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 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

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 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 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 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.

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