scholarly journals Vetsyn: Veterinary Syndromic Surveillance Streamlined into one R Package

2015 ◽  
Vol 7 (1) ◽  
Author(s):  
Fernanda C. Dórea ◽  
Stefan Widgrén ◽  
Ann Lindberg
Keyword(s):  
Laboratory Test ◽  
Signal Detection ◽  
Data Streams ◽  
Syndromic Surveillance ◽  
Historical Data ◽  
R Package ◽  
Surveillance Systems ◽  
Detection Algorithms ◽  
Set Up ◽  
Monitor Data

We describe an R package that was designed to provide ready implementation of veterinary syndromic surveillance systems, from classified data to the generation of alerts. The development of the package was informed by the experience developing two systems for veterinary syndromic surveillance based on laboratory test requests. Functions are available to carry out retrospective analyses of the data available; produce an outbreak-free baseline from historical data; monitor data streams prospectively with a combination of various temporal outbreak-signal detection algorithms; set up automated email alerts in case of alarms; and set up an html interface for the system.

scholarly journals Infectious disease surveillance for the London 2012 Olympic and Paralympic Games

2012 ◽  
Vol 17 (31) ◽  
Author(s):  
E Severi ◽  
E Heinsbroek ◽  
C Watson ◽  
M Catchpole ◽  
Collective HPA Olympics Surveillance Work Group
Keyword(s):  
Infectious Disease ◽  
Disease Surveillance ◽  
Syndromic Surveillance ◽  
British History ◽  
Surveillance Systems ◽  
Unscheduled Care ◽  
London 2012 ◽  
Laboratory Surveillance ◽  
Set Up ◽  
Paralympic Games

The London 2012 Olympic and Paralympic Games will be one of the largest mass gathering events in British history. In order to minimise potential infectious disease threats related to the event, the Health Protection Agency (HPA) has set up a suite of robust and multi-source surveillance systems. These include enhancements of already established systems (notification of infectious diseases, local and regional reporting, laboratory surveillance, mortality surveillance, international surveillance, and syndromic surveillance in primary care), as well as new systems created for the Games (syndromic surveillance in emergency departments and out-of-hours/unscheduled care, undiagnosed serious infectious illness surveillance). Enhanced existing and newly established surveillance systems will continue after the Games or will be ready for future reactivation should the need arise. In addition to the direct improvements to surveillance, the strengthening of relationships with national and international stakeholders will constitute a major post-Games legacy for the HPA.

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 Evaluating Syndromic Surveillance Systems

2016 ◽  
Vol 8 (1) ◽  
Author(s):  
Iain Lake ◽  
Felipe J. Colón-González ◽  
Roger Morbey ◽  
Alex J. Elliot ◽  
Gillian E. Smith ◽  
...  
Keyword(s):  
Empirical Analysis ◽  
Pandemic Influenza ◽  
Data Streams ◽  
Syndromic Surveillance ◽  
Evaluation Framework ◽  
Surveillance Data ◽  
Surveillance Systems ◽  
Time To Detection

Syndromic surveillance systems are commonly presented in the literature but few are rigorously evaluated. We present and test an evaluation framework to examine which events can and cannot be detected, the time to detection and the efficacy of different syndromic surveillance data streams. This was achieved using four national syndromic surveillance systems in England and simulating a number of possible disease events (e.g. outbreak of pandemic influenza, (Cryptosporidium) outbreak and deliberate anthrax release). This methodology can be widely adopted to provide more empirical analysis of the effectiveness of syndromic surveillance systems worldwide.

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

10.2196/11512 ◽  
2020 ◽  
Vol 6 (2) ◽  
pp. e11512 ◽  
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 Comparison of statistical algorithms for daily syndromic surveillance aberration detection

2019 ◽  
Vol 35 (17) ◽  
pp. 3110-3118
Author(s):  
Angela Noufaily ◽  
Roger A Morbey ◽  
Felipe J Colón-González ◽  
Alex J Elliot ◽  
Gillian E Smith ◽  
...  
Keyword(s):  
Public Health ◽  
Syndromic Surveillance ◽  
Performance Metrics ◽  
High Specificity ◽  
Outbreak Detection ◽  
Supplementary Information ◽  
Surveillance Systems ◽  
Detection Algorithms ◽  
Aberration Detection ◽  
Wide Range

Abstract Motivation Public health authorities can provide more effective and timely interventions to protect populations during health events if they have effective multi-purpose surveillance systems. These systems rely on aberration detection algorithms to identify potential threats within large datasets. Ensuring the algorithms are sensitive, specific and timely is crucial for protecting public health. Here, we evaluate the performance of three detection algorithms extensively used for syndromic surveillance: the ‘rising activity, multilevel mixed effects, indicator emphasis’ (RAMMIE) method and the improved quasi-Poisson regression-based method known as ‘Farrington Flexible’ both currently used at Public Health England, and the ‘Early Aberration Reporting System’ (EARS) method used at the US Centre for Disease Control and Prevention. We model the wide range of data structures encountered within the daily syndromic surveillance systems used by PHE. We undertake extensive simulations to identify which algorithms work best across different types of syndromes and different outbreak sizes. We evaluate RAMMIE for the first time since its introduction. Performance metrics were computed and compared in the presence of a range of simulated outbreak types that were added to baseline data. Results We conclude that amongst the algorithm variants that have a high specificity (i.e. >90%), Farrington Flexible has the highest sensitivity and specificity, whereas RAMMIE has the highest probability of outbreak detection and is the most timely, typically detecting outbreaks 2–3 days earlier. Availability and implementation R codes developed for this project are available through https://github.com/FelipeJColon/AlgorithmComparison Supplementary information Supplementary data are available at Bioinformatics online.

Construction of syndromic surveillance using a web-based daily questionnaire for health and its application at the G8 Hokkaido Toyako Summit meeting

2010 ◽  
Vol 138 (10) ◽  
pp. 1493-1502 ◽  
Author(s):  
H. SUGIURA ◽  
Y. OHKUSA ◽  
M. AKAHANE ◽  
T. SUGAHARA ◽  
N. OKABE ◽  
...  
Keyword(s):  
Surveillance System ◽  
Syndromic Surveillance ◽  
Health Surveillance ◽  
Health Conditions ◽  
Surveillance Systems ◽  
Web Based ◽  
Syndromic Surveillance System ◽  
Summit Meeting ◽  
Local Residents ◽  
Set Up

SUMMARYWe constructed a syndromic surveillance system to collect directly information on daily health conditions directly from local residents via the internet [web-based daily questionnaire for health surveillance system (WDQH SS)]. This paper considers the feasibility of the WDQH SS and its ability to detect epidemics. A verification study revealed that our system was an effective surveillance system. We then applied an improved WDQH SS as a measure against public health concerns at the G8 Hokkaido Toyako Summit meeting in 2008. While in operation at the Summit, our system reported a fever alert that was consistent with a herpangina epidemic. The highly mobile WDQH SS described in this study has three main advantages: the earlier detection of epidemics, compared to other surveillance systems; the ability to collect data even on weekends and holidays; and a rapid system set-up that can be completed within 3 days.

scholarly journals Simulation Studies as a Tool to Understand Bayes Factors

2021 ◽  
Vol 4 (1) ◽  
pp. 251524592097262
Author(s):  
Don van Ravenzwaaij ◽  
Alexander Etz
Keyword(s):  
Simulation Study ◽  
Prior Distribution ◽  
Bayes Factor ◽  
R Package ◽  
Bayes Factors ◽  
Simulation Studies ◽  
Social Scientists ◽  
Simple Simulation ◽  
Set Up

When social scientists wish to learn about an empirical phenomenon, they perform an experiment. When they wish to learn about a complex numerical phenomenon, they can perform a simulation study. The goal of this Tutorial is twofold. First, it introduces how to set up a simulation study using the relatively simple example of simulating from the prior. Second, it demonstrates how simulation can be used to learn about the Jeffreys-Zellner-Siow (JZS) Bayes factor, a currently popular implementation of the Bayes factor employed in the BayesFactor R package and freeware program JASP. Many technical expositions on Bayes factors exist, but these may be somewhat inaccessible to researchers who are not specialized in statistics. In a step-by-step approach, this Tutorial shows how a simple simulation script can be used to approximate the calculation of the Bayes factor. We explain how a researcher can write such a sampler to approximate Bayes factors in a few lines of code, what the logic is behind the Savage-Dickey method used to visualize Bayes factors, and what the practical differences are for different choices of the prior distribution used to calculate Bayes factors.

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