Dataset Information

Using decision fusion methods to improve outbreak detection in disease surveillance.

ABSTRACT: BACKGROUND:When outbreak detection algorithms (ODAs) are considered individually, the task of outbreak detection can be seen as a classification problem and the ODA as a sensor providing a binary decision (outbreak yes or no) for each day of surveillance. When they are considered jointly (in cases where several ODAs analyze the same surveillance signal), the outbreak detection problem should be treated as a decision fusion (DF) problem of multiple sensors. METHODS:This study evaluated the benefit for a decisions support system of using DF methods (fusing multiple ODA decisions) compared to using a single method of outbreak detection. For each day, we merged the decisions of six ODAs using 5 DF methods (two voting methods, logistic regression, CART and Bayesian network - BN). Classical metrics of accuracy, prediction and timelines were used during the evaluation steps. RESULTS:In our results, we observed the greatest gain (77%) in positive predictive value compared to the best ODA if we used DF methods with a learning step (BN, logistic regression, and CART). CONCLUSIONS:To identify disease outbreaks in systems using several ODAs to analyze surveillance data, we recommend using a DF method based on a Bayesian network. This method is at least equivalent to the best of the algorithms considered, regardless of the situation faced by the system. For those less familiar with this kind of technique, we propose that logistic regression be used when a training dataset is available.

SUBMITTER: Texier G

PROVIDER: S-EPMC6402142 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Using decision fusion methods to improve outbreak detection in disease surveillance.

Texier Gaëtan G Allodji Rodrigue S RS Diop Loty L Meynard Jean-Baptiste JB Pellegrin Liliane L Chaudet Hervé H

BMC medical informatics and decision making 20190305 1

<h4>Background</h4>When outbreak detection algorithms (ODAs) are considered individually, the task of outbreak detection can be seen as a classification problem and the ODA as a sensor providing a binary decision (outbreak yes or no) for each day of surveillance. When they are considered jointly (in cases where several ODAs analyze the same surveillance signal), the outbreak detection problem should be treated as a decision fusion (DF) problem of multiple sensors.<h4>Methods</h4>This study evalu ...[more]

PMID: 30837003

Similar Datasets

Project description:BACKGROUND: Detection of outbreaks is an important part of disease surveillance. Although many algorithms have been designed for detecting outbreaks, few have been specifically assessed against diseases that have distinct seasonal incidence patterns, such as those caused by vector-borne pathogens. METHODS: We applied five previously reported outbreak detection algorithms to Ross River virus (RRV) disease data (1991-2007) for the four local government areas (LGAs) of Brisbane, Emerald, Redland and Townsville in Queensland, Australia. The methods used were the Early Aberration Reporting System (EARS) C1, C2 and C3 methods, negative binomial cusum (NBC), historical limits method (HLM), Poisson outbreak detection (POD) method and the purely temporal SaTScan analysis. Seasonally-adjusted variants of the NBC and SaTScan methods were developed. Some of the algorithms were applied using a range of parameter values, resulting in 17 variants of the five algorithms. RESULTS: The 9,188 RRV disease notifications that occurred in the four selected regions over the study period showed marked seasonality, which adversely affected the performance of some of the outbreak detection algorithms. Most of the methods examined were able to detect the same major events. The exception was the seasonally-adjusted NBC methods that detected an excess of short signals. The NBC, POD and temporal SaTScan algorithms were the only methods that consistently had high true positive rates and low false positive and false negative rates across the four study areas. The timeliness of outbreak signals generated by each method was also compared but there was no consistency across outbreaks and LGAs. CONCLUSIONS: This study has highlighted several issues associated with applying outbreak detection algorithms to seasonal disease data. In lieu of a true gold standard, a quantitative comparison is difficult and caution should be taken when interpreting the true positives, false positives, sensitivity and specificity.

Project description:BackgroundGlobal pandemic influenza preparedness relies heavily on public health surveillance, but it is unclear that current surveillance fully meets pandemic preparedness needs.MethodsWe first developed a conceptual framework to help systematically identify strategies to improve the detection of an early case or cluster of novel human influenza disease during the pre-pandemic period. We then developed a process model (flow diagram) depicting nine major pathways through which a case in the community could be detected and confirmed, and mapped the improvement strategies onto this model. Finally, we developed an interactive decision tool by building quantitative measures of probability and time into each step of the process model and programming it to calculate the net probability and time required for case detection through each detection pathway. Input values for each step can be varied by users to assess the effects of different improvement strategies, alone or in combination. We illustrate application of the tool using hypothetical input data reflecting baseline and 12-month follow-up scenarios, following concurrent implementation of multiple improvement strategies.ResultsWe compared outputs from the tool across detection pathways and across time, at baseline and 12-month follow up. The process model and outputs from the tool suggest that traditional efforts to build epidemiology and laboratory capacity are efficient strategies, as are more focused strategies within these, such as targeted laboratory testing; expedited specimen transport; use of technologies to streamline data flow; and improved reporting compliance. Other promising strategies stem from community detection - better harnessing of electronic data mining and establishment of community-based monitoring.ConclusionOur practical tool allows policymakers to use their own realistic baseline values and program projections to assess the relative impact of different interventions to improve the probability and timeliness of detecting early human cases or clusters caused by a novel influenza virus, a possible harbinger of a new pandemic. Policymakers can use results to target investments to improve their surveillance infrastructure. Multi-national planners can also use the tool to help guide directions in surveillance system improvements more globally. Finally, our systematic approach can also be tailored to help improve surveillance for other diseases.

Project description:BackgroundAn increasing number of methods are being developed for the early detection of infectious disease outbreaks which could be naturally occurring or as a result of bioterrorism; however, no standardised framework for examining the usefulness of various outbreak detection methods exists. To promote comparability between studies, it is essential that standardised methods are developed for the evaluation of outbreak detection methods.MethodsThis analysis aims to review approaches used to evaluate outbreak detection methods and provide a conceptual framework upon which recommendations for standardised evaluation methods can be based. We reviewed the recently published literature for reports which evaluated methods for the detection of infectious disease outbreaks in public health surveillance data. Evaluation methods identified in the recent literature were categorised according to the presence of common features to provide a conceptual basis within which to understand current approaches to evaluation.ResultsThere was considerable variation in the approaches used for the evaluation of methods for the detection of outbreaks in public health surveillance data, and appeared to be no single approach of choice. Four main approaches were used to evaluate performance, and these were labelled the Descriptive, Derived, Epidemiological and Simulation approaches. Based on the approaches identified, we propose a basic framework for evaluation and recommend the use of multiple approaches to evaluation to enable a comprehensive and contextualised description of outbreak detection performance.ConclusionThe varied nature of performance evaluation demonstrated in this review supports the need for further development of evaluation methods to improve comparability between studies. Our findings indicate that no single approach can fulfil all evaluation requirements. We propose that the cornerstone approaches to evaluation identified provide key contributions to support internal and external validity and comparability of study findings, and suggest these be incorporated into future recommendations for performance assessment.

Project description:ObjectiveResearch studies show that social media may be valuable tools in the disease surveillance toolkit used for improving public health professionals' ability to detect disease outbreaks faster than traditional methods and to enhance outbreak response. A social media work group, consisting of surveillance practitioners, academic researchers, and other subject matter experts convened by the International Society for Disease Surveillance, conducted a systematic primary literature review using the PRISMA framework to identify research, published through February 2013, answering either of the following questions: Can social media be integrated into disease surveillance practice and outbreak management to support and improve public health?Can social media be used to effectively target populations, specifically vulnerable populations, to test an intervention and interact with a community to improve health outcomes?Examples of social media included are Facebook, MySpace, microblogs (e.g., Twitter), blogs, and discussion forums. For Question 1, 33 manuscripts were identified, starting in 2009 with topics on Influenza-like Illnesses (n = 15), Infectious Diseases (n = 6), Non-infectious Diseases (n = 4), Medication and Vaccines (n = 3), and Other (n = 5). For Question 2, 32 manuscripts were identified, the first in 2000 with topics on Health Risk Behaviors (n = 10), Infectious Diseases (n = 3), Non-infectious Diseases (n = 9), and Other (n = 10).ConclusionsThe literature on the use of social media to support public health practice has identified many gaps and biases in current knowledge. Despite the potential for success identified in exploratory studies, there are limited studies on interventions and little use of social media in practice. However, information gleaned from the articles demonstrates the effectiveness of social media in supporting and improving public health and in identifying target populations for intervention. A primary recommendation resulting from the review is to identify opportunities that enable public health professionals to integrate social media analytics into disease surveillance and outbreak management practice.

Project description:BackgroundPassive reporting and laboratory testing delays may limit gastrointestinal (GI) disease outbreak detection. Healthcare systems routinely collect clinical data in electronic medical records (EMRs) that could be used for surveillance. This study's primary objective was to identify data streams from EMRs that may perform well for GI outbreak detection.MethodsZip code-specific daily episode counts in 2009 were generated for 22 syndromic and laboratory-based data streams from Kaiser Permanente Northern California EMRs, covering 3.3 million members. Data streams included outpatient and inpatient diagnosis codes, antidiarrheal medication dispensings, stool culture orders, and positive microbiology tests for six GI pathogens. Prospective daily surveillance was mimicked using the space-time permutation scan statistic in single and multi-stream analyses, and space-time clusters were identified. Serotype relatedness was assessed for isolates in two Salmonella clusters.ResultsPotential outbreaks included a cluster of 18 stool cultures ordered over 5 days in one zip code and a Salmonella cluster in three zip codes over 9 days, in which at least five of six cases had the same rare serotype. In all, 28 potential outbreaks were identified using single stream analyses, with signals in outpatient diagnosis codes most common. Multi-stream analyses identified additional potential outbreaks and in one example, improved the timeliness of detection.ConclusionsGI disease-related data streams can be used to identify potential outbreaks when generated from EMRs with extensive regional coverage. This process can supplement traditional GI outbreak reports to health departments, which frequently consist of outbreaks in well-defined settings (e.g., day care centers and restaurants) with no laboratory-confirmed pathogen. Data streams most promising for surveillance included microbiology test results, stool culture orders, and outpatient diagnoses. In particular, clusters of microbiology tests positive for specific pathogens could be identified in EMRs and used to prioritize further testing at state health departments, potentially improving outbreak detection.

Dataset Information

Using decision fusion methods to improve outbreak detection in disease surveillance.

Publications

Using decision fusion methods to improve outbreak detection in disease surveillance.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets