Lean back and wait for the alarm? Testing an automated alarm system for nosocomial outbreaks to provide support for infection control professionals

INTRODUCTION: Outbreaks of communicable diseases in hospitals need to be quickly detected in order to enable immediate control. The increasing digitalization of hospital data processing offers potential solutions for automated outbreak detection systems (AODS). Our goal was to assess a newly developed AODS. METHODS: Our AODS was based on the diagnostic results of routine clinical microbiological examinations. The system prospectively counted detections per bacterial pathogen over time for the years 2016 and 2017. The baseline data covers data from 2013-2015. The comparative analysis was based on six different mathematical algorithms (normal/Poisson and score prediction intervals, the early aberration reporting system, negative binomial CUSUMs, and the Farrington algorithm). The clusters automatically detected were then compared with the results of our manual outbreak detection system. RESULTS: During the analysis period, 14 different hospital outbreaks were detected as a result of conventional manual outbreak detection. Based on the pathogens' overall incidence, outbreaks were divided into two categories: outbreaks with rarely detected pathogens (sporadic) and outbreaks with often detected pathogens (endemic). For outbreaks with sporadic pathogens, the detection rate of our AODS ranged from 83% to 100%. Every algorithm detected 6 of 7 outbreaks with a sporadic pathogen. The AODS identified outbreaks with an endemic pathogen were at a detection rate of 33% to 100%. For endemic pathogens, the results varied based on the epidemiological characteristics of each outbreak and pathogen. CONCLUSION: AODS for hospitals based on routine microbiological data is feasible and can provide relevant benefits for infection control teams. It offers in-time automated notification of suspected pathogen clusters especially for sporadically occurring pathogens. However, outbreaks of endemically detected pathogens need further individual pathogen-specific and setting-specific adjustments

in silico Surveillance: evaluating outbreak detection with simulation models

Background Detecting outbreaks is a crucial task for public health officials, yet gaps remain in the systematic evaluation of outbreak detection protocols. The authors’ objectives were to design, implement, and test a flexible methodology for generating detailed synthetic surveillance data that provides realistic geographical and temporal clustering of cases and use to evaluate outbreak detection protocols. Methods A detailed representation of the Boston area was constructed, based on data about individuals, locations, and activity patterns. Influenza-like illness (ILI) transmission was simulated, producing 100 years ofin silico ILI data. Six different surveillance systems were designed and developed using gathered cases from the simulated disease data. Performance was measured by inserting test outbreaks into the surveillance streams and analyzing the likelihood and timeliness of detection. Results Detection of outbreaks varied from 21% to 95%. Increased coverage did not linearly improve detection probability for all surveillance systems. Relaxing the decision threshold for signaling outbreaks greatly increased false-positives, improved outbreak detection slightly, and led to earlier outbreak detection. Conclusions Geographical distribution can be more important than coverage level. Detailed simulations of infectious disease transmission can be configured to represent nearly any conceivable scenario. They are a powerful tool for evaluating the performance of surveillance systems and methods used for outbreak detection

Development of Multi-Locus Variable Number Tandem Repeat Analysis for Outbreak Detection of Neisseria meningitidis

Neisseria meningitidis is a major cause of septicemia and meningitis worldwide. Traditional typing methods like pulsed-field gel electrophoresis (PFGE) for identifying outbreaks are subjective and time consuming. Multi-locus variable number tandem repeats analysis (MLVA) is an objective typing method amenable to automation that has been used to type other bacterial pathogens. This report describes the development of MLVA for outbreak detection of N. meningitidis. Tandem Repeats Finder software was used to identify variable number tandem repeats (VNTRs) from 3 sequenced N. meningitidis genomes. PCR amplification of identified VNTRs was performed on DNA from 7 serogroup representative isolates. PCR products were sequenced and repeats were manually counted. VNTR loci identified by this screen were evaluated on a collection of 46 outbreak and sporadic serogroup C isolates. Alleles at each locus were concatenated to define the MLVA type for each isolate. Minimum spanning tree (MST) analysis was performed to determine the genetic relationships among the isolates. The genetic distance was defined as the summed tandem repeat difference (STRD) between isolates MLVA types. Outbreak clusters were defined by a STRD less than or equal to 3. These data was compared to PFGE data to determine the utility of MLVA for outbreak detection. Twenty-one VNTR loci with variable copy numbers among the sequenced genomes were identified that met the established criteria of short repeat length and consensus sequence > 85%. Seven VNTR loci were reliably amplified among the 7 serogroups tested. These loci had repeat lengths between 4 and 20 nucleotides and exhibited between 10 and 26 alleles among 61 isolates belonging to 7 different serogroups. MST analysis with 7 loci differentiated serogroups, discriminated sporadic isolates and identified 7 out of 8 serogroup C outbreaks. In summary, MLVA with 5 VNTR loci distinguished N. meningitidis isolates from 7 different serogroups and sporadic isolates within each serogroup. In addition, MLVA identified 88% of PFGE-defined serogroup C outbreaks. Further investigation of these and other outbreak-associated isolates is necessary to define the optimal combination of VNTR loci and to evaluate MST analysis criteria in order to determine the utility of MLVA for N. meningitidis outbreak detection

Real-time early infectious outbreak detection systems using emerging technologies

The use of emerging technologies ( such as RFID - Radio Frequency Identification and remote sensing) can be employed to reduce health care costs and also to facilitate the automatic streamlining of infectious disease outbreak detection and monitoring processes in local health departments. It can assist medical practitioners with fast and accurate diagnosis and treatments. In this paper we outline the design and application of a real-time RFID and sensor-base Early Infectious (e.g., cholera) Outbreak Detection and Monitoring (IODM) system for health care.<br /

Feature selection algorithms for Malaysian dengue outbreak detection model

Dengue fever is considered as one of the most common mosquito borne diseases worldwide. Dengue outbreak detection can be very useful in terms of practical efforts to overcome the rapid spread of the disease by providing the knowledge to predict the next outbreak occurrence. Many studies have been conducted to model and predict dengue outbreak using different data mining techniques. This research aimed to identify the best features that lead to better predictive accuracy of dengue outbreaks using three different feature selection algorithms; particle swarm optimization (PSO), genetic algorithm (GA) and rank search (RS). Based on the selected features, three predictive modeling techniques (J48, DTNB and Naive Bayes) were applied for dengue outbreak detection. The dataset used in this research was obtained from the Public Health Department, Seremban, Negeri Sembilan, Malaysia. The experimental results showed that the predictive accuracy was improved by applying feature selection process before the predictive modeling process. The study also showed the set of features to represent dengue outbreak detection for Malaysian health agencies