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 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 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 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 Supervised Learning for Automated Infectious-Disease-Outbreak Detection

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Stephane Ghozzi ◽  
Benedikt Zacher ◽  
Alexander Ullrich
Keyword(s):  
Infectious Disease ◽  
Machine Learning ◽  
Time Series ◽  
Supervised Learning ◽  
Expert Knowledge ◽  
Disease Outbreak ◽  
Outbreak Detection ◽  
Infectious Disease Outbreak ◽  
Count Time Series ◽  
Disease Outbreak Detection

ObjectiveBy systematically scoring algorithms and integrating outbreak data through statistical learning, evaluate and improve the performance of automated infectious-disease-outbreak detection. The improvements should be directly relevant to the epidemiological practice. A broader objective is to explore the usefulness of machine-learning approaches in epidemiology.IntroductionWithin the traditional surveillance of notifiable infectious diseases in Germany, not only are individual cases reported to the Robert Koch Institute, but also outbreaks themselves are recorded: A label is assigned by epidemiologists to each case, indicating whether it is part of an outbreak and of which. This expert knowledge represents, in the language of machine leaning, a "ground truth" for the algorithmic task of detecting outbreaks from a stream of surveillance data. The integration of this kind of information in the design and evaluation of algorithms is called supervised learning.MethodsReported cases were aggregated weekly and divided into two count time series, one for endemic (not part of an outbreak) and one for epidemic cases. Two new algorithms were developed for the analysis of such time series: farringtonOutbreak is an adaptation of the standard method farringtonFlexible as implemented in the surveillance R package: It trains on endemic case counts but detects anomalies on total case counts. The second algorithm is hmmOutbreak, which is based on a hidden Markov model (HMM): A binary hidden state indicates whether an outbreak was reported in a given week, the transition matrix for this state is learned from the outbreak data and this state is integrated as factor in a generalised linear model of the total case count. An explicit probability of being in a state of outbreak is then computed for each week (one-week ahead) and a signal is generated if it is higher than a user-defined threshold.To evaluate performance, we framed outbreak detection as a simple binary classification problem: Is there an outbreak in a given week, yes or no? Was a signal generated for this week, yes or no? One can thus count, for each time series, the true positives (outbreak data and signals agree), false positives, true negatives and false negatives. From those, classical performance scores can be computed, such as sensitivity, specificity, precision, F-score or area under the ROC curve (AUC).For the evaluation with real-word data we used time series of reported cases of salmonellosis and campylobacteriosis for each of the 412 German counties over 9 years. We also ran simple simulations with different parameter sets, generating count time series and outbreaks with the sim.pointSource function of the surveillance R package.ResultsWe have developed a supervised-learning framework for outbreak detection based on reported infections and outbreaks, proposing two algorithms and an evaluation method. hmmOutbreak performs overall much better than the standard farringtonFlexible, with e.g. a 60% improvement in sensitivity (0.5 compared to 0.3) at a fixed specificity of 0.9. The results were confirmed by simulations. Furthermore, the computation of explicit outbreak probabilities allows a better and clearer interpretation of detection results than the usual testing of the null hypothesis "is endemic".ConclusionsMethods of machine learning can be usefully applied in the context of infectious-disease surveillance. Already a simple HMM shows large improvements and better interpretability: More refined methods, in particular semi-supervised approaches, look thus very promising. The systematic integration of available expert knowledge, in this case the recording of outbreaks, allows an evaluation of algorithmic performance that is of direct relevance for the epidemiological practice, in contrast to the usual intrinsic statistical metrics. Beyond that, this knowledge can be readily used to improve that performance and, in the future, gain insights in outbreak dynamics. Moreover, other types of labels will be similarly integrated in automated surveillance analyses, e.g. user feedback on whether a signal was relevant (reinforcement learning) or messages on specialised internet platforms that were found to be useful warnings of international epidemic events.

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