Society, Care and Public Health Research Institute (CAPHRI), Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
- Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
- Institute of Environment Education and Research, Bharati Vidyapeeth University, Pune, India
| | - Liselotte C H I van Asten
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment (Rijksinstituut voor Volksgezondheid en Milieu, RIVM), Bilthoven, The Netherlands
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Contribution of Enteroviruses to Acute Central Nervous System or Systemic Infections in Northern Italy (2015-2017): Is It Time to Establish a National Laboratory-Based Surveillance System? BIOMED RESEARCH INTERNATIONAL 2020; 2020:9393264. [PMID: 32685546 PMCID: PMC7352123 DOI: 10.1155/2020/9393264] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Accepted: 06/18/2020] [Indexed: 11/17/2022]
Abstract
Background Enteroviruses (EVs) can cause infections and outbreaks of mild to severe diseases, such as central nervous system (CNS) and systemic infections. The contribution of EVs to acute CNS/systemic infections requiring hospitalization was assessed by analysing data extracted from virology laboratory database. Methods Real-life data obtained from two molecular virology laboratories located in Northern Italy were retrieved from databases and analysed retrospectively. The queries used to extract the data were (i) requests for EV-RNA detection in clear cerebrospinal fluid (CSF) specimens collected from hospitalized patients with suspected acute CNS (including aseptic meningitis, encephalitis, and acute flaccid myelitis/paralysis) or systemic infections (sepsis-like illness or fever (≥ 38°C) of unknown origin), (ii) CSF samples collected from January 1st, 2015, to December 31st, 2017. Results 582 requests of EV-RNA detection in CSF samples collected from as many patients of any age were recorded. EV-RNA was detected in 4.5% of the CSF samples; 92.3% of EV-positive cases were patients < 15 years, 58.3% of whom were < 3 months. EVs circulated all-year-round, and the highest EV-positive rates were observed from May to August. The risk of EV infection and the relative illness ratio value among children < 1 − year − old were significantly higher than those observed for older patients. Conclusions EV surveillance should be carried out for all pediatric patients < 15 years and especially children less than 1 year of age with clinically suspected CNS infection/systemic infections. The implementation of a laboratory-based surveillance established for analysing the virological data provided by laboratories that routinely perform EV molecular testing may enable us to determine the impact of EVs that can cause infections requiring hospitalization.
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Yeng PK, Woldaregay AZ, Solvoll T, Hartvigsen G. Cluster Detection Mechanisms for Syndromic Surveillance Systems: Systematic Review and Framework Development. JMIR Public Health Surveill 2020; 6:e11512. [PMID: 32357126 PMCID: PMC7284413 DOI: 10.2196/11512] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2018] [Revised: 10/29/2018] [Accepted: 02/06/2020] [Indexed: 12/26/2022] Open
Abstract
Background The time lag in detecting disease outbreaks remains a threat to global health security. The advancement of technology has made health-related data and other indicator activities easily accessible for syndromic surveillance of various datasets. At the heart of disease surveillance lies the clustering algorithm, which groups data with similar characteristics (spatial, temporal, or both) to uncover significant disease outbreak. Despite these developments, there is a lack of updated reviews of trends and modelling options in cluster detection algorithms. Objective Our purpose was to systematically review practically implemented disease surveillance clustering algorithms relating to temporal, spatial, and spatiotemporal clustering mechanisms for their usage and performance efficacies, and to develop an efficient cluster detection mechanism framework. Methods We conducted a systematic review exploring Google Scholar, ScienceDirect, PubMed, IEEE Xplore, ACM Digital Library, and Scopus. Between January and March 2018, we conducted the literature search for articles published to date in English in peer-reviewed journals. The main eligibility criteria were studies that (1) examined a practically implemented syndromic surveillance system with cluster detection mechanisms, including over-the-counter medication, school and work absenteeism, and disease surveillance relating to the presymptomatic stage; and (2) focused on surveillance of infectious diseases. We identified relevant articles using the title, keywords, and abstracts as a preliminary filter with the inclusion criteria, and then conducted a full-text review of the relevant articles. We then developed a framework for cluster detection mechanisms for various syndromic surveillance systems based on the review. Results The search identified a total of 5936 articles. Removal of duplicates resulted in 5839 articles. After an initial review of the titles, we excluded 4165 articles, with 1674 remaining. Reading of abstracts and keywords eliminated 1549 further records. An in-depth assessment of the remaining 125 articles resulted in a total of 27 articles for inclusion in the review. The result indicated that various clustering and aberration detection algorithms have been empirically implemented or assessed with real data and tested. Based on the findings of the review, we subsequently developed a framework to include data processing, clustering and aberration detection, visualization, and alerts and alarms. Conclusions The review identified various algorithms that have been practically implemented and tested. These results might foster the development of effective and efficient cluster detection mechanisms in empirical syndromic surveillance systems relating to a broad spectrum of space, time, or space-time.
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Affiliation(s)
- Prosper Kandabongee Yeng
- Department of Computer Science, University of Tromsø, The Arctic University of Norway, Gjøvik, Norway.,Department of Information Security and Communication Technology, Norwegian University of Science and Technology, Gjøvik, Norway
| | | | - Terje Solvoll
- Norwegian Centre for E-health Research, University Hospital, Tromsø, Norway
| | - Gunnar Hartvigsen
- Department of Computer Science, University of Tromsø, The Arctic University of Norway, Gjøvik, Norway
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