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 Using Ambulatory Syndromic Surveillance Data for Chronic Disease: A BMI Case Study

2015 ◽  
Vol 7 (1) ◽  
Author(s):  
Andrew Walsh
Keyword(s):  
Syndromic Surveillance ◽  
Disease Outbreak ◽  
Surveillance Data ◽  
Outbreak Detection ◽  
Infectious Disease Outbreak ◽  
Disease Outbreak Detection ◽  
Health Domains ◽  
Ambulatory Practice ◽  
Practice Care

Ambulatory practice syndromic surveillance data needs to demonstrate utility beyond infectious disease outbreak detection to warrant integration into existing systems. The nature of ambulatory practice care makes it well suited for monitoring health domains not covered by emergency departments. This project demonstrates collection of height and weight measurements from ambulatory practice syndromic surveillance data. These data are used to calculate patient BMI, an important risk factor for many chronic diseases. This work is presented as a proof-of-principle for applying syndromic surveillance data to additional health domains.

scholarly journals Unknown Disease Outbreaks Detection: A Pilot Study on Feature-Based Knowledge Representation and Reasoning Model

2021 ◽  
Vol 9 ◽  
Author(s):  
Rui Feng ◽  
Qiping Hu ◽  
Yingan Jiang
Keyword(s):  
Knowledge Representation ◽  
Syndromic Surveillance ◽  
Empirical Studies ◽  
Medical Knowledge ◽  
Disease Outbreaks ◽  
Early Response ◽  
Disease Outbreak ◽  
Outbreak Detection ◽  
Knowledge Representation And Reasoning ◽  
Disease Outbreak Detection

Background: The outbreak of COVID-19 in 2019 has rapidly swept the world, causing irreparable loss to human beings. The pandemic has shown that there is still a delay in the early response to disease outbreaks and needs a method for unknown disease outbreak detection. The study's objective is to establish a new medical knowledge representation and reasoning model, and use the model to explore the feasibility of unknown disease outbreak detection.Methods: The study defined abnormal values with diagnostic significances from clinical data as the Features, and defined the Features as the antecedents of inference rules to match with knowledge bases, achieved in detecting known or emerging infectious disease outbreaks. Meanwhile, the study built a syndromic surveillance base to capture the target cases' Features to improve the reliability and fault-tolerant ability of the system.Results: The study combined the method with Severe Acute Respiratory Syndrome (SARS), Middle East Respiratory Syndrome (MERS), and early COVID-19 outbreaks as empirical studies. The results showed that with suitable surveillance guidelines, the method proposed in this study was capable to detect outbreaks of SARS, MERS, and early COVID-19 pandemics. The quick matching accuracies of confirmed infection cases were 89.1, 26.3–98%, and 82%, and the syndromic surveillance base would capture the Features of the remaining cases to ensure the overall detection accuracies. Based on the early COVID-19 data in Wuhan, this study estimated that the median time of the early COVID-19 cases from illness onset to local authorities' responses could be reduced to 7.0–10.0 days.Conclusions: This study offers a new solution to transfer traditional medical knowledge into structured data and form diagnosis rules, enables the representation of doctors' logistic thinking and the knowledge transmission among different users. The results of empirical studies demonstrate that by constantly inputting medical knowledge into the system, the proposed method will be capable to detect unknown diseases from existing ones and perform an early response to the initial outbreaks.

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 Waterborne disease outbreak detection: an integrated approach using health administrative databases

2017 ◽  
Vol 15 (4) ◽  
pp. 475-489 ◽  
Author(s):  
S. Coly ◽  
N. Vincent ◽  
E. Vaissiere ◽  
M. Charras-Garrido ◽  
A. Gallay ◽  
...  
Keyword(s):  
Drinking Water ◽  
Detection Method ◽  
Tap Water ◽  
Disease Outbreaks ◽  
Integrated Approach ◽  
Developed Countries ◽  
Outbreak Detection ◽  
Administrative Databases ◽  
Waterborne Disease ◽  
Disease Outbreak Detection

Hundreds of waterborne disease outbreaks (WBDO) of acute gastroenteritis (AGI) due to contaminated tap water are reported in developed countries each year. Such outbreaks are probably under-detected. The aim of our study was to develop an integrated approach to detect and study clusters of AGI in geographical areas with homogeneous exposure to drinking water. Data for the number of AGI cases are available at the municipality level while exposure to tap water depends on drinking water networks (DWN). These two geographical units do not systematically overlap. This study proposed to develop an algorithm which would match the most relevant grouping of municipalities with a specific DWN, in order that tap water exposure can be taken into account when investigating future disease outbreaks. A space-time detection method was applied to the grouping of municipalities. Seven hundred and fourteen new geographical areas (groupings of municipalities) were obtained compared with the 1,310 municipalities and the 1,706 DWN. Eleven potential WBDO were identified in these groupings of municipalities. For ten of them, additional environmental investigations identified at least one event that could have caused microbiological contamination of DWN in the days previous to the occurrence of a reported WBDO.

scholarly journals A Bayesian Outbreak Detection Method for Influenza-Like Illness

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Yury E. García ◽  
J. Andrés Christen ◽  
Marcos A. Capistrán
Keyword(s):  
Detection Method ◽  
Research Area ◽  
Bayesian Regression ◽  
Surveillance Data ◽  
Outbreak Detection ◽  
Epidemic Outbreak ◽  
Influenza Like Illness ◽  
Active Research ◽  
Reliable Methods ◽  
Active Research Area

Epidemic outbreak detection is an important problem in public health and the development of reliable methods for outbreak detection remains an active research area. In this paper we introduce a Bayesian method to detect outbreaks of influenza-like illness from surveillance data. The rationale is that, during the early phase of the outbreak, surveillance data changes from autoregressive dynamics to a regime of exponential growth. Our method uses Bayesian model selection and Bayesian regression to identify the breakpoint. No free parameters need to be tuned. However, historical information regarding influenza-like illnesses needs to be incorporated into the model. In order to show and discuss the performance of our method we analyze synthetic, seasonal, and pandemic outbreak data.

scholarly journals How Should We Be Conducting Routine Analysis of Traditional Emergency Department Syndromic Surveillance Data?

2015 ◽  
Vol 7 (1) ◽  
Author(s):  
David Atrubin ◽  
Michael Wiese
Keyword(s):  
Emergency Department ◽  
Best Practices ◽  
International Society ◽  
Disease Surveillance ◽  
Syndromic Surveillance ◽  
Surveillance Data ◽  
Outbreak Detection ◽  
Surveillance Systems ◽  
Track Record ◽  
Periodic Disease

This roundtable will focus on how traditional emergency department syndromic surveillance systems should be used to conduct daily or periodic disease surveillance.  As outbreak detection using these systems has demonstrated an equivocal track record, epidemiologists have sought out other interesting uses for these systems.  Over the numerous years of the International Society for Disease Surveillance (ISDS) Conference, many of these studies have been presented; however, there has been a dearth of discussion related to how these systems should be used. This roundtable offers a forum to discuss best practices for the routine use of emergency department syndromic surveillance data.

Pilot simulation study using meat inspection data for syndromic surveillance: use of whole carcass condemnation of adult cattle to assess the performance of several algorithms for outbreak detection

2015 ◽  
Vol 143 (12) ◽  
pp. 2559-2569 ◽  
Author(s):  
C. DUPUY ◽  
E. MORIGNAT ◽  
F. DOREA ◽  
C. DUCROT ◽  
D. CALAVAS ◽  
...  
Keyword(s):  
Syndromic Surveillance ◽  
Negative Binomial ◽  
Moving Average ◽  
High Sensitivity ◽  
Real Data ◽  
Outbreak Detection ◽  
Adult Cattle ◽  
Inspection Data ◽  
Sensitivity Specificity ◽  
Weighted Moving Average

SUMMARYThe objective of this study was to assess the performance of several algorithms for outbreak detection based on weekly proportions of whole carcass condemnations. Data from one French slaughterhouse over the 2005–2009 period were used (177 098 slaughtered cattle, 0.97% of whole carcass condemnations). The method involved three steps: (i) preparation of an outbreak-free historical baseline over 5 years, (ii) simulation of over 100 years of baseline time series with injection of artificial outbreak signals with several shapes, durations and magnitudes, and (iii) assessment of the performance (sensitivity, specificity, outbreak detection precocity) of several algorithms to detect these artificial outbreak signals. The algorithms tested included the Shewart p chart, confidence interval of the negative binomial model, the exponentially weighted moving average (EWMA); and cumulative sum (CUSUM). The highest sensitivity was obtained using a negative binomial algorithm and the highest specificity with CUSUM or EWMA. EWMA sensitivity was too low to select this algorithm for efficient outbreak detection. CUSUM's performance was complementary to the negative binomial algorithm. The use of both algorithms on real data for a prospective investigation of the whole carcass condemnation rate as a syndromic surveillance indicator could be relevant. Shewart could also be a good option considering its high sensitivity and simplicity of implementation.

scholarly journals The Early Warning And Response System (EWARS-TDR) For Dengue Outbreaks: Can It Also Be Applied To Chikungunya And Zika Outbreak Warning?

2021 ◽  
Author(s):  
Rocio Cardenas ◽  
Laith Hussain-Alkhateeb ◽  
David Benitez-Valladares ◽  
Gustavo Sanchez-Tejeda ◽  
Axel Kroeger
Keyword(s):  
Positive Predictive Value ◽  
Early Warning ◽  
Predictive Value ◽  
Large City ◽  
High Sensitivity ◽  
Surveillance Systems ◽  
Insect Vector ◽  
Alarm Signals ◽  
Response System ◽  
Dengue Outbreaks

Abstract Background. In the Americas, endemic countries for Aedes-borne diseases such as dengue, chikungunya, and Zika face great challenges particularly since the recent outbreaks of CHIKV and ZIKV, all transmitted by the same insect vector Aedes aegypti and Ae. albopictus. The Special Program for Research and Training in Tropical Diseases (TDR- WHO) has developed together with partners an early warning and Response System (EWARS) for dengue outbreaks based on a variety of alarm signals with a high sensitivity and positive predictive value (PPV). The question is if this tool can also be used for the prediction of Zika and chikungunya outbreaks.Methodology. We conducted in nine districts of Mexico and one large city in Colombia a retrospective analysis of epidemiological data (for the outbreak definition) and of climate and entomological data (as potential alarm indicators) produced by the national surveillance systems for dengue, chikungunya and Zika outbreak prediction covering the following outbreak years: for dengue 2012-2016, for Zika 2015-2017, for chikungunya 2014-2016. This period was divided into a “run in period” (to establish the “historical” pattern of the disease) and an “analysis period” (to identify sensitivity and PPV of outbreak prediction). Results. In Mexico, the sensitivity of alarm signals for correctly predicting an outbreak was 92% for dengue, and 97% for Zika (chikungunya data could not be obtained in Mexico); the PPV was 68% for dengue and 100% for Zika. The time period between alarm and start of the outbreak (i.e. the time available for early response activities) was for dengue 6-8 weeks and for Zika 3-5 weeks. In Colombia the sensitivity of the outbreak prediction was 92% for dengue, 93% for chikungunya and 100% for Zika; the PPV was 68% for dengue, 92% for chikungunya and 54% for Zika; the prediction distance was for dengue 3-5 weeks, for chikungunya 10-13 weeks and for Zika 6-10 weeks. Conclusion. The implementation of an early warning and response system (EWARS) could predict outbreaks of three Aedes borne diseases with a high sensitivity and positive predictive value and with a lag time long enough for preparing an adequate outbreak response in order to reduce the magnitude or avert the occurrence of outbreaks with their elevated social and economic tolls.

scholarly journals Selection, Identification, and Application of Aptamers against Agaricus bisporus Lectin to Establish an Aptamer-AuNPs Colorimetric Method for Detection of ABL

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Hao Gao ◽  
Yiling Tian ◽  
Min Zhang ◽  
Jianhui Liu ◽  
Yaowu Yuan ◽  
...  
Keyword(s):  
Agaricus Bisporus ◽  
Detection Method ◽  
Colorimetric Method ◽  
High Sensitivity ◽  
Antinutritional Factors ◽  
Detection Methods ◽  
Systematic Evolution ◽  
Novel Strategy ◽  
Exponential Enrichment ◽  
Current Detection

Agaricus bisporus lectin (ABL), which is one of the antinutritional factors in A. bisporus, is an important allergen and harmful to human health. Due to the shortcomings of the current detection methods, it is extremely urgent to establish a rapid and sensitive detection method for ABL in foods. To isolate the ssDNA aptamer of ABL, 13 rounds of subtractive systematic evolution of ligands by exponential enrichment (SELEX) selection were carried out. As a result, six candidate aptamers were selected and further examined for their binding affinity and specificity by enzyme-linked aptamer method. One aptamer (seq-41) against ABL with a high affinity and specificity was isolated and demonstrated to be the optimal aptamer whose dissociation constant reaches the nanomolar level, Kd = 31.17 ± 0.1070 nM. Based on seq-41, an aptamer-AuNPs colorimetric method was established to detect ABL with a linear range of 0.08∼1.70 μg/mL and the detection limit is 0.062 μg/mL. This study provides a novel aptamer-AuNPs colorimetric method with high sensitivity and specificity for detection of ABL and a novel strategy for development of detection method of fungal or plant allergens.

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