scholarly journals Active syndromic surveillance of COVID-19 in Israel

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Elad Yom-Tov
Keyword(s):  
Medical Care ◽  
Syndromic Surveillance ◽  
Lead Time ◽  
Urgent Care ◽  
Surveillance Systems ◽  
Syndromic Surveillance System ◽  
Disease Indicators ◽  
Chat Bot ◽  
Optimization Mechanism ◽  
Google Search

AbstractSyndromic surveillance systems monitor disease indicators to detect emergence of diseases and track their progression. Here, we report on a rapidly deployed active syndromic surveillance system for tracking COVID-19 in Israel. The system was a novel combination of active and passive components: Ads were shown to people searching for COVID-19 symptoms on the Google search engine. Those who clicked on the ads were referred to a chat bot which helped them decide whether they needed urgent medical care. Through its conversion optimization mechanism, the ad system was guided to focus on those people who required such care. Over 6 months, the ads were shown approximately 214,000 times and clicked on 12,000 times, and 722 people were informed they needed urgent care. Click rates on ads and the fraction of people deemed to require urgent care were correlated with the hospitalization rate ($$R^2=0.54$$ R 2 = 0.54 and $$R^2=0.50$$ R 2 = 0.50 , respectively) with a lead time of 9 days. Males and younger people were more likely to use the system, and younger people were more likely to be determined to require urgent care (slope: $$- \,0.009$$ - 0.009 , $$P=0.01$$ P = 0.01 ). Thus, the system can assist in predicting case numbers and hospital load at a significant lead time and, simultaneously, help people determine if they need medical care.

scholarly journals Monitoring Respiratory Syncytial Virus Regionally In Children Aged < 5 Years Old Using Emergency Department and Urgent Care Center Chief Complaint Data in Florida’s Syndromic Surveillance System, Week 1, 2010 - Week 32, 2014

2015 ◽  
Vol 7 (1) ◽  
Author(s):  
Heather Rubino ◽  
David Atrubin ◽  
Janet J. Hamilton
Keyword(s):  
Surveillance System ◽  
Syndromic Surveillance ◽  
Hospital Admissions ◽  
Care Center ◽  
Laboratory Data ◽  
Chief Complaint ◽  
Urgent Care ◽  
Surveillance Systems ◽  
Syndromic Surveillance System ◽  
Syncytial Virus

ED chief complaint and discharge diagnosis data accessed through a syndromic surveillance system can be used for effective, timely monitoring of RSV hospitalizations in children < 5 years old and may be a more efficient and complete means of monitoring seasonality of RSV activity by region and statewide compared to hospital-based laboratory data reporting. Additionally, this surveillance technique can efficiently monitor RSV activity as well as estimate hospital admissions due to RSV and may be a useful approach for other states with syndromic surveillance systems.

Utility of Ambulance Data for Real-Time Syndromic Surveillance: A Pilot in the West Midlands Region, United Kingdom

2017 ◽  
Vol 32 (6) ◽  
pp. 667-672 ◽  
Author(s):  
Dan Todkill ◽  
Paul Loveridge ◽  
Alex J. Elliot ◽  
Roger A. Morbey ◽  
Obaghe Edeghere ◽  
...  
Keyword(s):  
United Kingdom ◽  
Real Time ◽  
Surveillance System ◽  
Syndromic Surveillance ◽  
Lessons Learned ◽  
Surveillance Systems ◽  
Preliminary Assessment ◽  
The West ◽  
Syndromic Surveillance System ◽  
West Midlands

AbstractIntroductionThe Public Health England (PHE; United Kingdom) Real-Time Syndromic Surveillance Team (ReSST) currently operates four national syndromic surveillance systems, including an emergency department system. A system based on ambulance data might provide an additional measure of the “severe” end of the clinical disease spectrum. This report describes the findings and lessons learned from the development and preliminary assessment of a pilot syndromic surveillance system using ambulance data from the West Midlands (WM) region in England.Hypothesis/ProblemIs an Ambulance Data Syndromic Surveillance System (ADSSS) feasible and of utility in enhancing the existing suite of PHE syndromic surveillance systems?MethodsAn ADSSS was designed, implemented, and a pilot conducted from September 1, 2015 through March 1, 2016. Surveillance cases were defined as calls to the West Midlands Ambulance Service (WMAS) regarding patients who were assigned any of 11 specified chief presenting complaints (CPCs) during the pilot period. The WMAS collected anonymized data on cases and transferred the dataset daily to ReSST, which contained anonymized information on patients’ demographics, partial postcode of patients’ location, and CPC. The 11 CPCs covered a broad range of syndromes. The dataset was analyzed descriptively each week to determine trends and key epidemiological characteristics of patients, and an automated statistical algorithm was employed daily to detect higher than expected number of calls. A preliminary assessment was undertaken to assess the feasibility, utility (including quality of key indicators), and timeliness of the system for syndromic surveillance purposes. Lessons learned and challenges were identified and recorded during the design and implementation of the system.ResultsThe pilot ADSSS collected 207,331 records of individual ambulance calls (daily mean=1,133; range=923-1,350). The ADSSS was found to be timely in detecting seasonal changes in patterns of respiratory infections and increases in case numbers during seasonal events.ConclusionsFurther validation is necessary; however, the findings from the assessment of the pilot ADSSS suggest that selected, but not all, ambulance indicators appear to have some utility for syndromic surveillance purposes in England. There are certain challenges that need to be addressed when designing and implementing similar systems.TodkillD, LoveridgeP, ElliotAJ, MorbeyRA, EdeghereO, Rayment-BishopT, Rayment-BishopC, ThornesJE, SmithG. Utility of ambulance data for real-time syndromic surveillance: a pilot in the West Midlands region, United Kingdom. Prehosp Disaster Med. 2017;32(6):667–672.

Enhanced One Health Surveillance during the 58th Presidential Inauguration—District of Columbia, January 2017

2019 ◽  
Vol 14 (2) ◽  
pp. 201-207
Author(s):  
Tiana A. Garrett-Cherry ◽  
Andrew K. Hennenfent ◽  
Sasha McGee ◽  
John Davies-Cole
Keyword(s):  
Syndromic Surveillance ◽  
Health Care Systems ◽  
Animal Health ◽  
Clinical Signs ◽  
Health Surveillance ◽  
Local Health ◽  
Surveillance Systems ◽  
Syndromic Surveillance System ◽  
Medical Aid ◽  
Care Systems

ABSTRACTObjective:In January 2017, Washington, DC, hosted the 58th United States presidential inauguration. The DC Department of Health leveraged multiple health surveillance approaches, including syndromic surveillance (human and animal) and medical aid station–based patient tracking, to detect disease and injury associated with this mass gathering.Methods:Patient data were collected from a regional syndromic surveillance system, medical aid stations, and an internet-based emergency department reporting system. Animal health data were collected from DC veterinary facilities.Results:Of 174 703 chief complaints from human syndromic data, there were 6 inauguration-related alerts. Inauguration attendees who visited aid stations (n = 162) and emergency departments (n = 180) most commonly reported feeling faint/dizzy (n = 29; 17.9%) and pain/cramps (n = 34;18.9%). In animals, of 533 clinical signs reported, most were gastrointestinal (n = 237; 44.5%) and occurred in canines (n = 374; 70.2%). Ten animals that presented dead on arrival were investigated; no significant threats were identified.Conclusion:Use of multiple surveillance systems allowed for near-real-time detection and monitoring of disease and injury syndromes in humans and domestic animals potentially associated with inaugural events and in local health care systems.

scholarly journals Using State and National Surveillance Systems during World Equestrian Games in NC

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Lana Deyneka ◽  
Zachary Faigen ◽  
Anne Hakenwerth ◽  
Nicole Lee ◽  
Amy Ising ◽  
...  
Keyword(s):  
Public Health ◽  
Syndromic Surveillance ◽  
Urgent Care ◽  
Poison Center ◽  
Surveillance Systems ◽  
Mass Gathering ◽  
Mass Gatherings ◽  
Epidemic Intelligence ◽  
Polk County ◽  
Ed Visits

ObjectiveTo describe surveillance activities and use of existing state (NC DETECT) and national (NSSP) syndromic surveillance systems during the International Federation for Equestrian Sports (FEI) World Equestrian Games (WEG), in Mill Spring, NC from September 11 to September 23, 2018MethodsNC DETECT collects statewide data from hospital emergency department (ED) visits and Carolinas Poison Center (CPC) calls. NC DETECT also collects data from select Urgent Care Centers (UCC) in the Charlotte area. CPC data are updated hourly, while ED data are updated twice a day. NC DETECT data were monitored daily for census (total ED visits), communicable disease syndromes, injury syndromes, and other occurrences of public health significance related to the event. The geographic areas monitored were Polk County (the location of the main event), the counties where the guests were lodging in the Western NC Region (Henderson, Transylvania, Buncombe, Rutherford, McDowell, and Cleveland), the Charlotte Metropolitan area, and statewide. Because of the large number of people from other states and countries who attended, ED surveillance was mainly conducted by hospitals so that visits were captured for all patients and not just NC residents. WEG dashboards containing ED data were created prior to the event using NC DETECT and NSSP ESSENCE systems, and were accessible to epidemiologists at the state level. NSSP syndrome queries were shared with the neighboring state (SC) public health agency. Surveillance began two weeks prior to the event to establish baseline levels for all ED visits for hospitals in Polk County and the Western NC Region. Surveillance occurred daily before the event, during the event, and for two weeks following the event to account for incubation periods of potential diseases.ResultsThe 2018 Equestrian games in Western NC were affected by heavy rain and heat. The weather led to low attendance and cancellation of a few competitions. During the observation period, ED admissions and most of the mass gathering related syndromes in both NC DETECT and NSSP systems were at baseline. ED admissions for motor vehicle collisions and dehydration syndromes were above baseline for 09/19 and 09/21/18 (Figures 3-4). CPC calls and UC admissions for selected UC centers in the Charlotte area were also monitored, and were at baseline.ConclusionsNC DETECT and NSSP Dashboards provided effective and timely surveillance for the WEG event to assist local public health in the rural NC area with epidemiologic investigations and appropriate response. NC DETECT’s CPC and UC data provided additional valuable information, and complemented ED surveillance during the mass gathering event. Syndromic surveillance became essential during WEG, as NC DPH deployment plans and resource availability changed when Hurricane Florence bore down on the region.References1. Joseph S. Lombardo, Carol A. Sniegoski, Wayne A. Loschen, Matthew Westercamp, Michael Wade, Shandy Dearth, and Guoyan Zhang Public Health Surveillance for Mass Gatherings Johns Hopkins APL Technical Digest , Volume 27, Number 4 (2008)2. Kaiser R, Coulombier D. Epidemic intelligence during mass gatherings. Euro Surveill. 2006;113. Ising A, Li M, Deyneka L, Vaughan-Batten H, Waller A. Improving syndromic surveillance for nonpower users: NC DETECT dashboards. Emerging Health Threats Journal 2011, 4: 11702 - DOI: 10.3402/ehtj.v4i0.11702 

Framework for the Development of Response Protocols for Public Health Syndromic Surveillance Systems: Case Studies of 8 US States

2009 ◽  
Vol 3 (S1) ◽  
pp. S29-S36 ◽  
Author(s):  
Lori Uscher-Pines ◽  
Corey L. Farrell ◽  
Steven M. Babin ◽  
Jacqueline Cattani ◽  
Charlotte A. Gaydos ◽  
...  
Keyword(s):  
Public Health ◽  
Surveillance System ◽  
Syndromic Surveillance ◽  
The United States ◽  
Essential Elements ◽  
System Response ◽  
Surveillance Systems ◽  
Health Departments ◽  
Public Health Response ◽  
Syndromic Surveillance System

ABSTRACTObjectives: To describe current syndromic surveillance system response protocols in health departments from 8 diverse states in the United States and to develop a framework for health departments to use as a guide in initial design and/or enhancement of response protocols.Methods: Case study design that incorporated in-depth interviews with health department staff, textual analysis of response plans, and a Delphi survey of syndromic surveillance response experts.Results: All 8 states and 30 of the 33 eligible health departments agreed to participate (91% response rate). Fewer than half (48%) of surveyed health departments had a written response protocol, and health departments reported conducting in-depth investigations on fewer than 15% of syndromic surveillance alerts. A convened panel of experts identified 32 essential elements for inclusion in public health protocols for response to syndromic surveillance system alerts.Conclusions: Because of the lack of guidance, limited resources for development of response protocols, and few examples of syndromic surveillance detecting previously unknown events of public health significance, health departments have not prioritized the development and refinement of response protocols. Systems alone, however, are not effective without an organized public health response. The framework proposed here can guide health departments in creating protocols that will be standardized, tested, and relevant given their goals with such systems. (Disaster Med Public Health Preparedness. 2009;3(Suppl 1):S29–S36)

scholarly journals Evaluating the application and utility of ESSENCE for early event detection

2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Allison K. Kunerth ◽  
Elizabeth Baker ◽  
Alan Zelicoff ◽  
Michael Elliot ◽  
Kevin Syberg
Keyword(s):  
Public Health ◽  
Event Detection ◽  
Syndromic Surveillance ◽  
Early Event ◽  
Surveillance Systems ◽  
User Characteristics ◽  
Syndromic Surveillance System ◽  
Local Public Health ◽  
Public Health Agencies ◽  
Surrounding Areas

ObjectiveA mixed methods study is being conducted on the statewide EarlyNotification of Community Based Epidemics (ESSENCE) systemin Missouri to identify factors that can improve the timeliness andidentification of outbreaks. This research will provide stakeholderswith guidance on how best to implement and improve ESSENCEusage statewide, and by sharing this research input can be solicitedon the utility of the applied framework as well as future implicationsfrom this body of work.IntroductionIn spite of the noted benefits of syndromic surveillance, andmore than a decade after it started gaining support, the primary usefor syndromic surveillance appears to be largely for seasonal andjurisdictional disease monitoring, event response and situationalawareness as opposed to its intended purpose of early event detection.(1-4) Research assessing the user characteristics and standards appliedat local public health agencies (LPHA’s) for syndromic surveillanceare scarce, and in national surveys epidemiologists frequently tendto utilize their own syndromic surveillance systems as opposed toa national system such as Biosense. While the National SyndromicSurveillance Program (NSSP) has addressed many operationalconcerns from stakeholders, and is in the process of providing accessto the cloud based Biosense platform-along with ESSENCE as a keytool, there is still a paucity of research that exists as to what can bedone to improve the utilization of syndromic surveillance systems forits primary purpose of early event detection.MethodsThis research proposes to evaluate the use of ESSENCE withinMissouri and the surrounding areas, to comprehensively identifyits strengths and limitations, through an assessment of the userexperience. This research will evaluate three key areas: 1) thequality of the data received by the syndromic surveillance system,2) the characteristics of the individuals and organizations utilizingthe system (end-users), 3) the influence and extent of syndromicsurveillance data on public health actions. ESSENCE data will beevaluated directly with special attention to the top three data qualityattributes across the literature, completeness, accuracy and timeliness.(5) A survey will also be administered to ESSENCE system users andpublic health leadership at LPHA’s, to gain insight into perspectives,perceptions and general practices, as well as how they interact withdata from ESSENCE.ResultsThe data for this research is primarily being collected throughoutthe fall of 2016, so the hope is to bring preliminary data to thisconference as a means to validate some of the findings, solicit inputon the proposed framework and share this research in a timely mannerfor the NSSP roll out of Biosense and ESSENCE.ConclusionsThrough a thorough evaluation, the application and utility ofESSENCE for early event detection will be better understood, alongwith the identification of factors that can be targeted in the future(and across syndromic surveillance platforms) for improvement in thetimely identification of outbreaks.

scholarly journals Cluster Detection Mechanisms for Syndromic Surveillance Systems: Systematic Review and Framework Development (Preprint)

2018 ◽  
Author(s):  
Prosper Kandabongee Yeng ◽  
Ashenafi Zebene Woldaregay ◽  
Terje Solvoll ◽  
Gunnar Hartvigsen
Keyword(s):  
Systematic Review ◽  
Disease Surveillance ◽  
Syndromic Surveillance ◽  
Space Time ◽  
Cluster Detection ◽  
Surveillance Systems ◽  
Syndromic Surveillance System ◽  
Detection Algorithms ◽  
Aberration Detection ◽  
Detection Mechanisms

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.

scholarly journals The Assessment of Twitter’s Potential for Outbreak Detection: Avian Influenza Case Study

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Samira Yousefinaghani ◽  
Rozita Dara ◽  
Zvonimir Poljak ◽  
Theresa M. Bernardo ◽  
Shayan Sharif
Keyword(s):  
Avian Influenza ◽  
Real World ◽  
Syndromic Surveillance ◽  
Animal Health ◽  
Outbreak Detection ◽  
Surveillance Systems ◽  
Sources Of Information ◽  
Syndromic Surveillance System ◽  
Influenza Outbreaks ◽  
Traditional Surveillance

AbstractSocial media services such as Twitter are valuable sources of information for surveillance systems. A digital syndromic surveillance system has several advantages including its ability to overcome the problem of time delay in traditional surveillance systems. Despite the progress made with using digital syndromic surveillance systems, the possibility of tracking avian influenza (AI) using online sources has not been fully explored. In this study, a Twitter-based data analysis framework was developed to automatically monitor avian influenza outbreaks in a real-time manner. The framework was implemented to find worrisome posts and alerting news on Twitter, filter irrelevant ones, and detect the onset of outbreaks in several countries. The system collected and analyzed over 209,000 posts discussing avian influenza on Twitter from July 2017 to November 2018. We examined the potential of Twitter data to represent the date, severity and virus type of official reports. Furthermore, we investigated whether filtering irrelevant tweets can positively impact the performance of the system. The proposed approach was empirically evaluated using a real-world outbreak-reporting source. We found that 75% of real-world outbreak notifications of AI were identifiable from Twitter. This shows the capability of the system to serve as a complementary approach to official AI reporting methods. Moreover, we observed that one-third of outbreak notifications were reported on Twitter earlier than official reports. This feature could augment traditional surveillance systems and provide a possibility of early detection of outbreaks. This study could potentially provide a first stepping stone for building digital disease outbreak warning systems to assist epidemiologists and animal health professionals in making relevant decisions.

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