scholarly journals Surveillance of ambulance dispatch data as a tool for early warning

2006 ◽  
Vol 11 (12) ◽  
pp. 13-14 ◽  
Author(s):  
K H Bork ◽  
B M Klein ◽  
K Mølbak ◽  
S Trautner ◽  
U B Pedersen ◽  
...  
Keyword(s):  
Surveillance System ◽  
Disease Surveillance ◽  
Disease Outbreaks ◽  
Detection Algorithm ◽  
Detection Sensitivity ◽  
Outbreak Detection ◽  
Early Detection Of Disease ◽  
Prediction Limit ◽  
Ambulance Dispatch ◽  
Time To Detection

Early detection of disease outbreaks is essential for authorities to initiate and conduct an appropriate response. A need for an outbreak detection that monitored data predating laboratory confirmations was identified, which prompted the establishment of a novel symptom surveillance system. The surveillance system monitors approximately 80% of the Danish population by applying an outbreak detection algorithm to ambulance dispatch data. The system also monitors both regional and national activity and has a built-in, switch-on capacity for implementing symptom surveillance reporting in case of an alert. In an evaluation with outbreak scenarios it was found that decreasing the outbreak detection sensitivity from a prediction limit of 95% to one of 99% moderately reduced the time to detection, but considerably diminished the number of false alerts. The system was able to detect an increased activity of influenza-like illness in December 2003 in a timely fashion. The system has now been implemented in the national disease surveillance programme.

scholarly journals Web-based applications for response to acute infectious disease outbreaks in the community: a systematic review (Preprint)

2020 ◽  
Author(s):  
Emma Quinn ◽  
Kai Hsun Hsiao ◽  
Isis Maitland-Scott ◽  
Maria Gomez ◽  
Melissa T Baysari ◽  
...  
Keyword(s):  
Public Health ◽  
Information Exchange ◽  
Keyword Search ◽  
Communicable Disease ◽  
Evaluation Studies ◽  
Disease Outbreaks ◽  
Outbreak Detection ◽  
Public Health Action ◽  
Web Based ◽  
Early Detection Of Disease

BACKGROUND Web-based technology has dramatically improved our ability to detect communicable disease outbreaks, with the potential to reduce morbidity and mortality due to swift public health action. Applications 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 We sought to describe the literature on web-based apps for indicator-based surveillance and response to acute communicable disease outbreaks in the community, in regards to their design, implementation and evaluation. METHODS We conducted a systematic search of the published literature across four databases (Medline via OVID, via OVID, Web of Science Core Collection, ProQuest Science and Google Scholar) for peer-reviewed journal articles from January 1998 to October 2019 using a keyword search. Articles with full text available were extracted for review, and exclusion criteria applied to identify eligible articles. RESULTS From 6649 retrieved articles, a total of 23 remained, describing 15 web-apps. Apps were primarily designed to improve the early detection of disease outbreaks, targeted government settings, and comprised complex algorithmic and/or statistical outbreak detection mechanisms. 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 (6/15 apps) were mostly cross-sectional with some evidence of reduction to time to notification of outbreak, but studies were lacking user-based needs assessments and evaluation of implementation. CONCLUSIONS Public health officials designing new or improving existing disease outbreak web apps should ensure that outbreak detection is automatic and signals are verified by users, the app is easy to use, and that staff and resources are available to support the operations of the app, as well as conduct rigorous and holistic evaluations. CLINICALTRIAL

scholarly journals Using the MCRISP Network to Study Acute Gastroenteritis and Influenza-Like Illness Outbreaks in Child Care Centers Compared to Statewide Epidemics

2019 ◽  
Vol 34 (s1) ◽  
pp. s57-s57
Author(s):  
Andrew Hashikawa ◽  
Student Peter DeJonge ◽  
Stuart Bradin ◽  
Emily Martin
Keyword(s):  
Child Care ◽  
Acute Gastroenteritis ◽  
Surveillance System ◽  
Disease Surveillance ◽  
Care Center ◽  
Incidence Rates ◽  
Surveillance Program ◽  
Early Detection Of Disease ◽  
Influenza Like Illness ◽  
Disease Surveillance System

Introduction:Biosurveillance is critical for early detection of disease outbreaks and resource mobilization. Child care center (CCC) attendance has long been recognized as a significant independent predictor for respiratory and gastrointestinal diseases, but CCC surveillance is currently not part of the statewide disease surveillance system. The Michigan Child Care Related Infections Surveillance Program (MCRISP) is an independent, online reporting network with >30 local CCCs that was created to fill this surveillance gap.Aim:To describe the capability of a novel CCC biosurveillance system (MCRISP) to report pediatric Influenza-Like Illness (ILI) and Acute Gastroenteritis (AGE) illness over three years to (i) assess both the timing and magnitude of epidemics in CCCs and (ii) compare CCC outbreak patterns with those of the state database.Methods:MCRISP collates real-time syndromic reports of illness from local county CCCs. The statewide Michigan Disease Surveillance System (MDSS) collects reports of diagnosed illness from designated laboratories, clinics, and hospitals statewide. We assessed epidemic curves based on MCRISP incidence rates and MDSS case counts for ILI and AGE over three seasons (2014-7).Results:A total of 4,627 MCRISP cases (2,425 ILI and 2,202 AGE reports) were reported during the three years of study surveillance. Epidemic patterns (seasonal peaks, troughs, and breadth) for both ILI and AGE in CCCs mirrored those reported at county and state levels, respectively. Two distinguishing features of CCC ILI outbreaks were noted in all three seasons: MCRISP ILI rates remained elevated after MDSS influenza counts abated, and MCRISP rates consistently peaked prior to MDSS influenza peaks. Neither of these phenomena were observed in comparing AGE outbreaks between surveillance systems.Discussion:ILI and AGE incidence rates from the MCRISP network appeared to broadly mirror epidemics from the established state surveillance system. MCRISP may act as a sentinel system for larger community outbreaks of respiratory disease.

scholarly journals Disease Outbreak Detection Using Search Keywords Patterns

10.29007/z8tp ◽  
2020 ◽  
Author(s):  
Izzat Alsmadi ◽  
Zaid Almubaid ◽  
Hisham Al-Mubaid
Keyword(s):  
Disease Surveillance ◽  
Positive Impact ◽  
Disease Outbreaks ◽  
Disease Outbreak ◽  
Outbreak Detection ◽  
The Internet ◽  
Disease Outbreak Detection ◽  
Target Disease ◽  
Query Patterns ◽  
Source Of Information

In the recent years, people are becoming more dependent on the Internet as their main source of information about healthcare. A number of research projects in the past few decades examined and utilized the internet data for information extraction in healthcare including disease surveillance and monitoring. In this paper, we investigate and study the potential of internet data like internet search keywords and search query patterns in the healthcare domain for disease monitoring and detection. Specifically, we investigate search keyword patterns for disease outbreak detection. Accurate prediction and detection of disease outbreaks in a timely manner can have a big positive impact on the entire health care system. Our method utilizes machine learning in identifying interesting patterns related to target disease outbreak from search keyword logs. We conducted experiments on the flu disease, which is the most searched disease in the interest of this problem. We showed examples of keywords that can be good predictors of outbreaks of the flu. Our method proved that the correlation between search queries and keyword trends are truly reliable in the sense that it can be used to predict the outbreak of the disease.

Evaluation of a Statewide Foodborne Illness Complaint Surveillance System in Minnesota, 2000 through 2006

2010 ◽  
Vol 73 (11) ◽  
pp. 2059-2064 ◽  
Author(s):  
JOHN LI ◽  
KIRK SMITH ◽  
DAWN KAEHLER ◽  
KAREN EVERSTINE ◽  
JOSH ROUNDS ◽  
...  
Keyword(s):  
Surveillance System ◽  
Disease Surveillance ◽  
Foodborne Illness ◽  
Outbreak Detection ◽  
Medical Attention ◽  
Consumer Complaints ◽  
Department Of Health ◽  
Significant Difference ◽  
Foodborne Outbreaks ◽  
National Framework

Foodborne outbreaks are detected by recognition of similar illnesses among persons with a common exposure or by identification of case clusters through pathogen-specific surveillance. PulseNet USA has created a national framework for pathogen-specific surveillance, but no comparable effort has been made to improve surveillance of consumer complaints of suspected foodborne illness. The purpose of this study was to characterize the complaint surveillance system in Minnesota and to evaluate its use for detecting outbreaks. Minnesota Department of Health foodborne illness surveillance data from 2000 through 2006 were analyzed for this study. During this period, consumer complaint surveillance led to detection of 79% of confirmed foodborne outbreaks. Most norovirus infection outbreaks were detected through complaints. Complaint surveillance also directly led or contributed to detection of 25% of salmonellosis outbreaks. Eighty-one percent of complainants did not seek medical attention. The number of ill persons in a complainant's party was significantly associated with a complaint ultimately resulting in identification of a foodborne outbreak. Outbreak confirmation was related to a complainant's ability to identify a common exposure and was likely related to the process by which the Minnesota Department of Health chooses complaints to investigate. A significant difference (P < 0.001) was found in incubation periods between complaints that were outbreak associated (median, 27 h) and those that were not outbreak associated (median, 6 h). Complaint systems can be used to detect outbreaks caused by a variety of pathogens. Case detection for foodborne disease surveillance in Minnesota happens through a multitude of mechanisms. The ability to integrate these mechanisms and carry out rapid investigations leads to improved outbreak detection.

scholarly journals Real-time monitoring of COVID-19 dynamics using automated trend fitting and anomaly detection

2021 ◽  
Vol 376 (1829) ◽  
pp. 20200266
Author(s):  
Thibaut Jombart ◽  
Stéphane Ghozzi ◽  
Dirk Schumacher ◽  
Timothy J. Taylor ◽  
Quentin J. Leclerc ◽  
...  
Keyword(s):  
Disease Surveillance ◽  
Large Scale ◽  
R Package ◽  
Detection Algorithm ◽  
Outbreak Detection ◽  
Main Trend ◽  
Objective Criterion ◽  
Fitting Procedure ◽  
Data Points ◽  
The Uk

As several countries gradually release social distancing measures, rapid detection of new localized COVID-19 hotspots and subsequent intervention will be key to avoiding large-scale resurgence of transmission. We introduce ASMODEE (automatic selection of models and outlier detection for epidemics), a new tool for detecting sudden changes in COVID-19 incidence. Our approach relies on automatically selecting the best (fitting or predicting) model from a range of user-defined time series models, excluding the most recent data points, to characterize the main trend in an incidence. We then derive prediction intervals and classify data points outside this interval as outliers, which provides an objective criterion for identifying departures from previous trends. We also provide a method for selecting the optimal breakpoints, used to define how many recent data points are to be excluded from the trend fitting procedure. The analysis of simulated COVID-19 outbreaks suggests ASMODEE compares favourably with a state-of-art outbreak-detection algorithm while being simpler and more flexible. As such, our method could be of wider use for infectious disease surveillance. We illustrate ASMODEE using publicly available data of National Health Service (NHS) Pathways reporting potential COVID-19 cases in England at a fine spatial scale, showing that the method would have enabled the early detection of the flare-ups in Leicester and Blackburn with Darwen, two to three weeks before their respective lockdown. ASMODEE is implemented in the free R package trendbreaker . This article is part of the theme issue ‘Modelling that shaped the early COVID-19 pandemic response in the UK’.

scholarly journals A real-time spatio-temporal syndromic surveillance system with application to small companion animals

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Alison C. Hale ◽  
Fernando Sánchez-Vizcaíno ◽  
Barry Rowlingson ◽  
Alan D. Radford ◽  
Emanuele Giorgi ◽  
...  
Keyword(s):  
Real Time ◽  
Surveillance System ◽  
Syndromic Surveillance ◽  
Gastrointestinal Disease ◽  
Companion Animals ◽  
Reporting Rate ◽  
Outbreak Detection ◽  
Syndromic Surveillance System ◽  
Early Detection Of Disease ◽  
Spatio Temporal

AbstractLack of disease surveillance in small companion animals worldwide has contributed to a deficit in our ability to detect and respond to outbreaks. In this paper we describe the first real-time syndromic surveillance system that conducts integrated spatio-temporal analysis of data from a national network of veterinary premises for the early detection of disease outbreaks in small animals. We illustrate the system’s performance using data relating to gastrointestinal disease in dogs and cats. The data consist of approximately one million electronic health records for dogs and cats, collected from 458 UK veterinary premises between March 2014 and 2016. For this illustration, the system predicts the relative reporting rate of gastrointestinal disease amongst all presentations, and updates its predictions as new data accrue. The system was able to detect simulated outbreaks of varying spatial geometry, extent and severity. The system is flexible: it generates outcomes that are easily interpretable; the user can set their own outbreak detection thresholds. The system provides the foundation for prompt detection and control of health threats in companion animals.

scholarly journals Influence of infectious disease seasonality on the performance of the outbreak detection algorithm in the China Infectious Disease Automated-alert and Response System

2017 ◽  
Vol 46 (1) ◽  
pp. 98-106 ◽  
Author(s):  
Ruiping Wang ◽  
Yonggen Jiang ◽  
Xiaoqin Guo ◽  
Yiling Wu ◽  
Genming Zhao
Keyword(s):  
Infectious Disease ◽  
Detection Algorithm ◽  
Outbreak Detection ◽  
Epidemic Season ◽  
Evaluation Indexes ◽  
Response System ◽  
Disease Outbreak Detection ◽  
Time To Detection ◽  
Alert Threshold ◽  
Early Alert

Objective The Chinese Center for Disease Control and Prevention developed the China Infectious Disease Automated-alert and Response System (CIDARS) in 2008. The CIDARS can detect outbreak signals in a timely manner but generates many false-positive signals, especially for diseases with seasonality. We assessed the influence of seasonality on infectious disease outbreak detection performance. Methods Chickenpox surveillance data in Songjiang District, Shanghai were used. The optimized early alert thresholds for chickenpox were selected according to three algorithm evaluation indexes: sensitivity (Se), false alarm rate (FAR), and time to detection (TTD). Performance of selected proper thresholds was assessed by data external to the study period. Results The optimized early alert threshold for chickenpox during the epidemic season was the percentile P65, which demonstrated an Se of 93.33%, FAR of 0%, and TTD of 0 days. The optimized early alert threshold in the nonepidemic season was P50, demonstrating an Se of 100%, FAR of 18.94%, and TTD was 2.5 days. The performance evaluation demonstrated that the use of an optimized threshold adjusted for seasonality could reduce the FAR and shorten the TTD. Conclusions Selection of optimized early alert thresholds based on local infectious disease seasonality could improve the performance of the CIDARS.

scholarly journals A Participatory System for Preventing Pandemics of Animal Origins: Pilot Study of the Participatory One Health Disease Detection (PODD) System (Preprint)

2017 ◽  
Author(s):  
Terdsak Yano ◽  
Somphorn Phornwisetsirikun ◽  
Patipat Susumpow ◽  
Surasing Visrutaratna ◽  
Karoon Chanachai ◽  
...  
Keyword(s):  
Local Governments ◽  
Surveillance System ◽  
Disease Surveillance ◽  
One Health ◽  
Disease Outbreaks ◽  
Human Diseases ◽  
Disease Detection ◽  
Outbreak Control ◽  
Cattle Disease ◽  
Abnormal Events

BACKGROUND Aiming for early disease detection and prompt outbreak control, digital technology with a participatory One Health approach was used to create a novel disease surveillance system called Participatory One Health Disease Detection (PODD). PODD is a community-owned surveillance system that collects data from volunteer reporters; identifies disease outbreak automatically; and notifies the local governments (LGs), surrounding villages, and relevant authorities. This system provides a direct and immediate benefit to the communities by empowering them to protect themselves. OBJECTIVE The objective of this study was to determine the effectiveness of the PODD system for the rapid detection and control of disease outbreaks. METHODS The system was piloted in 74 LGs in Chiang Mai, Thailand, with the participation of 296 volunteer reporters. The volunteers and LGs were key participants in the piloting of the PODD system. Volunteers monitored animal and human diseases, as well as environmental problems, in their communities and reported these events via the PODD mobile phone app. LGs were responsible for outbreak control and provided support to the volunteers. Outcome mapping was used to evaluate the performance of the LGs and volunteers. RESULTS LGs were categorized into one of the 3 groups based on performance: A (good), B (fair), and C (poor), with the majority (46%,34/74) categorized into group B. Volunteers were similarly categorized into 4 performance groups (A-D), again with group A showing the best performance, with the majority categorized into groups B and C. After 16 months of implementation, 1029 abnormal events had been reported and confirmed to be true reports. The majority of abnormal reports were sick or dead animals (404/1029, 39.26%), followed by zoonoses and other human diseases (129/1029, 12.54%). Many potentially devastating animal disease outbreaks were detected and successfully controlled, including 26 chicken high mortality outbreaks, 4 cattle disease outbreaks, 3 pig disease outbreaks, and 3 fish disease outbreaks. In all cases, the communities and animal authorities cooperated to apply community contingency plans to control these outbreaks, and community volunteers continued to monitor the abnormal events for 3 weeks after each outbreak was controlled. CONCLUSIONS By design, PODD initially targeted only animal diseases that potentially could emerge into human pandemics (eg, avian influenza) and then, in response to community needs, expanded to cover human health and environmental health issues.

scholarly journals A multi-data source surveillance system to detect a bioterrorism attack during the G8 Summit in Scotland

2007 ◽  
Vol 136 (7) ◽  
pp. 876-885 ◽  
Author(s):  
N. MEYER ◽  
J. McMENAMIN ◽  
C. ROBERTSON ◽  
M. DONAGHY ◽  
G. ALLARDICE ◽  
...  
Keyword(s):  
Surveillance System ◽  
Disease Outbreaks ◽  
Detection Algorithm ◽  
Syndromic Surveillance System ◽  
Detection Algorithms ◽  
Data Source ◽  
Telephone Calls ◽  
Real Time Identification ◽  
Log Linear ◽  
Traditional Surveillance

SUMMARYIn 18 weeks, Health Protection Scotland (HPS) deployed a syndromic surveillance system to early-detect natural or intentional disease outbreaks during the G8 Summit 2005 at Gleneagles, Scotland. The system integrated clinical and non-clinical datasets. Clinical datasets included Accident & Emergency (A&E) syndromes, and General Practice (GPs) codes grouped into syndromes. Non-clinical data included telephone calls to a nurse helpline, laboratory test orders, and hotel staff absenteeism. A cumulative sum-based detection algorithm and a log-linear regression model identified signals in the data. The system had a fax-based track for real-time identification of unusual presentations. Ninety-five signals were triggered by the detection algorithms and four forms were faxed to HPS. Thirteen signals were investigated. The system successfully complemented a traditional surveillance system in identifying a small cluster of gastroenteritis among the police force and triggered interventions to prevent further cases.

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