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 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 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.

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 Automated, Laboratory-based System Using the Internet for Disease Outbreak Detection, the Netherlands

2003 ◽  
Vol 9 (9) ◽  
pp. 1046-1052 ◽  
Author(s):  
Marc-Alain Widdowson ◽  
Arnold Bosman ◽  
Edward van Straten ◽  
Mark Tinga ◽  
Sandra Chaves ◽  
...  
Keyword(s):  
The Netherlands ◽  
Disease Outbreak ◽  
Outbreak Detection ◽  
The Internet ◽  
Disease Outbreak Detection

The Transitional Medical Model: An innovative methodology for a community’s disease outbreak and pandemic preparedness and response plan

2019 ◽  
Vol 14 (4) ◽  
pp. 255-267
Author(s):  
Paul Rega, MD, FACEP ◽  
Christopher Bork, PT, PhD, EMT-B, FASAHP ◽  
Michael Bisesi, PhD, CIH ◽  
Jeffrey P. Gold, MD ◽  
Kelly Burkholder-Allen RN, MSEd
Keyword(s):  
Infectious Disease ◽  
Disaster Response ◽  
Medical Model ◽  
Disease Outbreaks ◽  
Signs And Symptoms ◽  
Early Response ◽  
Recovery Phase ◽  
Disease Outbreak ◽  
Resistant Bacteria ◽  
Infectious Disease Outbreak

Infectious disease outbreaks, epidemics, and subsequent pandemics are not typical disasters in the sense that they often lack clearly delineated phases. As in any event that is biological in nature, its onset may be gradual with signs and symptoms that are so subtle that they go unrecognized, thus missing opportunities to invoke an early response and implement containment strategies. An infectious disease outbreak--whether caused by a novel virus, a particularly virulent influenza strain, or newly emerging or resistant bacteria with the capability of human-to-human transmission--can quickly degrade a community’s healthcare infrastructure in advance of coordinated mitigation, preparation, and response activities. The Transitional Medical Model (TMM) was developed to aid communities with these crucial phases of disaster response as well as to assist with the initial steps within the recovery phase. The TMM is a methodology that provides a crosswalk between the routine operations and activities of a community’s public health infrastructure with action steps associated with the mitigation, preparedness, response, and recovery phases of an infectious disease outbreak.

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.

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