Equine syndromic surveillance in Colorado using veterinary laboratory testing order data.

INTRODUCTION: The Risk Identification Unit (RIU) of the US Dept. of Agriculture's Center for Epidemiology and Animal Health (CEAH) conducts weekly surveillance of national livestock health data and routine coordination with agricultural stakeholders. As part of an initiative to increase the number of species, health issues, and data sources monitored, CEAH epidemiologists are building a surveillance system based on weekly syndromic counts of laboratory test orders in consultation with Colorado State University laboratorians and statistical analysts from the Johns Hopkins University Applied Physics Laboratory. Initial efforts focused on 12 years of equine test records from three state labs. Trial syndrome groups were formed based on RIU experience and published literature. Exploratory analysis, stakeholder input, and laboratory workflow details were needed to modify these groups and filter the corresponding data to eliminate alerting bias. Customized statistical detection methods were sought for effective monitoring based on specialized laboratory information characteristics and on the likely presentation and animal health significance of diseases associated with each syndrome. METHODS: Data transformation and syndrome formation focused on test battery type, test name, submitter source organization, and specimen type. We analyzed time series of weekly counts of tests included in candidate syndrome groups and conducted an iterative process of data analysis and veterinary consultation for syndrome refinement and record filters. This process produced a rule set in which records were directly classified into syndromes using only test name when possible, and otherwise, the specimen type or related body system was used with test name to determine the syndrome. Test orders associated with government regulatory programs, veterinary teaching hospital testing protocols, or research projects, rather than clinical concerns, were excluded. We constructed a testbed for sets of 1000 statistical trials and applied a stochastic injection process assuming lognormally distributed incubation periods to choose an alerting algorithm with the syndrome-required sensitivity and an alert rate within the specified acceptable range for each resulting syndrome. Alerting performance of the EARS C3 algorithm traditionally used by CEAH was compared to modified C2, CuSUM, and EWMA methods, with and without outlier removal and adjustments for the total weekly number of non-mandatory tests. RESULTS: The equine syndrome groups adopted for monitoring were abortion/reproductive, diarrhea/GI, necropsy, neurological, respiratory, systemic fungal, and tickborne. Data scales, seasonality, and variance differed widely among the weekly time series. Removal of mandatory and regulatory tests reduced weekly observed counts significantly-by >80% for diarrhea/GI syndrome. The RIU group studied outcomes associated with each syndrome and called for detection of single-week signals for most syndromes with expected false-alert intervals >8 and <52 weeks, 8-week signals for neurological and tickborne monitoring (requiring enhanced sensitivity), 6-week signals for respiratory, and 4-week signals for systemic fungal. From the test-bed trials, recommended methods, settings and thresholds were derived. CONCLUSIONS: Understanding of laboratory submission sources, laboratory workflow, and of syndrome-related outcomes are crucial to form syndrome groups for routine monitoring without artifactual alerting. Choices of methods, parameters, and thresholds varied by syndrome and depended strongly on veterinary epidemiologist-specified performance requirements.

Syndromic Surveillance System for Korea-US Joint Biosurveillance Portal: Design and Lessons Learned.

Driven by the growing importance of situational awareness of bioterrorism threats, the Republic of Korea (ROK) and the United States have constructed a joint military capability, called the Biosurveillance Portal (BSP), to enhance biosecurity. As one component of the BSP, we developed the Military Active Real-time Syndromic Surveillance (MARSS) system to detect and track natural and deliberate disease outbreaks. This article describes the ROK military health data infrastructure and explains how syndromic data are derived and made available to epidemiologists. Queries corresponding to 8 syndromes, based on published clinical effects of weaponized pathogens, were used to classify military hospital patient records to form aggregated daily syndromic counts. A set of ICD-10 codes for each syndrome was defined through literature review and expert panel discussion. A study set of time series of national daily counts for each syndrome was extracted from the Defense Medical Statistical Information System between January 1, 2011, and May 31, 2014. A stratified, adjusted cumulative summation algorithm was implemented for each syndrome group to signal alerts prompting investigation. The algorithm was developed by calculating sensitivity to sets of 1,000 artificial outbreak signals randomly injected in the dataset, with each signal injected in a separate trial. Queries and visualizations were adapted from the Suite for Automated Global bioSurveillance. Findings indicated that early warning of outbreaks affecting fewer than 50 patients will require analysis at subnational levels, especially for common syndrome groups. Developing MARSS to improve sensitivity will require modification of underlying syndromic diagnosis codes, engineering to coordinate alerts among subdivisions, and enhanced algorithms. The bioterrorist threat in the Korean peninsula mandates these efforts.

Fuzzy association rule mining and classification for the prediction of malaria in South Korea.

BACKGROUND: Malaria is the world's most prevalent vector-borne disease. Accurate prediction of malaria outbreaks may lead to public health interventions that mitigate disease morbidity and mortality. METHODS: We describe an application of a method for creating prediction models utilizing Fuzzy Association Rule Mining to extract relationships between epidemiological, meteorological, climatic, and socio-economic data from Korea. These relationships are in the form of rules, from which the best set of rules is automatically chosen and forms a classifier. Two classifiers have been built and their results fused to become a malaria prediction model. Future malaria cases are predicted as Low, Medium or High, where these classes are defined as a total of 0-2, 3-16, and above 17 cases, respectively, for a region in South Korea during a two-week period. Based on user recommendations, HIGH is considered an outbreak. RESULTS: Model accuracy is described by Positive Predictive Value (PPV), Sensitivity, and F-score for each class, computed on test data not previously used to develop the model. For predictions made 7-8 weeks in advance, model PPV and Sensitivity are 0.842 and 0.681, respectively, for the HIGH classes. The F0.5 and F3 scores (which combine PPV and Sensitivity) are 0.804 and 0.694, respectively, for the HIGH classes. The overall FARM results (as measured by F-scores) are significantly better than those obtained by Decision Tree, Random Forest, Support Vector Machine, and Holt-Winters methods for the HIGH class. For the Medium class, Random Forest and FARM obtain comparable results, with FARM being better at F0.5, and Random Forest obtaining a higher F3. CONCLUSIONS: A previously described method for creating disease prediction models has been modified and extended to build models for predicting malaria. In addition, some new input variables were used, including indicators of intervention measures. The South Korea malaria prediction models predict Low, Medium or High cases 7-8 weeks in the future. This paper demonstrates that our data driven approach can be used for the prediction of different diseases.

Prediction of high incidence of dengue in the Philippines.

BACKGROUND: Accurate prediction of dengue incidence levels weeks in advance of an outbreak may reduce the morbidity and mortality associated with this neglected disease. Therefore, models were developed to predict high and low dengue incidence in order to provide timely forewarnings in the Philippines. METHODS: Model inputs were chosen based on studies indicating variables that may impact dengue incidence. The method first uses Fuzzy Association Rule Mining techniques to extract association rules from these historical epidemiological, environmental, and socio-economic data, as well as climate data indicating future weather patterns. Selection criteria were used to choose a subset of these rules for a classifier, thereby generating a Prediction Model. The models predicted high or low incidence of dengue in a Philippines province four weeks in advance. The threshold between high and low was determined relative to historical incidence data. PRINCIPAL FINDINGS: Model accuracy is described by Positive Predictive Value (PPV), Negative Predictive Value (NPV), Sensitivity, and Specificity computed on test data not previously used to develop the model. Selecting a model using the F0.5 measure, which gives PPV more importance than Sensitivity, gave these results: PPV = 0.780, NPV = 0.938, Sensitivity = 0.547, Specificity = 0.978. Using the F3 measure, which gives Sensitivity more importance than PPV, the selected model had PPV = 0.778, NPV = 0.948, Sensitivity = 0.627, Specificity = 0.974. The decision as to which model has greater utility depends on how the predictions will be used in a particular situation. CONCLUSIONS: This method builds prediction models for future dengue incidence in the Philippines and is capable of being modified for use in different situations; for diseases other than dengue; and for regions beyond the Philippines. The Philippines dengue prediction models predicted high or low incidence of dengue four weeks in advance of an outbreak with high accuracy, as measured by PPV, NPV, Sensitivity, and Specificity.

Combining surveillance systems: effective merging of U.S. Veteran and military health data.

BACKGROUND: The U.S. Department of Veterans Affairs (VA) and Department of Defense (DoD) had more than 18 million healthcare beneficiaries in 2011. Both Departments conduct individual surveillance for disease events and health threats. METHODS: We performed joint and separate analyses of VA and DoD outpatient visit data from October 2006 through September 2010 to demonstrate geographic and demographic coverage, timeliness of influenza epidemic awareness, and impact on spatial cluster detection achieved from a joint VA and DoD biosurveillance platform. RESULTS: Although VA coverage is greater, DoD visit volume is comparable or greater. Detection of outbreaks was better in DoD data for 58% and 75% of geographic areas surveyed for seasonal and pandemic influenza, respectively, and better in VA data for 34% and 15%. The VA system tended to alert earlier with a typical H3N2 seasonal influenza affecting older patients, and the DoD performed better during the H1N1 pandemic which affected younger patients more than normal influenza seasons. Retrospective analysis of known outbreaks demonstrated clustering evidence found in separate DoD and VA runs, which persisted with combined data sets. CONCLUSION: The analyses demonstrate two complementary surveillance systems with evident benefits for the national health picture. Relative timeliness of reporting could be improved in 92% of geographic areas with access to both systems, and more information provided in areas where only one type of facility exists. Combining DoD and VA data enhances geographic cluster detection capability without loss of sensitivity to events isolated in either population and has a manageable effect on customary alert rates.

Usefulness of syndromic data sources for investigating morbidity resulting from a severe weather event.

OBJECTIVE: We evaluated emergency department (ED) data, emergency medical services (EMS) data, and public utilities data for describing an outbreak of carbon monoxide (CO) poisoning following a windstorm. METHODS: Syndromic ED data were matched against previously collected chart abstraction data. We ran detection algorithms on selected time series derived from all 3 data sources to identify health events associated with the CO poisoning outbreak. We used spatial and spatiotemporal scan statistics to identify geographic areas that were most heavily affected by the CO poisoning event. RESULTS: Of the 241 CO cases confirmed by chart review, 190 (78.8%) were identified in the syndromic surveillance data as exact matches. Records from the ED and EMS data detected an increase in CO-consistent syndromes after the storm. The ED data identified significant clusters of CO-consistent syndromes, including zip codes that had widespread power outages. Weak temporal gastrointestinal (GI) signals, possibly resulting from ingestion of food spoiled by lack of refrigeration, were detected in the ED data but not in the EMS data. Spatial clustering of GI-based groupings in the ED data was not detected. CONCLUSIONS: Data from this evaluation support the value of ED data for surveillance after natural disasters. Enhanced EMS data may be useful for monitoring a CO poisoning event, if these data are available to the health department promptly.

Development and evaluation of a data-adaptive alerting algorithm for univariate temporal biosurveillance data.

This paper discusses further advances in making robust predictions with the Holt-Winters forecasts for a variety of syndromic time series behaviors and introduces a control-chart detection approach based on these forecasts. Using three collections of time series data, we compare biosurveillance alerting methods with quantified measures of forecast agreement, signal sensitivity, and time-to-detect. The study presents practical rules for initialization and parameterization of biosurveillance time series. Several outbreak scenarios are used for detection comparison. We derive an alerting algorithm from forecasts using Holt-Winters-generalized smoothing for prospective application to daily syndromic time series. The derived algorithm is compared with simple control-chart adaptations and to more computationally intensive regression modeling methods. The comparisons are conducted on background data from both authentic and simulated data streams. Both types of background data include time series that vary widely by both mean value and cyclic or seasonal behavior. Plausible, simulated signals are added to the background data for detection performance testing at signal strengths calculated to be neither too easy nor too hard to separate the compared methods. Results show that both the sensitivity and the timeliness of the Holt-Winters-based algorithm proved to be comparable or superior to that of the more traditional prediction methods used for syndromic surveillance.

Department of Defense West Nile virus surveillance in 2002.

The Department of Defense (DoD) has engaged in West Nile virus (WNV) surveillance and response since 1999. In 2002, the three Services continued their cooperative, multidisciplinary approach to the WNV outbreak. Activities included a doubling of mosquito surveillance and vector control responses, extension of and doubling of bird and nonhuman mammal surveillance to all four continental United States regions, expanded diagnostic testing by DoD laboratories, and installation environmental clean up and personnel protection campaigns. Medical treatment facilities conducted passive surveillance and reported possible cases in DoD health care beneficiaries. Efforts were coordinated through active communication within installations, with commands, and with surrounding communities. Undertaken activities complemented each other to maximize surveillance coverage. The surveillance detected WNV on 44 DoD installations. It led directly to vector control and prevention activities, and there were no confirmed cases of WNV reported in the DoD force. This multi-Service effort is a surveillance template for future outbreaks that threaten DoD force health.

Disease outbreak detection system using syndromic data in the greater Washington DC area.

BACKGROUND: Many infectious disease outbreaks, including those caused by intentional attacks, may first present insidiously as ill-defined syndromes or unexplained deaths. While there is no substitute for the astute healthcare provider or laboratorian alerting the health department of unusual patient presentations, suspicious patterns may be apparent at the community level well before patient-level data raise an alarm. METHODS: Through centralized Department of Defense medical information systems, diagnoses based on International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) codes are obtained daily from 99 military emergency rooms and primary care clinics across the Washington, DC, region. Similar codes are grouped together in seven diagnostic clusters that represent related presenting signs, symptoms, and diagnoses. Daily monitoring of the data is conducted and evaluated for variation from comparable historic patterns for all seven syndrome groups. Geospatial mapping and trend analysis are performed using geographic information systems software. Data were received on a daily basis beginning in December 1999 and collection continues. The data cut-off date for this manuscript was January 2002. RESULTS: Demographic breakdown of military beneficiaries covered by the surveillance area reveals a broad age, gender, and geographic distribution that is generalizable to the Washington DC region. Ongoing surveillance for the previous 2 years demonstrates expected fluctuations for day-of-the-week and seasonal variations. Detection of several natural disease outbreaks are discussed as well as an analysis of retrospective data from the Centers for Disease Control and Prevention's sentinel physicians-surveillance network during the influenza season that revealed a significantly similar curve to the percentage of patients coded with a respiratory illness in this new surveillance system. DISCUSSION: We believe that this surveillance system can provide early detection of disease outbreaks such as influenza and possibly intentional acts. Early detection should enable officials to quickly focus limited public health resources, decrease subsequent mortality, and improve risk communication. The system is simple, flexible, and, perhaps most critical, acceptable to providers in that it puts no additional requirements on them.