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PubMed2005

Current global iodine status and progress over the last decade towards the elimination of iodine deficiency.

OBJECTIVE: To estimate worldwide iodine nutrition and monitor country progress towards sustained elimination of iodine deficiency disorders. METHODS: Cross-sectional data on urinary iodine (UI) and total goitre prevalence (TGP) in school-age children from 1993-2003 compiled in the WHO Global Database on Iodine Deficiency were analysed. The median UI was used to classify countries according to the public health significance of their iodine nutrition status. Estimates of the global and regional populations with insufficient iodine intake were based on the proportion of each country's population with UI below 100 microg/l. TGP was computed for trend analysis over 10 years. FINDINGS: UI data were available for 92.1% of the world's school-age children. Iodine deficiency is still a public health problem in 54 countries. A total of 36.5% (285 million) school-age children were estimated to have an insufficient iodine intake, ranging from 10.1% in the WHO Region of the Americas to 59.9% in the European Region. Extrapolating this prevalence to the general population generated an estimate of nearly two billion individuals with insufficient iodine intake. Iodine intake was more than adequate, or excessive, in 29 countries. Global TGP in the general population was 15.8%. CONCLUSION: Forty-three countries have reached optimal iodine nutrition. Strengthened UI monitoring is required to ensure that salt iodization is having the desired impact, to identify at-risk populations and to ensure sustainable prevention and control of iodine deficiency. Efforts to eliminate iodine deficiency should be maintained and expanded.

Child↗
PubMed2005

Federal role in early detection preparedness systems.

This report discusses CDC's role in the early detection of threats to public health, including linkage of detection to other preparedness functions, and provides an update on recent progress in improving these capabilities. CDC's role has been fivefold: 1) identifying and adopting information system standards; 2) providing funding; 3) defining critical surveillance functionalities; 4) accelerating Internet-based surveillance systems and electronic reporting of laboratory results; and 5) implementing BioSense, a secure, Internet-accessible, national early detection system that includes syndromic surveillance. Ongoing iterative efforts and consultation are needed to ensure future progress.

Centers for Disease Control and Prevention, U.S.↗
PubMed2005

BioSense: implementation of a National Early Event Detection and Situational Awareness System.

BioSense is a CDC initiative to support enhanced early detection, quantification, and localization of possible biologic terrorism attacks and other events of public health concern on a national level. The goals of the BioSense initiative are to advance early detection by providing the standards, infrastructure, and data acquisition for near real-time reporting, analytic evaluation and implementation, and early event detection support for state and local public health officials. BioSense collects and analyzes Department of Defense and Department of Veterans Affairs ambulatory clinical diagnoses and procedures and Laboratory Corporation of America laboratory-test orders. The application summarizes and presents analytical results and data visualizations by source, day, and syndrome for each ZIP code, state, and metropolitan area through maps, graphs, and tables. An initial proof of a concept evaluation project was conducted before the system was made available to state and local users in April 2004. User recruitment involved identifying and training BioSense administrators and users from state and local health departments. User support has been an essential component of the implementation and enhancement process. CDC initiated the BioIntelligence Center (BIC) in June 2004 to conduct internal monitoring of BioSense national data daily. BIC staff have supported state and local system monitoring, conducted data anomaly inquiries, and communicated with state and local public health officials. Substantial investments will be made in providing regional, state, and local data for early event detection and situational awareness, test beds for data and algorithm evaluation, detection algorithm development, and data management technologies, while maintaining the focus on state and local public health needs.

Bioterrorism↗
PubMed2005

Guidelines for constructing a statewide hospital syndromic surveillance network.

INTRODUCTION: The process to initiate a comprehensive and inexpensive statewide hospital emergency department-based syndromic surveillance system (HEDSS) in Connecticut can serve as a template for others. OBJECTIVES: With limited financial resources, the Connecticut Department of Public Health (CDPH) determined the requirements necessary to establish and routinely conduct hospital emergency department (HED)-based syndromic surveillance. METHODS: A statewide survey assessed ability and willingness of Connecticut hospitals to participate in HED syndromic surveillance. The New York City HED-based system protocol and analysis programs, available without financial charge, were modified for use in Connecticut. This system is based on hospitals sending daily standardized files of chief complaint data through encrypted e-mail or an FTP protocol to CDPH with subsequent categorization into syndromes using a SAS program. Anticipating regional surveillance needs during the Republican National Convention in New York City (RNC), CDPH initiated HEDSS in August 2004. RESULTS: Most Connecticut HEDs were willing and able to participate on a voluntary basis. Beginning in July 2004, hospital recruitment began. By the time of the RNC, 11 of 32 Connecticut hospitals participated in HEDSS. Since then, an additional six HEDs have joined. CONCLUSION: Establishing a voluntary statewide HEDSS was possible using an existing, readily available protocol with minimum financial resources and consensus from a statewide workgroup over a several-month time period.

Connecticut↗
PubMed2005

Implementation of laboratory order data in BioSense Early Event Detection and Situation Awareness System.

INTRODUCTION: Laboratory test orders constitute an early outbreak data source. CDC receives laboratory order data in HL7 format from the Laboratory Corporation of America (LabCorp) and plans to use the data in the BioSense Early Event Detection and Situation Awareness System. METHODS: These LabCorp data contain information on tests ordered and include the type of test ordered and the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM)-coded reasons for the order. A consensus panel was formed to group test orders on the basis of expert opinion into eight standard syndrome categories to provide an additional data source for early outbreak detection. A laboratory order taxonomy was developed and used in the mapping consolidation phase. The five main classes of this taxonomy are miscellaneous functional tests, fluid screening tests, system-specific tests, tests for specific infections (by primary manifestation), and tests for specific noninfectious diseases. RESULTS: Summary of numbers of laboratory order codes in each syndrome category are fever (53), respiratory (53), gastrointestinal (27), neurological (35), rash (37), lymphadenitis (20), localized cutaneous lesion (11), and specific infection (63). CONCLUSION: With the daily use of laboratory order data in BioSense, the actual distribution of laboratory order codes in syndrome groups can be evaluated, allowing modification of the mapping.

Clinical Laboratory Information Systems↗
PubMed2005

Use of Medicaid prescription data for syndromic surveillance--New York.

INTRODUCTION: This study explored the utility and value of Medicaid prescription data for statewide syndromic surveillance. METHODS: Daily Medicaid claims forms are transmitted to the New York State Syndromic Surveillance Project as summary counts by ZIP Code, age category, sex, and 18 medication groups. The CUSUM statistic is used to analyze the data daily at the county level with a 7-10-day moving average baseline. The system was evaluated following an outbreak of pertussis in a small institutional setting in a rural county by comparing the county's CUSUM signals for prescriptions for macrolide antibiotics with the onset of the outbreak. RESULTS: A case of pertussis was suspected on July 21, 2004, and was reported to the New York State Department of Health on July 22. Treatment and prophylaxis were initiated on July 22. CUSUM analysis flagged a county-wide increase in macrolide antibiotics on the day of treatment/prophylaxis for the first case and contacts in the outbreak. The following week, approximately 300 contacts received prophylaxis (not all of whom were Medicaid clients), resulting in CUSUM signals during the week (July 28 and 29). CONCLUSION: Medicaid prescription data are routinely collected and readily available for syndromic surveillance. This data source has shown potential value as an indicator of disease activity, as in this case study, in an area where a high concentration of Medicaid recipients reside. However, for the surveillance system to be considered a useful early warning system, additional study is required to determine the best methods for selecting from the automatically generated CUSUM signals those that might be important for public health.

Disease Outbreaks↗
PubMed2005

Poison Control Center--based syndromic surveillance for foodborne illness.

OBJECTIVES: This retrospective study evaluated the usefulness of a poison control center (PCC) data collection system in Tucson, Arizona for early detection of foodborne disease outbreaks. METHODS: A search of a PCC database identified callers with gastrointestinal symptoms attributable to suspected foodborne illnesses whose calls were received during January 1-March 31, 2000. For each foodborne illness-related call, PCC coding was compared with a predefined diarrhea/gastroenteritis syndrome. PCC calls also were evaluated by using ZIP code, age, sex, and date of symptom onset to determine if call classifications matched any laboratory-confirmed cases reported to a county health department. RESULTS: An independent review generally agreed with the PCC's classification of calls. When calls and cases were compared, only one potential match was identified. CONCLUSION: Although confirmatory diagnostic information was not available, PCC calls were not duplicative of cases evaluated by the county health department, which suggests that they represent two independent data sets. PCC data might provide a useful addition to surveillance data reported to public health agencies for the early detection of foodborne disease outbreaks. These results will now be used to develop collaborative prospective surveillance systems.

Arizona↗
PubMed2005

Monitoring over-the-counter medication sales for early detection of disease outbreaks--New York City.

INTRODUCTION: Over-the-counter (OTC) medications are frequently used during the initial phase of illness, and increases in their sales might serve as an early indicator of communitywide disease outbreaks. Since August 2002, the New York City (NYC) Department of Health and Mental Hygiene (DOHMH) has tracked OTC medication sales to enhance detection of natural and intentional infectious disease outbreaks. OBJECTIVES: This report describes the surveillance system and presents results from retrospective analyses and a comparison between citywide trends in OTC medication sales and emergency department (ED) visits. METHODS: Sales data transmitted daily to DOHMH are categorized into two groups: influenza-like illness (ILI), which includes cough and influenza medications, and gastrointestinal illness (GI), which includes major brand and generic antidiarrheals. Cyclical, linear regression models were used to identify significant (p<0.05) increases in the daily ratio of ILI to analgesics sales (analgesics are used as a denominator in the absence of total sales). Daily and weekly average ratios of GI to analgesic sales were analyzed. Citywide trends in OTC ILI and GI medication sales were compared with ED visits for fever/influenza and diarrhea syndromes. RESULTS: Citywide ILI drug sales were highest during annual influenza epidemics and elevated during spring and fall allergy seasons, similar to trends in the ED fever/influenza syndrome. ILI sales did not consistently provide earlier warning than the ED system of communitywide influenza. GI drug sales increased during the fall and peaked during early winter and after the blackout of August 2003. Unlike ED diarrheal visits, GI medication sales did not substantially increase during late winter (February-March). CONCLUSION: Citywide OTC medication sales can provide indications of communitywide illness, including annual influenza epidemics. Antidiarrheal medication sales were more sensitive to increases in GI caused by norovirus and influenza than illness caused by rotavirus. OTC medication sales can be considered as an adjunct syndromic surveillance system but might not be as sensitive as ED systems.

Commerce↗
PubMed2005

Experimental surveillance using data on sales of over-the-counter medications--Japan, November 2003-April 2004.

OBJECTIVES: This report describes a study to explore the possibility of using data on sales of over-the- counter (OTC) medications as part of a routine syndromic surveillance system aimed at early detection of infections of public health concern. A retrospective evaluation was conducted of sales of OTC medications used to treat the common cold. This report discusses the correlation of these data to influenza activity in Japan during the 2003-04 influenza season and evaluates the potential of using such data to predict influenza epidemics. METHODS: Data from approximately 1,100 pharmacies throughout Japan collected during November 2003-April 2004 were analyzed. OTC sales data were compared with influenza incidence data (one weekly and two daily data sets) to determine correlations and predictability. Adjusted R-square was used as an index of goodness of-fit in the estimation. Data reflecting daily influenza activity were obtained from the National Surveillance of Daily Influenza Outpatients and the Mailing List-Based Influenza Epidemic Database. National sentinel surveillance data for influenza from approximately 5,000 sites nationwide also were analyzed. RESULTS: Although a correlation was demonstrated between sales of OTC medications used to treat the common cold and concurrent influenza activity, analysis of sales data alone was not sufficient to determine influenza activity in advance even when sales promotion effects were excluded from the analysis. CONCLUSION: Because visiting a health-care provider costs more than purchasing OTC medications, the hypothesis was formed that an ill person will purchase OTC medications first and visit a physician only if the condition does not resolve or worsens. The results of this study do not provide any clear evidence to support this hypothesis. For this reason, OTC sales do not appear to be a good candidate for a national real-time detection system for influenza epidemics in Japan.

Commerce↗
PubMed2005

Use of multiple data streams to conduct Bayesian biologic surveillance.

INTRODUCTION: Emergency department (ED) records and over-the-counter (OTC) sales data are two of the most commonly used sources of data for syndromic surveillance. The majority of detection algorithms monitor these data sources separately and either do not combine them or combine them in an ad hoc fashion. This report outlines a new causal model that combines the two data sources coherently to perform outbreak detection. OBJECTIVES: This report describes the extension of the Population-wide Anomaly Detection and Assessment (PANDA) Bayesian biologic surveillance algorithm to combine information from multiple data streams. It also outlines the assumptions and techniques used to make this approach scalable for real-time surveillance of a large population. METHODS: A causal Bayesian network model used previously was extended to incorporate evidence from daily OTC sales data. At the level of individual persons, the actions that result in the purchase of OTC products and in admission to an ED were modeled. RESULTS: Preliminary results indicate that this model has a tractable running time consisting of 209 seconds for initialization and approximately 4 seconds for every hour's worth of ED data, as measured on a Pentium-4 three-Gigahertz machine with two Gigabytes of RAM. CONCLUSION: Preliminary results for surveillance using a new Bayesian algorithm that models the interaction between ED and OTC data are positive regarding the run time of the algorithm.

Algorithms↗
PubMed2005

INFERNO: a system for early outbreak detection and signature forecasting.

OBJECTIVE: Public health surveillance systems that monitor daily disease incidence provide valuable information about threats to public health and enable public health authorities to detect enteric outbreaks rapidly. This report describes the INtegrated Forecasts and EaRly eNteric Outbreak (INFERNO) detection system of algorithms for outbreak detection and forecasting. METHODS: INFERNO incorporates existing knowledge of infectious disease epidemiology into adaptive forecasts and uses the concept of an outbreak signature as a composite of disease epidemic curves. RESULTS: Four main components comprise the system: 1) training, 2) warning and flagging, 3) signature forecasting, and 4) evaluation. The unifying goal of the system is to gain insight into the nature of temporal variations in the incidence of infection. Daily collected records are smoothed initially by using a loess-type smoother. Upon receipt of new data, the smoothing is updated; estimates are made of the first two derivatives of the smoothed curve, which are used for near-term forecasting. Recent data and near-term forecasts are used to compute a five level, color-coded warning index to quantify the level of concern. Warning algorithms are designed to balance false detection of an epidemic (Type I errors) with failure to correctly detect an epidemic (Type II errors). If the warning index signals a sufficiently high probability of an epidemic, the fitting of a gamma-based signature curve to the actual data produces a forecast of the possible size of the outbreak. CONCLUSION: Although the system is under development, its potential has been demonstrated through successful use of emergency department records associated with a substantial waterborne outbreak of cryptosporidiosis that occurred in Milwaukee, Wisconsin, in 1993. Prospects for further development, including adjustment for seasonality and reporting delays, are also outlined.

Algorithms↗
PubMed2005

High-fidelity injection detectability experiments: a tool for evaluating syndromic surveillance systems.

INTRODUCTION: When public health surveillance systems are evaluated, CDC recommends that the expected sensitivity, specificity, and timeliness of surveillance systems be characterized for outbreaks of different sizes, etiologies, and geographic or demographic scopes. High-Fidelity Injection Detectability Experiments (HiFIDE) is a tool that health departments can use to compute these metrics for detection algorithms and surveillance data that they are using in their surveillance system. OBJECTIVE: The objective of this study is to develop a tool that allows health departments to estimate the expected sensitivity, specificity, and timeliness of outbreak detection. METHODS: HiFIDE extends existing semisynthetic injection methods by replacing geometrically shaped injects with injects derived from surveillance data collected during real outbreaks. These injects maintain the known relation between outbreak size and effect on surveillance data, which allows inferences to be made regarding the smallest outbreak that can be expected to be detectable. RESULTS: An example illustrates the use of HiFIDE to analyze detectability of a waterborne Cryptosporidium outbreak in Washington, DC. CONCLUSION: HiFIDE enables public health departments to perform system validations recommended by CDC. HiFIDE can be obtained for no charge for noncommercial use (http://www.hifide.org).

Cryptosporidiosis↗
PubMed2005

Linked analysis for definition of nurse advice line syndrome groups, and comparison to encounters.

INTRODUCTION: Nurse advice call centers are a potentially important source of data for syndromic surveillance purposes. For this reason, researchers at Johns Hopkins University Applied Physics Laboratory and Kaiser Permanente of the Mid-Atlantic States (KPMAS) have been collaborating to develop methods to use this data within the ESSENCE II Syndromic Surveillance System in the National Capital Region. OBJECTIVE: The objective of this report is to present a general method for finding syndrome groups in data sources that can be linked to physician encounters and to determine effective advice call syndrome groups for use with KPMAS advice data. METHODS: Advice calls are linked to physician encounters and stratified by patient age. They are placed in groups according to a maximum positive predictive value criterion. The groups are evaluated by correlating the resulting syndrome time series against physician encounter data. RESULTS: Potentially useful advice syndrome groups are found for respiratory, lower gastrointestinal (GI), and total GI syndromes for each age stratum. CONCLUSION: The time series of the advice data for respiratory, lower GI, and upper GI syndromes accurately predict the physician encounter time series for the corresponding syndromes for each age stratum.

Diagnosis-Related Groups↗
PubMed2005

Simulation for assessing statistical methods of biologic terrorism surveillance.

INTRODUCTION: Multiple systems have been developed that use surveillance of health-care encounters to provide early warning of a terrorist attack. Limited practical experience and the absence of adequate theoretical assessments have precluded determining which alarm-generating algorithms should be preferred. In the absence of practical and theoretical results, choosing a particular statistical algorithm can be difficult. One way to evaluate algorithms is through simulation. OBJECTIVES: This report describes conceptual features of an example simulation based on the dispersal of anthrax spores and presents results based on the example simulation. METHODS: A simulation was implemented based on the dispersal of anthrax spores from a crop-dusting plane. Simulated cases were then included into an observed data stream. Detection approaches included SaTScan trade mark and small area regression and testing (SMART) scores. An evaluation metric was developed for comparison of results. In addition, a simulation of a separate data stream was added; and then separate and combined surveillance data were compared. RESULTS: In the simulation in which a single data stream was used, the two statistical approaches were substantially similar in performance. The combined surveillance based on two data streams is superior to surveillance based on either stream separately. CONCLUSION: The other potential uses of such a system are considered. These uses include the comparison of different data sources (e.g., outpatient versus emergency department and evaluating the impact of potential changes to the surveillance system, and increasing the population under surveillance). Simulation is a valuable technique for evaluating and planning for syndromic surveillance.

Algorithms↗
PubMed2005

An evaluation model for syndromic surveillance: assessing the performance of a temporal algorithm.

INTRODUCTION: Syndromic surveillance offers the potential to rapidly detect outbreaks resulting from terrorism. Despite considerable experience with implementing syndromic surveillance, limited evidence exists to describe the performance of syndromic surveillance systems in detecting outbreaks. OBJECTIVES: To describe a model for simulating cases that might result from exposure to inhalational anthrax and then use the model to evaluate the ability of syndromic surveillance to detect an outbreak of inhalational anthrax after an aerosol release. METHODS: Disease progression and health-care use were simulated for persons infected with anthrax. Simulated cases were then superimposed on authentic surveillance data to create test data sets. A temporal outbreak detection algorithm was applied to each test data set, and sensitivity and timeliness of outbreak detection were calculated by using syndromic surveillance. RESULTS: The earliest detection using a temporal algorithm was 2 days after a release. Earlier detection tended to occur when more persons were infected, and performance worsened as the proportion of persons seeking care in the prodromal disease state declined. A shorter median incubation state led to earlier detection, as soon as 1 day after release when the incubation state was < or =5 days. CONCLUSION: Syndromic surveillance of a respiratory syndrome using a temporal detection algorithm tended to detect an anthrax attack within 3-4 days after exposure if >10,000 persons were infected. The performance of surveillance (i.e., timeliness and sensitivity) worsened as the number of persons infected decreased.

Algorithms↗
PubMed2005

Evaluation of syndromic surveillance based on National Health Service Direct derived data--England and Wales.

INTRODUCTION: Syndromic surveillance systems might serve as an early warning to detect outbreaks of infectious diseases and chemical poisoning, including those caused by deliberate release. In England and Wales, data from National Health Service (NHS) Direct, a national telephone health advice service, were used for surveillance of 10 syndromes commonly occurring in the community. OBJECTIVES: The objective of this study was to evaluate NHS Direct syndromic surveillance using the "Framework for Evaluating Public Health Surveillance Systems for Early Detection of Outbreaks", published by CDC. METHODS: Quantitative and qualitative assessments were performed. Examination of daily data flow was used to determine the timeliness and data quality. Validity was determined by comparing NHS Direct surveillance with a well-established clinical-based surveillance system using a time series analysis. Semistructured interviews of main stakeholders were conducted to determine usefulness, flexibility, acceptability, portability, stability, and system costs. RESULTS: NHS Direct syndromic surveillance has representative national coverage, provides near real-time recording and data analysis, and can potentially detect high-risk, large-scale events. Direct costs are low and variable costs are unpredictable. Flexibility depends on urgency of the need for change, and portability relies on the existence of infrastructure similar to NHS Direct. Statistically significant correlation exists between NHS Direct surveillance and a surveillance system based on the Royal College of General Practitioners data for influenza-like illness. CONCLUSION: The CDC framework is a useful tool to standardize the evaluation of syndromic surveillance. NHS Direct syndromic surveillance is timely, representative, useful, and acceptable with low marginal costs and borderline flexibility and portability. Cross-correlation time series modeling might represent an appropriate method in the evaluation of syndromic surveillance validity.

Disease Outbreaks↗
PubMed2005

Initial evaluation of the early aberration reporting system--Florida.

INTRODUCTION: In recent years, many syndromic surveillance systems have been deployed around the United States for the early detection of biologic terrorism-related and naturally occurring outbreaks. These systems and the associated aberration detection methods need to be evaluated. OBJECTIVE: This study evaluated several detection methods of the Early Aberration Reporting System (EARS) under serially correlated syndromic data and to demonstrated the need for calibrating these methods. METHODS: In an initial evaluation of the Syndromic Tracking and Reporting System in Hillsborough County, Florida, serially correlated syndromic data were simulated using statistical models in conjunction with real syndromic data. The detection methods were tested against two patterns of simulated outbreaks. They were compared using a conditional average run length and a receiver operating characteristic curve under defined patterns of detection. RESULTS: Increasing serial correlation inflates the false alarm rate and elevates sensitivity. Among the detection methods in EARS, C2 and P-chart have the best overall receiver operating characteristic curve within the context of the simulations. C2 is least affected by the serial correlation, the outbreak type, and the defined patterns of detection signal. CONCLUSION: Evaluation of the detection methods needs to be adaptable to the constantly changing nature of syndromic surveillance. Deployment of EARS and other methods requires adjusting the false alarm rate and sensitivity in accordance with the syndromic data, the operating resources, and the objectives of the local system. For timely detection, C2 is superior to other methods, including C3, under the simulation conditions. P-chart is the most sensitive when the serial correlation is negligible.

Algorithms↗
PubMed2005

Deciphering data anomalies in BioSense.

INTRODUCTION: Since June 2004, CDC's BioIntelligence Center has monitored daily nationwide syndromic data by using the BioSense surveillance application. OBJECTIVES: The BioSense application has been monitored by a team of full-time CDC analysts. This report examines their role in identifying and deciphering data anomalies. It also discusses the limitations of the current surveillance application, lessons learned, and potential next steps to improve national syndromic surveillance methodology. METHODS: Data on clinical diagnoses (International Classification of Diseases, Ninth Revision, Clinical Modifications [ICD-9-CM]) and medical procedures (CPT codes) are provided by Department of Veterans Affairs and Department of Defense ambulatory-care clinics; data on select sales of over-the-counter health-care products are provided by participating retail pharmacies; and data on laboratory tests ordered are provided by Laboratory Corporation of America, Inc. All data are filtered to exclude information irrelevant to syndromic surveillance. RESULTS: During June-November 2004, of the approximately 160 data anomalies examined, no events involving disease outbreaks or deliberate exposure to a pathogen were detected. Data anomalies were detected by using a combination of statistical algorithms and analytical visualization features. The anomalies primarily reflected unusual changes in either daily data volume or in types of clinical diagnoses and procedures. This report describes steps taken in routine monitoring, including 1) detecting data anomalies, 2) estimating geographic and temporal scope of the anomalies, 3) gathering supplemental facts, 4) comparing data from multiple data sources, 5) developing hypotheses, and 6) ruling out or validating the existence of an actual event. To be useful for early detection, these steps must be completed quickly (i.e., in hours or days). Anomalies described are attributable to multiple causes, including miscoded data, effects of retail sales promotions, and smaller but explainable signals. CONCLUSION: BioSense requires an empirical learning curve to make the best use of the public health data it contains. This process can be made more effective by continued improvements to the user interface and collective input from local public health partners.

Bioterrorism↗