scholarly journals Approaches to the evaluation of outbreak detection methods

2006 ◽  
Vol 6 (1) ◽  
Author(s):  
Rochelle E Watkins ◽  
Serryn Eagleson ◽  
Robert G Hall ◽  
Lynne Dailey ◽  
Aileen J Plant
Keyword(s):  
Outbreak Detection ◽  
Detection Methods

scholarly journals Acute Infectious Gastroenteritis: The Causative Agents, Omics-Based Detection of Antigens and Novel Biomarkers

Children ◽  
2021 ◽  
Vol 8 (12) ◽  
pp. 1112
Author(s):  
Haziqah Hasan ◽  
Nor Ashika Nasirudeen ◽  
Muhammad Alif Farhan Ruzlan ◽  
Muhammad Aiman Mohd Jamil ◽  
Noor Akmal Shareela Ismail ◽  
...  
Keyword(s):  
Drug Susceptibility ◽  
Outbreak Detection ◽  
Detection Methods ◽  
Clinical Workflow ◽  
Omics Technologies ◽  
Infectious Gastroenteritis ◽  
Causative Agents ◽  
Prediction Of Prognosis ◽  
Hospital Acquired ◽  
Or Genes

Acute infectious gastroenteritis (AGE) is among the leading causes of mortality in children less than 5 years of age worldwide. There are many causative agents that lead to this infection, with rotavirus being the commonest pathogen in the past decade. However, this trend is now being progressively replaced by another agent, which is the norovirus. Apart from the viruses, bacteria such as Salmonella and Escherichia coli and parasites such as Entamoeba histolytica also contribute to AGE. These agents can be recognised by their respective biological markers, which are mainly the specific antigens or genes to determine the causative pathogen. In conjunction to that, omics technologies are currently providing crucial insights into the diagnosis of acute infectious gastroenteritis at the molecular level. Recent advancement in omics technologies could be an important tool to further elucidate the potential causative agents for AGE. This review will explore the current available biomarkers and antigens available for the diagnosis and management of the different causative agents of AGE. Despite the high-priced multi-omics approaches, the idea for utilization of these technologies is to allow more robust discovery of novel antigens and biomarkers related to management AGE, which eventually can be developed using easier and cheaper detection methods for future clinical setting. Thus, prediction of prognosis, virulence and drug susceptibility for active infections can be obtained. Case management, risk prediction for hospital-acquired infections, outbreak detection, and antimicrobial accountability are aimed for further improvement by integrating these capabilities into a new clinical workflow.

scholarly journals Comparison of Molecular Subtyping and Antimicrobial Resistance Detection Methods Used in a Large Multistate Outbreak of Extensively Drug-Resistant Campylobacter jejuni Infections Linked to Pet Store Puppies

2020 ◽  
Vol 58 (10) ◽  
Author(s):  
Lavin A. Joseph ◽  
Louise K. Francois Watkins ◽  
Jessica Chen ◽  
Kaitlin A. Tagg ◽  
Christy Bennett ◽  
...  
Keyword(s):  
Antimicrobial Resistance ◽  
Campylobacter Jejuni ◽  
Multilocus Sequence Typing ◽  
The United States ◽  
Sequence Type ◽  
Outbreak Detection ◽  
Detection Methods ◽  
Drug Resistant ◽  
Molecular Subtyping ◽  
Content Type

ABSTRACT Campylobacter jejuni is a leading cause of enteric bacterial illness in the United States. Traditional molecular subtyping methods, such as pulsed-field gel electrophoresis (PFGE) and 7-gene multilocus sequence typing (MLST), provided limited resolution to adequately identify C. jejuni outbreaks and separate out sporadic isolates during outbreak investigations. Whole-genome sequencing (WGS) has emerged as a powerful tool for C. jejuni outbreak detection. In this investigation, 45 human and 11 puppy isolates obtained during a 2016–2018 outbreak linked to pet store puppies were sequenced. Core genome multilocus sequence typing (cgMLST) and high-quality single nucleotide polymorphism (hqSNP) analysis of the sequence data separated the isolates into the same two clades containing minor within-clade differences; however, cgMLST analysis does not require selection of an appropriate reference genome, making the method preferable to hqSNP analysis for Campylobacter surveillance and cluster detection. The isolates were classified as sequence type 2109 (ST2109)—a rarely seen MLST sequence type. PFGE was performed on 38 human and 10 puppy isolates; PFGE patterns did not reliably predict clustering by cgMLST analysis. Genetic detection of antimicrobial resistance determinants predicted that all outbreak-associated isolates would be resistant to six drug classes. Traditional antimicrobial susceptibility testing (AST) confirmed a high correlation between genotypic and phenotypic antimicrobial resistance determinations. WGS analysis linked C. jejuni isolates in humans and pet store puppies even when canine exposure information was unknown, aiding the epidemiological investigation during the outbreak. WGS data were also used to quickly identify the highly drug-resistant profile of these outbreak-associated C. jejuni isolates.

scholarly journals Automated use of WHONET and SaTScan to detect outbreaks of Shigella spp. using antimicrobial resistance phenotypes

2009 ◽  
Vol 138 (6) ◽  
pp. 873-883 ◽  
Author(s):  
J. STELLING ◽  
W. K. YIH ◽  
M. GALAS ◽  
M. KULLDORFF ◽  
M. PICHEL ◽  
...  
Keyword(s):  
Antimicrobial Resistance ◽  
Disease Surveillance ◽  
Laboratory Data ◽  
Disease Outbreak ◽  
Outbreak Detection ◽  
Detection Methods ◽  
Scan Statistic ◽  
National Network ◽  
Disease Outbreak Detection ◽  
Shigella Spp

SUMMARYAntimicrobial resistance is a priority emerging public health threat, and the ability to detect promptly outbreaks caused by resistant pathogens is critical for resistance containment and disease control efforts. We describe and evaluate the use of an electronic laboratory data system (WHONET) and a space–time permutation scan statistic for semi-automated disease outbreak detection. In collaboration with WHONET-Argentina, the national network for surveillance of antimicrobial resistance, we applied the system to the detection of local and regional outbreaks of Shigella spp. We searched for clusters on the basis of genus, species, and resistance phenotype and identified 19 statistical ‘events’ in a 12-month period. Of the six known outbreaks reported to the Ministry of Health, four had good or suggestive agreement with SaTScan-detected events. The most discriminating analyses were those involving resistance phenotypes. Electronic laboratory-based disease surveillance incorporating statistical cluster detection methods can enhance infectious disease outbreak detection and response.

scholarly journals The importance of age-specific data in routine syndromic surveillance

2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Roger Morbey ◽  
Alex J. Elliot ◽  
Gillian E. Smith
Keyword(s):  
Public Health ◽  
Hospital Admissions ◽  
Age Distribution ◽  
Age Groups ◽  
Health Indicators ◽  
Older Age ◽  
Outbreak Detection ◽  
Detection Methods ◽  
Surveillance Systems ◽  
Wide Range

ObjectiveTo investigate whether aberration detection methods for syndromicsurveillance would be more useful if data were stratified by age band.IntroductionWhen monitoring public health incidents using syndromicsurveillance systems, Public Health England (PHE) uses the ageof the presenting patient as a key indicator to further assess theseverity, impact of the incident, and to provide intelligence on thelikely cause. However the age distribution of cases is usually notconsidered until after unusual activity has been identified in the all-ages population data. We assessed whether monitoring specific agegroups contemporaneously could improve the timeliness, specificityand sensitivity of public health surveillance.MethodsFirst, we examined a wide range of health indicators from the PHEsyndromic surveillance systems to identify for further study thosewith the greatest seasonal variation in the age distribution of cases.Secondly, we examined the identified indicators to ascertain whetherany age bands consistently lagged behind other age bands. Finally,we applied outbreak detection methods retrospectively to age specificdata, identifying periods of increased activity that were only detectedor detected earlier when age-specific surveillance was used.ResultsSeasonal increases in respiratory indicators occurred first inyounger age groups, with increases in children under 5 providingearly warning of subsequent increases occurring in older age groups.Also, we found age specific indicators improved the specificity ofsurveillance using indicators relating to respiratory and eye problems;identifying unusual activity that was less apparent in the all-agespopulation.ConclusionsRoutine surveillance of respiratory indicators in young childrenwould have provided early warning of increases in older age groups,where the burden on health care usage, e.g. hospital admissions, isgreatest. Furthermore this cross-correlation between ages occurredconsistently even though the age distribution of the burden ofrespiratory cases varied between seasons. Age specific surveillancecan improve sensitivity of outbreak detection although all-agesurveillance remains more powerful when case numbers are low.

scholarly journals A regional approach for the influenza surveillance in France

2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Marc Ruello ◽  
Camille Pelat ◽  
Céline Caserio-Schönemann ◽  
Anne Fouillet ◽  
Isabelle Bonmarin ◽  
...  
Keyword(s):  
Public Health ◽  
Health Care ◽  
National Level ◽  
Health Agency ◽  
Data Sources ◽  
Outbreak Detection ◽  
Detection Methods ◽  
Influenza Surveillance ◽  
Time Step ◽  
Statistical Results

ObjectiveTo describe the results of the new organization of influenzasurveillance in France, based on a regional approach.IntroductionIn France, until winter 2014-2015, management and preventiveactions for the control of the flu epidemic were implemented whenthe national incidence of influenza-like illness (ILI) consultationsin general practice was over an epidemic threshold. The 2014-2015influenza epidemic had a major public health impact, particularly inthe elderly, and caused a severe overloading of the health care system,in particular emergency departments (ED) [1]. The epidemic alertemitted by the French National Public Health Agency at the nationallevel was too late for the hospitals to prepare themselves in manyregions.After a national feedback organized in April 2015 with allpartners involved in influenza surveillance and management, it wasrecommended to improve influenza surveillance in France following3 axes: 1) regionalize surveillance so that healthcare structures canadapt to the particular situation of their region; 2) use a pre-epidemicalert level for better anticipating the outbreak; 3) use multiple datasources and multiple outbreak detection methods to strengthen thedetermination of influenza alert level.MethodsA user-friendly web application was developed to provide commondata visualizations and statistical results of outbreak detectionmethods to all the epidemiologists involved in influenza surveillanceat the national level or in the 15 regional units of our agency [2].It relies on 3 data sources, aggregated on a weekly time step: 1) theproportion of ILI among all coded attendances in the ED participatingto the OSCOUR Network [3] ; 2) the proportion of ILI among allcoded visits made by emergency general practitioners (GPs) workingin the SOS Médecins associations [3]; 3) the incidence rate of ILIestimated from a sample of sentinel GPs [4].For each region each week, 3 statistical outbreak detection methodswere applied to the 3 data sources, generating 9 results that werecombined to obtain a weekly regional influenza alarm level. Basedon this alarm level and on other information (e.g.virological data),the epidemiologists then determined the epidemiological status ofeach region as either 1) epidemic-free, 2) in pre/post epidemic or 3)epidemic.The R software was used for programming algorithms and buildingthe web interface (package shiny).ResultsThe epidemiological status of influenza at the regional level wascommunicated through maps published in the weekly influenzareports of the Agency throughout the surveillance season [5].In week 2016-W03, Brittany was the first French region to declarethe influenza epidemic, with nine other regions in pre-epidemic alert.The epidemic then spread over the whole mainland territory. The peakof the epidemic was declared in week 11, the end in week 16.ConclusionsThis regional multi-source approach has been made possible bythe sharing of data visualizations and statistical results through a webapplication. This application helped detecting early the epidemicstart and allowed a reactive communication with the regionalhealth authorities in charge of the organization of health care, themanagement and the setting up of the appropriate preventivemeasures.

scholarly journals Enhancing Epidemic Detection Using Syndromic Surveillance and Early Notification Methods

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Tippa Wongstitwilairoong ◽  
Saranath Lawpoolsri Niyom ◽  
Ngamphol Soonthornworasiri ◽  
Jariyanart Gaywee ◽  
Jaranit Kaewkungwal
Keyword(s):  
Early Warning ◽  
Predictive Value ◽  
Syndromic Surveillance ◽  
Detection Method ◽  
Disease Outbreaks ◽  
High Sensitivity ◽  
Disease Outbreak ◽  
Surveillance Data ◽  
Outbreak Detection ◽  
Detection Methods

ObjectiveThis paper presents an investigation using early notification methods to enhancing epidemic detection in syndromic surveillance data from royal Thai army in Thailand.IntroductionEarly Notification Detection Systems have taken a critical role in providing early notice of disease outbreaks. To improve the detection methods for disease outbreaks, many detection methods have been created and implemented. However, there is limited information on the effectively of syndromic surveillance in Thailand. Knowing the performance, strengths and weakness of these surveillance systems in providing early warning for outbreaks will increase disease outbreak detection capacity in Thailand.MethodsThis study describes and compares the capabilities of various outbreak detection algorithms using 37,043 unique syndromic daily reports based on medical information from both civilian and military personnel from the Unit Base Surveillance of Royal Thai Army (RTA) along the Thai-Myanmar and Thai-Cambodia boarder areas. Traditional epidemic detection method: mean plus two SD were compared with algorithms for early notification methods and which included regression, regression/EWMA/Poisson, CDC-C1, CDC-C2 and CDC-C3. Early notification and epidemic detection methods were compared according to their ability to generate alert notifications. Sensitivity, specificity, positive predictive value (PPV), negative predictive value and overall accuracy to detect or predict disease outbreaks were estimated.ResultsThis study shows that the preliminary results are promising for epidemic detection by early notification methods in syndromic surveillance in Thailand. The majority of syndromic records were categorized into 12 symptoms. The three most common symptoms were respiratory, fever and gastrointestinal illness (11,501; 9,549 and 4,498 respectively). The results from the early notification systems were analyzed and their performances were compared with traditional epidemic detection method according to their ability to generate early warning alerts for the 3 symptoms. In our study regression/EWMA/Poisson method had higher specificity across the 3 symptoms (94.5%, 94.7% and 95.9% respectively), but generated lower sensitivity (22.6%, 40.4% and 23.1%). CDC-C1, CDC-C2 and CDC-C3 algorithms are easy to understand and are widely used. CDC-C3 had higher sensitivity to detect gradual disease outbreak effects (64.2%, 70.2% and 57.7%), but it is known to produce higher alarm rates/false positive signals.ConclusionsWithin the syndromic surveillance data of RTA, the CDC algorithm is the best chosen to use in the syndromic system due to being easy to understand and implement in a system with high sensitivity. CDC-C2 is the best early notification detection method due to its high sensitivity and PPV. However, CDC-C3 is shows the highest sensitivity, but exhibits the lowest specificity and PPV for all symptoms including a high alarm rates. To be useful, early notification detection methods must have acceptable operating characteristics. Consequently, we should select the most appropriate algorithm method to explain the data well and in order to improve detection of outbreaks. The comparison methods used in this study may be useful for testing other proposed alert threshold methods and may have further applications for other populations and other diseases.References1. Chretien JP, Burkom HS, Sedyaningsih ER, Larasati RP, et al. Syndromic Surveillance: Adapting Innovations to Developing Settings. PLoS Medicine 2008; vol 5: page 1-6.2. Burkom HS, Elbert Y, Magruder SF, Najmi AH, Peter W, Thompson MW. Developments in the roles, features, and evaluation of alerting algorithms for disease outbreak monitoring. Johns Hopkins APL Technical Digest 2008; vol 27: page 313.

scholarly journals Comparison of Two Signal Detection Methods in a Coroner-Based System for near Real-Time Mortality Surveillance

2007 ◽  
Vol 122 (4) ◽  
pp. 521-530 ◽  
Author(s):  
Matthew R. Groenewold
Keyword(s):  
Signal Detection ◽  
Real Time ◽  
Predictive Value ◽  
Outbreak Detection ◽  
Detection Methods ◽  
Surveillance Systems ◽  
Epidemic Threshold ◽  
Threshold Method ◽  
Advantages And Disadvantages ◽  
Mortality Surveillance

Objectives. This article describes and compares the performance characteristics of two approaches to outbreak detection in the context of a coroner-based mortality surveillance system using controlled feature set simulation. Methods. The comparative capabilities of the outbreak detection methods— the Epidemic Threshold and Cusum methods—were assessed by introducing a series of simulated signals, configured as nonoverlapping, three-day outbreaks, into historic surveillance data and assessing their respective performances. Treating each calendar day as a separate observation, sensitivity, predictive value positive, and predictive value negative were calculated for both signal detection methods at various outbreak magnitudes. Their relative performances were also assessed in terms of the overall percentage of outbreaks detected. Results. Both methods exhibited low sensitivity for small outbreaks and low to moderate sensitivity for larger ones. In terms of overall outbreak detection, large outbreaks were detected with moderate to high levels of reliability, while smaller ones were detected with low to moderate reliability for both methods. The Epidemic Threshold method performed significantly better than the Cusum method for overall outbreak detection. Conclusions. The use of coroner data for mortality surveillance has both advantages and disadvantages, the chief advantage being the rapid availability of coroner data compared to vital statistics data, making near real-time mortality surveillance possible. Given the lack of sensitivity and limited outbreak detection reliability of the methods studied, the use of mortality surveillance for early outbreak detection appears to have limited usefulness. If it is used, it should be as an adjuvant in conjunction with other surveillance systems.

scholarly journals Improving regional influenza surveillance through a combination of automated outbreak detection methods: the 2015/16 season in France

2017 ◽  
Vol 22 (32) ◽  
Author(s):  
Camille Pelat ◽  
Isabelle Bonmarin ◽  
Marc Ruello ◽  
Anne Fouillet ◽  
Céline Caserio-Schönemann ◽  
...  
Keyword(s):  
Public Health ◽  
General Practitioners ◽  
National Level ◽  
Health Agency ◽  
Public Health Agency ◽  
Data Sources ◽  
Outbreak Detection ◽  
Detection Methods ◽  
Influenza Epidemic ◽  
National Public Health

The 2014/15 influenza epidemic caused a work overload for healthcare facilities in France. The French national public health agency announced the start of the epidemic – based on indicators aggregated at the national level – too late for many hospitals to prepare. It was therefore decided to improve the influenza alert procedure through (i) the introduction of a pre-epidemic alert level to better anticipate future outbreaks, (ii) the regionalisation of surveillance so that healthcare structures can be informed of the arrival of epidemics in their region, (iii) the standardised use of data sources and statistical methods across regions. A web application was developed to deliver statistical results of three outbreak detection methods applied to three surveillance data sources: emergency departments, emergency general practitioners and sentinel general practitioners. This application was used throughout the 2015/16 influenza season by the epidemiologists of the headquarters and regional units of the French national public health agency. It allowed them to signal the first influenza epidemic alert in week 2016-W03, in Brittany, with 11 other regions in pre-epidemic alert. This application received positive feedback from users and was pivotal for coordinating surveillance across the agency’s regional units.

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