Evaluating detection of an inhalational anthrax outbreak

Perspective on Improving Environmental Monitoring of Biothreats

For more than a decade, the United States has performed environmental monitoring by collecting and analyzing air samples for a handful of biological threat agents (BTAs) in order to detect a possible biological attack. This effort has faced numerous technical challenges including timeliness, sampling efficiency, sensitivity, specificity, and robustness. The cost of city-wide environmental monitoring using conventional technology has also been a challenge. A large group of scientists with expertise in bioterrorism defense met to assess the objectives and current efficacy of environmental monitoring and to identify operational and technological changes that could enhance its efficacy and cost-effectiveness, thus enhancing its value. The highest priority operational change that was identified was to abandon the current concept of city-wide environmental monitoring because the operational costs were too high and its value was compromised by low detection sensitivity and other environmental factors. Instead, it was suggested that the focus should primarily be on indoor monitoring and secondarily on special-event monitoring because objectives are tractable and these operational settings are aligned with likelihood and risk assessments. The highest priority technological change identified was the development of a reagent-less, real-time sensor that can identify a potential airborne release and trigger secondary tests of greater sensitivity and specificity for occasional samples of interest. This technological change could be transformative with the potential to greatly reduce operational costs and thereby create the opportunity to expand the scope and effectiveness of environmental monitoring.

Public Health Surveillance Systems: Recent Advances in Their Use and Evaluation

Surveillance is critical for improving population health. Public health surveillance systems generate information that drives action, and the data must be of sufficient quality and with a resolution and timeliness that matches objectives. In the context of scientific advances in public health surveillance, changing health care and public health environments, and rapidly evolving technologies, the aim of this article is to review public health surveillance systems. We consider their current use to increase the efficiency and effectiveness of the public health system, the role of system stakeholders, the analysis and interpretation of surveillance data, approaches to system monitoring and evaluation, and opportunities for future advances in terms of increased scientific rigor, outcomes-focused research, and health informatics.

Atmospheric dispersion modelling of bioaerosols that are pathogenic to humans and livestock - A review to inform risk assessment studies

In this review we discuss studies that applied atmospheric dispersion models (ADM) to bioaerosols that are pathogenic to humans and livestock in the context of risk assessment studies. Traditionally, ADMs have been developed to describe the atmospheric transport of chemical pollutants, radioactive matter, dust, and particulate matter. However, they have also enabled researchers to simulate bioaerosol dispersion. To inform risk assessment, the aims of this review were fourfold, namely (1) to describe the most important physical processes related to ADMs and pathogen transport, (2) to discuss studies that focused on the application of ADMs to pathogenic bioaerosols, (3) to discuss emission and inactivation rate parameterisations, and (4) to discuss methods for conversion of concentrations to infection probabilities (concerning quantitative microbial risk assessment). The studies included human, livestock, and industrial sources. Important factors for dispersion included wind speed, atmospheric stability, topographic effects, and deposition. Inactivation was mainly governed by humidity, temperature, and ultraviolet radiation. A majority of the reviewed studies, however, lacked quantitative analyses and application of full quantitative microbial risk assessments (QMRA). Qualitative conclusions based on geographical dispersion maps and threshold doses were encountered frequently. Thus, to improve risk assessment for future outbreaks and releases, we recommended determining well-quantified emission and inactivation rates and applying dosimetry and dose-response models to estimate infection probabilities in the population at risk.

Historical evolution of human anthrax from occupational disease to potentially global threat as bioweapon

PURPOSE

Anthrax is caused by Bacillus anthracis, which can naturally infect livestock, wildlife and occupationally exposed humans. However, for its resistance due to spore formation, ease of dissemination, persistence in the environment and high virulence, B. anthracis has been considered the most serious bioterrorism agent for a long time. During the last century anthrax evolved from limited natural disease to potentially global threat if used as bioweapon. Several factors may mitigate the consequences of an anthrax attack, including 1. the capability to promptly recognize and manage the illness and its public health consequences; 2. the limitation of secondary contamination risk through an appropriate decontamination; and 3. the evolution of genotyping methods (for microbes characterization at high resolution level) that can influence the course and/or focus of investigations, impacting the response of the government to an attack.

METHODS

A PubMed search has been done using the key words “bioterrorism anthrax”.

RESULTS

Over one thousand papers have been screened and the most significant examined to present a comprehensive literature review in order to discuss the current knowledge and strategies in preparedness for a possible deliberate release of B. anthracis spores and to indicate the most current and complete documents in which to deepen.

CONCLUSIONS

The comprehensive analysis of the two most relevant unnatural anthrax release events, Sverdlovsk in the former Soviet Union (1979) and the contaminated letters in the USA (2001), shows that inhalational anthrax may easily and cheaply be spread resulting in serious consequences. The damage caused by an anthrax attack can be limited if public health organization, first responders, researchers and investigators will be able to promptly manage anthrax cases and use new technologies for decontamination methods and in forensic microbiology.

Simulation-Based Evaluation of the Performances of an Algorithm for Detecting Abnormal Disease-Related Features in Cattle Mortality Records

We performed a simulation study to evaluate the performances of an anomaly detection algorithm considered in the frame of an automated surveillance system of cattle mortality. The method consisted in a combination of temporal regression and spatial cluster detection which allows identifying, for a given week, clusters of spatial units showing an excess of deaths in comparison with their own historical fluctuations. First, we simulated 1,000 outbreaks of a disease causing extra deaths in the French cattle population (about 200,000 herds and 20 million cattle) according to a model mimicking the spreading patterns of an infectious disease and injected these disease-related extra deaths in an authentic mortality dataset, spanning from January 2005 to January 2010. Second, we applied our algorithm on each of the 1,000 semi-synthetic datasets to identify clusters of spatial units showing an excess of deaths considering their own historical fluctuations. Third, we verified if the clusters identified by the algorithm did contain simulated extra deaths in order to evaluate the ability of the algorithm to identify unusual mortality clusters caused by an outbreak. Among the 1,000 simulations, the median duration of simulated outbreaks was 8 weeks, with a median number of 5,627 simulated deaths and 441 infected herds. Within the 12-week trial period, 73% of the simulated outbreaks were detected, with a median timeliness of 1 week, and a mean of 1.4 weeks. The proportion of outbreak weeks flagged by an alarm was 61% (i.e. sensitivity) whereas one in three alarms was a true alarm (i.e. positive predictive value). The performances of the detection algorithm were evaluated for alternative combination of epidemiologic parameters. The results of our study confirmed that in certain conditions automated algorithms could help identifying abnormal cattle mortality increases possibly related to unidentified health events.

A practical approach to designing syndromic surveillance systems for livestock and poultry

The field of animal syndromic surveillance (SyS) is growing, with many systems being developed worldwide. Now is an appropriate time to share ideas and lessons learned from early SyS design and implementation. Based on our practical experience in animal health SyS, with additions from the public health and animal health SyS literature, we put forward for discussion a 6-step approach to designing SyS systems for livestock and poultry. The first step is to formalise policy and surveillance goals which are considerate of stakeholder expectations and reflect priority issues (1). Next, it is important to find consensus on national priority diseases and identify current surveillance gaps. The geographic, demographic, and temporal coverage of the system must be carefully assessed (2). A minimum dataset for SyS that includes the essential data to achieve all surveillance objectives while minimizing the amount of data collected should be defined. One can then compile an inventory of the data sources available and evaluate each using the criteria developed (3). A list of syndromes should then be produced for all data sources. Cases can be classified into syndrome classes and the data can be converted into time series (4). Based on the characteristics of the syndrome-time series, the length of historic data available and the type of outbreaks the system must detect, different aberration detection algorithms can be tested (5). Finally, it is essential to develop a minimally acceptable response protocol for each statistical signal produced (6). Important outcomes of this pre-operational phase should be building of a national network of experts and collective action and evaluation plans. While some of the more applied steps (4 and 5) are currently receiving consideration, more emphasis should be put on earlier conceptual steps by decision makers and surveillance developers (1-3).

Structural models used in real-time biosurveillance outbreak detection and outbreak curve isolation from noisy background morbidity levels

OBJECTIVE

We discuss the use of structural models for the analysis of biosurveillance related data.

METHODS AND RESULTS

Using a combination of real and simulated data, we have constructed a data set that represents a plausible time series resulting from surveillance of a large scale bioterrorist anthrax attack in Miami. We discuss the performance of anomaly detection with structural models for these data using receiver operating characteristic (ROC) and activity monitoring operating characteristic (AMOC) analysis. In addition, we show that these techniques provide a method for predicting the level of the outbreak valid for approximately 2 weeks, post-alarm.

CONCLUSIONS

Structural models provide an effective tool for the analysis of biosurveillance data, in particular for time series with noisy, non-stationary background and missing data.

Application of change point analysis to daily influenza-like illness emergency department visits

BACKGROUND

The utility of healthcare utilization data from US emergency departments (EDs) for rapid monitoring of changes in influenza-like illness (ILI) activity was highlighted during the recent influenza A (H1N1) pandemic. Monitoring has tended to rely on detection algorithms, such as the Early Aberration Reporting System (EARS), which are limited in their ability to detect subtle changes and identify disease trends.

OBJECTIVE

To evaluate a complementary approach, change point analysis (CPA), for detecting changes in the incidence of ED visits due to ILI.

METHODOLOGY AND PRINCIPAL FINDINGS

Data collected through the Distribute project (isdsdistribute.org), which aggregates data on ED visits for ILI from over 50 syndromic surveillance systems operated by state or local public health departments were used. The performance was compared of the cumulative sum (CUSUM) CPA method in combination with EARS and the performance of three CPA methods (CUSUM, structural change model and Bayesian) in detecting change points in daily time-series data from four contiguous US states participating in the Distribute network. Simulation data were generated to assess the impact of autocorrelation inherent in these time-series data on CPA performance. The CUSUM CPA method was robust in detecting change points with respect to autocorrelation in time-series data (coverage rates at 90% when -0.2≤ρ≤0.2 and 80% when -0.5≤ρ≤0.5). During the 2008-9 season, 21 change points were detected and ILI trends increased significantly after 12 of these change points and decreased nine times. In the 2009-10 flu season, we detected 11 change points and ILI trends increased significantly after two of these change points and decreased nine times. Using CPA combined with EARS to analyze automatically daily ED-based ILI data, a significant increase was detected of 3% in ILI on April 27, 2009, followed by multiple anomalies in the ensuing days, suggesting the onset of the H1N1 pandemic in the four contiguous states.

CONCLUSIONS AND SIGNIFICANCE

As a complementary approach to EARS and other aberration detection methods, the CPA method can be used as a tool to detect subtle changes in time-series data more effectively and determine the moving direction (ie, up, down, or stable) in ILI trends between change points. The combined use of EARS and CPA might greatly improve the accuracy of outbreak detection in syndromic surveillance systems.

Rank-based spatial clustering: an algorithm for rapid outbreak detection

OBJECTIVE

Public health surveillance requires outbreak detection algorithms with computational efficiency sufficient to handle the increasing volume of disease surveillance data. In response to this need, the authors propose a spatial clustering algorithm, rank-based spatial clustering (RSC), that detects rapidly infectious but non-contagious disease outbreaks.

DESIGN

The authors compared the outbreak-detection performance of RSC with that of three well established algorithms-the wavelet anomaly detector (WAD), the spatial scan statistic (KSS), and the Bayesian spatial scan statistic (BSS)-using real disease surveillance data on to which they superimposed simulated disease outbreaks.

MEASUREMENTS

The following outbreak-detection performance metrics were measured: receiver operating characteristic curve, activity monitoring operating curve curve, cluster positive predictive value, cluster sensitivity, and algorithm run time.

RESULTS

RSC was computationally efficient. It outperformed the other two spatial algorithms in terms of detection timeliness, and outbreak localization. RSC also had overall better timeliness than the time-series algorithm WAD at low false alarm rates.

CONCLUSION

RSC is an ideal algorithm for analyzing large datasets when the application of other spatial algorithms is not practical. It also allows timely investigation for public health practitioners by providing early detection and well-localized outbreak clusters.

Optimizing the response to surveillance alerts in automated surveillance systems

Although much research effort has been directed toward refining algorithms for disease outbreak alerting, considerably less attention has been given to the response to alerts generated from statistical detection algorithms. Given the inherent inaccuracy in alerting, it is imperative to develop methods that help public health personnel identify optimal policies in response to alerts. This study evaluates the application of dynamic decision making models to the problem of responding to outbreak detection methods, using anthrax surveillance as an example. Adaptive optimization through approximate dynamic programming is used to generate a policy for decision making following outbreak detection. We investigate the degree to which the model can tolerate noise theoretically, in order to keep near optimal behavior. We also evaluate the policy from our model empirically and compare it with current approaches in routine public health practice for investigating alerts. Timeliness of outbreak confirmation and total costs associated with the decisions made are used as performance measures. Using our approach, on average, 80 per cent of outbreaks were confirmed prior to the fifth day of post-attack with considerably less cost compared to response strategies currently in use. Experimental results are also provided to illustrate the robustness of the adaptive optimization approach and to show the realization of the derived error bounds in practice.

Comparing early outbreak detection algorithms based on their optimized parameter values

BACKGROUND

Many researchers have evaluated the performance of outbreak detection algorithms with recommended parameter values. However, the influence of parameter values on algorithm performance is often ignored.

METHODS

Based on reported case counts of bacillary dysentery from 2005 to 2007 in Beijing, semi-synthetic datasets containing outbreak signals were simulated to evaluate the performance of five outbreak detection algorithms. Parameters' values were optimized prior to the evaluation.

RESULTS

Differences in performances were observed as parameter values changed. Of the five algorithms, space-time permutation scan statistics had a specificity of 99.9% and a detection time of less than half a day. The exponential weighted moving average exhibited the shortest detection time of 0.1 day, while the modified C1, C2 and C3 exhibited a detection time of close to one day.

CONCLUSION

The performance of these algorithms has a correlation to their parameter values, which may affect the performance evaluation.

Real-time public health surveillance for emergency preparedness

Public health agencies conduct surveillance to identify and prioritize health issues and evaluate interventions. Recently, natural and deliberate epidemics have motivated supplementary approaches to traditional surveillance methods based on physician and laboratory reporting.

Fueled initially by post–September 11, 2001, bioterrorism-related funding, and more recently used for detecting natural outbreaks, these systems, many of which are called “syndromic” systems because they focus on syndromes recorded before the diagnosis, capture real-time health data and scan for anomalies suggesting an outbreak. Although these systems as typically implemented have often proven unreliable for detecting natural and simulated epidemics, real-time health-related data hold promise for public health.

If redesigned to reliably perform beyond outbreak detection, syndromic systems could demonstrate unprecedented capabilities in responding to public health emergencies.

Predicting Hospital Surge after a Large-Scale Anthrax Attack: A Model-Based Analysis of CDC's Cities Readiness Initiative Prophylaxis Recommendations

Background . After a major bioterrorism attack, the US Centers for Disease Control and Prevention (CDC) Cities Readiness Initiative (CRI) calls for dispensing of medical countermeasures to targeted populations within 48 hours. The authors explore how meeting or missing this 48-hour goal after a hypothetical aerosol anthrax attack would affect hospital surge, in light of the multiple uncertainties surrounding anthrax-related illness and response. Design . The authors created a discrete-time state transition computer model representing the dynamic interaction between disease progression of inhalational anthrax and the rate of dispensing of prophylactic antibiotics in an exposed population. Results . A CRI-compliant prophylaxis campaign starting 2 days after exposure would protect from 86% to 87% of exposed individuals from illness (assuming, in the base case, 90% antibiotic effectiveness and a 95% attack rate). Each additional day needed to complete the campaign would result in, on average, 2.4% to 2.9% more hospitalizations in the exposed population; each additional day's delay to initiating prophylaxis beyond 2 days would result in 5.2% to 6.5% additional hospitalizations. These population protection estimates vary roughly proportionally to antibiotic effectiveness but are relatively insensitive to variations in anthrax incubation period. Conclusion . Delays in detecting and initiating response to large-scale, covert aerosol anthrax releases in a major city would render even highly effective CRI-compliant mass prophylaxis campaigns unable to prevent unsustainable levels of surge hospitalizations. Although outcomes may improve with more rapid epidemiological identification of affected subpopulations and increased collaboration across regional public health and hospital systems, these findings support an increased focus on prevention of this public health threat.

Pulmonary deposition of aerosolized Bacillus atrophaeus in a Swine model due to exposure from a simulated anthrax letter incident

Dry anthrax spore powder is readily disseminated as an aerosol and it is possible that passive dispersion when opening a letter containing anthrax spores may result in lethal doses to humans. The specific aim of this study was to quantify the respirable aerosol hazard associated with opening an envelope/letter contaminated with a dry spore powder of the biological pathogen anthrax in a typical office environment. An envelope containing a letter contaminated with 1.0 g of dry Bacillus atrophaeus (BG) spores (pathogen simulant) was opened in the presence of an unrestrained swine model. Aerosolized spores were detected in the room in seconds and peak concentrations occurred by three minutes. The swine, located approximately 1.5 m from the source, was exposed to the aerosol for 28 min following the letter opening event and then moved to a clean room for 30 min. A necropsy was completed to determine the extent of in vivo spore deposition in the lungs. The median number of viable colony forming units (CFU) measured in the combined right and left lung was 21,200: the average mass of both lungs was 283 g. In excess of 100 CFU per gram of lung tissue was found at sites within the anterior, intermediate and posterior lobes. The results of this study confirmed that opening an envelope containing spores generated an aerosol spanning the respirable particle size range of 1-10 microm, and that normal respiration of swine led to spore deposition throughout the lungs. The observed deposition of spores in the lungs of the swine is within the LD(50) range of 2,500-55,000 estimated for humans for inhaled anthrax. Thus, there would appear to be a significant health risk to those individuals exposed to anthrax spores when opening a contaminated envelope.

[The validation of a new population surveillance system that analyzes the daily mortality rates in Montreal]

OBJECTIVES

Since 2004, a surveillance system provides counts, almost in real time, of the number of deaths per day occurring on the Island of Montreal. The validity of this monitoring tool and its ability to detect spikes in daily deaths, such as can occur due to heat waves, have been evaluated.

METHOD

Comparison of the number of deaths per day recorded in the monitoring system with the number of deaths per day recorded in the official record of deaths in Quebec for 134 days of 2004.

RESULTS

The monitoring system is accurate (for over 73% of days, the difference in the number of deaths between the two files falls within a range of +/-3 deaths given an average of 43 deaths per day). The system identifies more than 80% of all deaths and is efficient in identifying days with excess deaths that are 20% above the average.

DISCUSSION

This novel monitoring system, based on data used mainly for management of medical services, meets contemporary public health requirements in terms of early detection of unusual health events.

[The validation of a new population surveillance system that analyzes the daily mortality rates in Montreal]

OBJECTIVES

Since 2004, a surveillance system provides counts, almost in real time, of the number of deaths per day occurring on the Island of Montreal. The validity of this monitoring tool and its ability to detect spikes in daily deaths, such as can occur due to heat waves, have been evaluated.

METHOD

Comparison of the number of deaths per day recorded in the monitoring system with the number of deaths per day recorded in the official record of deaths in Quebec for 134 days of 2004.

RESULTS

The monitoring system is accurate (for over 73% of days, the difference in the number of deaths between the two files falls within a range of +/-3 deaths given an average of 43 deaths per day). The system identifies more than 80% of all deaths and is efficient in identifying days with excess deaths that are 20% above the average.

DISCUSSION

This novel monitoring system, based on data used mainly for management of medical services, meets contemporary public health requirements in terms of early detection of unusual health events.

Responding to a bioterrorism attack--one scenario: part 1

This article continues the discussions introduced in an earlier article submitted to The Health Care Manager entitled "Epidemic Simulation for Syndromic Surveillance," wherein a format for analysis of the incidence of a bioterrorist attack was presented. This article outlines a simulation conducted as part of a federal grant award administered through the Center for Biological Defense at the University of South Florida. The disease entity simulated was an attack of anthrax introduced into the Central Florida region. The spread, effects, and eventual control of the disease entity are highlighted.

Modeling the incubation period of inhalational anthrax

Ever since the pioneering work of Philip Sartwell, the incubation period distribution for infectious diseases is most often modeled using a lognormal distribution. Theoretical models based on underlying disease mechanisms in the host are less well developed. This article modifies a theoretical model originally developed by Brookmeyer and others for the inhalational anthrax incubation period distribution in humans by using a more accurate distribution to represent the in vivo bacterial growth phase and by extending the model to represent the time from exposure to death, thereby allowing the model to be fit to nonhuman primate time-to-death data. The resulting incubation period distribution and the dose dependence of the median incubation period are in good agreement with human data from the 1979 accidental atmospheric anthrax release in Sverdlovsk, Russia, and limited nonhuman primate data. The median incubation period for the Sverdlovsk victims is 9.05 (95% confidence interval = 8.0-10.3) days, shorter than previous estimates, and it is predicted to drop to less than 2.5 days at doses above 10(6) spores. The incubation period distribution is important because the left tail determines the time at which clinical diagnosis or syndromic surveillance systems might first detect an anthrax outbreak based on early symptomatic cases, the entire distribution determines the efficacy of medical intervention-which is determined by the speed of the prophylaxis campaign relative to the incubation period-and the right tail of the distribution influences the recommended duration for antibiotic treatment.

Mechanistic modeling of emergency events: assessing the impact of hypothetical releases of anthrax

A modular system for source-to-dose-to-effect modeling analysis has been developed based on the modeling environment for total risk studies (MENTOR),((1)) and applied to study the impacts of hypothetical atmospheric releases of anthrax spores. The system, MENTOR-2E (MENTOR for Emergency Events), provides mechanistically consistent analysis of inhalation exposures for various release scenarios, while allowing consideration of specific susceptible subpopulations (such as the elderly) at the resolution of individual census tracts. The MENTOR-2E application presented here includes atmospheric dispersion modeling, statistically representative samples of individuals along with corresponding activity patterns, and population-based dosimetry modeling that accounts for activity and physiological variability. Two hypothetical release scenarios were simulated: a 100 g release of weaponized B. anthracis over a period of (a) one hour and (b) 10 hours, and the impact of these releases on population in the State of New Jersey was studied. Results were compared with those from simplified modeling of population dynamics (location, activities, etc.), and atmospheric dispersion of anthrax spores. The comparisons showed that in the two release scenarios simulated, each major approximation resulted in an overestimation of the number of probable infections by a factor of 5 to 10; these overestimations can have significant public health implications when preparing for and responding effectively to an actual release. This is in addition to uncertainties in dose-response modeling, which result in an additional factor of 5 to 10 variation in estimated casualties. The MENTOR-2E system has been developed in a modular fashion so that improvements in individual modules can be readily made without impacting the other modules, and provides a first step toward the development of models that can be used in supporting real-time decision making.

Decision theoretic analysis of improving epidemic detection

The potentially catastrophic impact of an epidemic specially these due to bioterrorist attack, makes developing effective detection methods essential for public health. Current detection methods trade off reliability of alarms for early detection of outbreaks. The performance of these methods can be improved by disease-specific modeling techniques that take into account the potential costs and effects of an attack to provide optimal warnings and the cost and effectiveness of interventions. We study this optimization problem in the framework of sequential decision making under uncertainty. Our approach relies on estimating the future benefit of true alarms and the costs of false alarms. Using these quantities it identifies optimal decisions regarding the credibility of outputs from a traditional detection method at each point in time. The key contribution of this paper is to apply Partially Observable Markov Decision Processes (POMDPs) on outbreak detection methods for improving alarm function in the case of anthrax. We present empirical evidence illustrating that at a fixed specificity, the performance of detection methods with respect to sensitivity and timeliness is improved significantly by utilizing POMDPs in detection of anthrax attacks.