Simulated anthrax attacks and syndromic surveillance

Mathematical models and analysis tools for risk assessment of unnatural epidemics: a scoping review

Predicting, issuing early warnings, and assessing risks associated with unnatural epidemics (UEs) present significant challenges. These tasks also represent key areas of focus within the field of prevention and control research for UEs. A scoping review was conducted using databases such as PubMed, Web of Science, Scopus, and Embase, from inception to 31 December 2023. Sixty-six studies met the inclusion criteria. Two types of models (data-driven and mechanistic-based models) and a class of analysis tools for risk assessment of UEs were identified. The validation part of models involved calibration, improvement, and comparison. Three surveillance systems (event-based, indicator-based, and hybrid) were reported for monitoring UEs. In the current study, mathematical models and analysis tools suggest a distinction between natural epidemics and UEs in selecting model parameters and warning thresholds. Future research should consider combining a mechanistic-based model with a data-driven model and learning to pursue time-varying, high-precision risk assessment capabilities.

Public Discussion of Anthrax on Twitter: Using Machine Learning to Identify Relevant Topics and Events

Background

Social media allows researchers to study opinions and reactions to events in real time. One area needing more study is anthrax-related events. A computational framework that utilizes machine learning techniques was created to collect tweets discussing anthrax, further categorize them as relevant by the month of data collection, and detect discussions on anthrax-related events.

Objective

The objective of this study was to detect discussions on anthrax-related events and to determine the relevance of the tweets and topics of discussion over 12 months of data collection.

Methods

This is an infoveillance study, using tweets in English containing the keyword “Anthrax” and “Bacillus anthracis”, collected from September 25, 2017, through August 15, 2018. Machine learning techniques were used to determine what people were tweeting about anthrax. Data over time was plotted to determine whether an event was detected (a 3-fold spike in tweets). A machine learning classifier was created to categorize tweets by relevance to anthrax. Relevant tweets by month were examined using a topic modeling approach to determine the topics of discussion over time and how these events influence that discussion.

Results

Over the 12 months of data collection, a total of 204,008 tweets were collected. Logistic regression analysis revealed the best performance for relevance (precision=0.81; recall=0.81; F₁-score=0.80). In total, 26 topics were associated with anthrax-related events, tweets that were highly retweeted, natural outbreaks, and news stories.

Conclusions

This study shows that tweets related to anthrax can be collected and analyzed over time to determine what people are discussing and to detect key anthrax-related events. Future studies are required to focus only on opinion tweets, use the methodology to study other terrorism events, or to monitor for terrorism threats.

Systematic Review and Evaluation of Mathematical Attack Models of Human Inhalational Anthrax for Supporting Public Health Decision Making and Response

BACKGROUND

Anthrax is a potential biological weapon and can be used in an air-borne or mail attack, such as in the attack in the United States in 2001. Planning for such an event requires the best available science. Since large-scale experiments are not feasible, mathematical modelling is a crucial tool to inform planning. The aim of this study is to systematically review and evaluate the approaches to mathematical modelling of inhalational anthrax attack to support public health decision making and response.

METHODS

A systematic review of inhalational anthrax attack models was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria. The models were reviewed based on a set of defined criteria, including the inclusion of atmospheric dispersion component and capacity for real-time decision support.

RESULTS

Of 13 mathematical modelling studies of human inhalational anthrax attacks, there were six studies that took atmospheric dispersion of anthrax spores into account. Further, only two modelling studies had potential utility for real-time decision support, and only one model was validated using real data.

CONCLUSION

The limited modelling studies available use widely varying methods, assumptions, and data. Estimation of attack size using different models may be quite different, and is likely to be under-estimated by models which do not consider weather conditions. Validation with available data is crucial and may improve models. Further, there is a need for both complex models that can provide accurate atmospheric dispersion modelling, as well as for simpler modelling tools that provide real-time decision support for epidemic response.

SCM: a practical tool to implement hospital-based syndromic surveillance

BACKGROUND

Syndromic surveillance has been widely used for the early warning of infectious disease outbreaks, especially in mass gatherings, but the collection of electronic data on symptoms in hospitals is one of the fundamental challenges that must be overcome during operating a syndromic surveillance system. The objective of our study is to describe and evaluate the implementation of a symptom-clicking-module (SCM) as a part of the enhanced hospital-based syndromic surveillance during the 41st World Exposition in Shanghai, China, 2010.

METHODS

The SCM, including 25 targeted symptoms, was embedded in the sentinels' Hospital Information Systems (HIS). The clinicians used SCM to record these information of all the visiting patients, and data were collated and transmitted automatically in daily batches. The symptoms were categorized into seven targeted syndromes using pre-defined criteria, and statistical algorithms were applied to detect temporal aberrations in the data series.

RESULTS

SCM was deployed successfully in each sentinel hospital and was operated during the 184-day surveillance period. A total of 1,730,797 patient encounters were recorded by SCM, and 6.1 % (105,352 visits) met the criteria of the seven targeted syndromes. Acute respiratory and gastrointestinal syndromes were reported most frequently, accounted for 92.1 % of reports in all syndromes, and the aggregated time-series presented an obvious day-of-week variation over the study period. In total, 191 aberration signals were triggered, and none of them were identified as outbreaks after verification and field investigation.

CONCLUSIONS

SCM has acted as a practical tool for recording symptoms in the hospital-based enhanced syndromic surveillance system during the 41st World Exposition in Shanghai, in the context of without a preexisting electronic tool to collect syndromic data in the HIS of the sentinel hospitals.

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.

Disease prediction models and operational readiness

The objective of this manuscript is to present a systematic review of biosurveillance models that operate on select agents and can forecast the occurrence of a disease event. We define a disease event to be a biological event with focus on the One Health paradigm. These events are characterized by evidence of infection and or disease condition. We reviewed models that attempted to predict a disease event, not merely its transmission dynamics and we considered models involving pathogens of concern as determined by the US National Select Agent Registry (as of June 2011). We searched commercial and government databases and harvested Google search results for eligible models, using terms and phrases provided by public health analysts relating to biosurveillance, remote sensing, risk assessments, spatial epidemiology, and ecological niche modeling. After removal of duplications and extraneous material, a core collection of 6,524 items was established, and these publications along with their abstracts are presented in a semantic wiki at http://BioCat.pnnl.gov. As a result, we systematically reviewed 44 papers, and the results are presented in this analysis. We identified 44 models, classified as one or more of the following: event prediction (4), spatial (26), ecological niche (28), diagnostic or clinical (6), spread or response (9), and reviews (3). The model parameters (e.g., etiology, climatic, spatial, cultural) and data sources (e.g., remote sensing, non-governmental organizations, expert opinion, epidemiological) were recorded and reviewed. A component of this review is the identification of verification and validation (V&V) methods applied to each model, if any V&V method was reported. All models were classified as either having undergone Some Verification or Validation method, or No Verification or Validation. We close by outlining an initial set of operational readiness level guidelines for disease prediction models based upon established Technology Readiness Level definitions.

Biowarfare and bioterrorism

Bioterrorism is not only a reality of the times in which we live but bioweapons have been used for centuries. Critical care physicians play a major role in the recognition of and response to a bioterrorism attack. Critical care clinicians must be familiar with the diagnosis and management of the most likely bioterrorism agents, and also be adequately prepared to manage a mass casualty situation. This article reviews the epidemiology, diagnosis, and treatment of the most likely agents of biowarfare and bioterrorism.

Epidemic features affecting the performance of outbreak detection algorithms

BACKGROUND

Outbreak detection algorithms play an important role in effective automated surveillance. Although many algorithms have been designed to improve the performance of outbreak detection, few published studies have examined how epidemic features of infectious disease impact on the detection performance of algorithms. This study compared the performance of three outbreak detection algorithms stratified by epidemic features of infectious disease and examined the relationship between epidemic features and performance of outbreak detection algorithms.

METHODS

Exponentially weighted moving average (EWMA), cumulative sum (CUSUM) and moving percentile method (MPM) algorithms were applied. We inserted simulated outbreaks into notifiable infectious disease data in China Infectious Disease Automated-alert and Response System (CIDARS), and compared the performance of the three algorithms with optimized parameters at a fixed false alarm rate of 5% classified by epidemic features of infectious disease. Multiple linear regression was adopted to analyse the relationship of the algorithms' sensitivity and timeliness with the epidemic features of infectious diseases.

RESULTS

The MPM had better detection performance than EWMA and CUSUM through all simulated outbreaks, with or without stratification by epidemic features (incubation period, baseline counts and outbreak magnitude). The epidemic features were associated with both sensitivity and timeliness. Compared with long incubation, short incubation had lower probability (β* = -0.13, P < 0.001) but needed shorter time to detect outbreaks (β* = -0.57, P < 0.001). Lower baseline counts were associated with higher probability (β* = -0.20, P < 0.001) and longer time (β* = 0.14, P < 0.001). The larger outbreak magnitude was correlated with higher probability (β* = 0.55, P < 0.001) and shorter time (β* = -0.23, P < 0.001).

CONCLUSIONS

The results of this study suggest that the MPM is a prior algorithm for outbreak detection and differences of epidemic features in detection performance should be considered in automatic surveillance practice.

Space–time pattern of hepatitis A in Spain, 1997–2007

In Spain hepatitis A is a compulsory notifiable disease and individual cases are reported to the national epidemiological surveillance network. Incidence rates show variations in different regions. The aim of this study was to analyse the space–time pattern of hepatitis A risk at municipal level in Spain and at global and local levels during the period 1997–2007. At global level we used two estimates of risk: the standardized incidence ratio (SIR) and the posterior probability that the smoothed relative risk is >1 (PP). At local level we used the scan statistic method to analyse the space–time clusters. The SIR and significant PP (>0·8) showed the highest risk concentrated in areas of the Mediterranean coast. The most likely cluster gave a relative risk of 53·530. These spatial statistics methodologies can be complementary tools in the epidemiological surveillance of infectious diseases.

Gumbel based p-value approximations for spatial scan statistics

BACKGROUND

The spatial and space-time scan statistics are commonly applied for the detection of geographical disease clusters. Monte Carlo hypothesis testing is typically used to test whether the geographical clusters are statistically significant as there is no known way to calculate the null distribution analytically. In Monte Carlo hypothesis testing, simulated random data are generated multiple times under the null hypothesis, and the p-value is r/(R + 1), where R is the number of simulated random replicates of the data and r is the rank of the test statistic from the real data compared to the same test statistics calculated from each of the random data sets. A drawback to this powerful technique is that each additional digit of p-value precision requires ten times as many replicated datasets, and the additional processing can lead to excessive run times.

RESULTS

We propose a new method for obtaining more precise p-values with a given number of replicates. The collection of test statistics from the random replicates is used to estimate the true distribution of the test statistic under the null hypothesis by fitting a continuous distribution to these observations. The choice of distribution is critical, and for the spatial and space-time scan statistics, the extreme value Gumbel distribution performs very well while the gamma, normal and lognormal distributions perform poorly. From the fitted Gumbel distribution, we show that it is possible to estimate the analytical p-value with great precision even when the test statistic is far out in the tail beyond any of the test statistics observed in the simulated replicates. In addition, Gumbel-based rejection probabilities have smaller variability than Monte Carlo-based rejection probabilities, suggesting that the proposed approach may result in greater power than the true Monte Carlo hypothesis test for a given number of replicates.

CONCLUSIONS

For large data sets, it is often advantageous to replace computer intensive Monte Carlo hypothesis testing with this new method of fitting a Gumbel distribution to random data sets generated under the null, in order to reduce computation time and obtain much more precise p-values and slightly higher statistical power.

Syndromic surveillance for local outbreaks of lower-respiratory infections: would it work?

Background

Although syndromic surveillance is increasingly used to detect unusual illness, there is a debate whether it is useful for detecting local outbreaks. We evaluated whether syndromic surveillance detects local outbreaks of lower-respiratory infections (LRIs) without swamping true signals by false alarms.

Methods and Findings

Using retrospective hospitalization data, we simulated prospective surveillance for LRI-elevations. Between 1999–2006, a total of 290762 LRIs were included by date of hospitalization and patients place of residence (>80% coverage, 16 million population). Two large outbreaks of Legionnaires disease in the Netherlands were used as positive controls to test whether these outbreaks could have been detected as local LRI elevations. We used a space-time permutation scan statistic to detect LRI clusters. We evaluated how many LRI-clusters were detected in 1999–2006 and assessed likely causes for the cluster-signals by looking for significantly higher proportions of specific hospital discharge diagnoses (e.g. Legionnaires disease) and overlap with regional influenza elevations. We also evaluated whether the number of space-time signals can be reduced by restricting the scan statistic in space or time. In 1999–2006 the scan-statistic detected 35 local LRI clusters, representing on average 5 clusters per year. The known Legionnaires' disease outbreaks in 1999 and 2006 were detected as LRI-clusters, since cluster-signals were generated with an increased proportion of Legionnaires disease patients (p:<0.0001). 21 other clusters coincided with local influenza and/or respiratory syncytial virus activity, and 1 cluster appeared to be a data artifact. For 11 clusters no likely cause was defined, some possibly representing as yet undetected LRI-outbreaks. With restrictions on time and spatial windows the scan statistic still detected the Legionnaires' disease outbreaks, without loss of timeliness and with less signals generated in time (up to 42% decline).

Conclusions

To our knowledge this is the first study that systematically evaluates the performance of space-time syndromic surveillance with nationwide high coverage data over a longer period. The results show that syndromic surveillance can detect local LRI-outbreaks in a timely manner, independent of laboratory-based outbreak detection. Furthermore, since comparatively few new clusters per year were observed that would prompt investigation, syndromic hospital-surveillance could be a valuable tool for detection of local LRI-outbreaks.

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.

A test of syndromic surveillance using a severe acute respiratory syndrome model

OBJECTIVES

We describe a field simulation that was conducted using volunteers to assess the ability of 3 hospitals in a network to manage a large influx of patients with a potentially communicable disease. This drill provided the opportunity to evaluate the ability of the New York City Department of Health and Mental Hygiene's (NYC-DOHMH) emergency department chief complaint syndromic surveillance system to detect a cluster of patients with febrile respiratory illness.

METHODS

The evaluation was a prospective simulation. The clinical picture was modeled on severe acute respiratory syndrome symptoms. Forty-four volunteers participated in the drill as mock patients.

RESULTS

Records from 42 patients (95%) were successfully transmitted to the NYC-DOHMH. The electronic chief complaint for 24 (57%) of these patients indicated febrile or respiratory illness. The drill did not generate a statistical signal in the NYC-DOHMH SaTScan analysis. The 42 drill patients were classified in 8 hierarchical categories based on chief complaints: sepsis (2), cold (3), diarrhea (2), respiratory (20), fever/flu (4), vomit (3), and other (8). The number of respiratory visits, while elevated on the day of the drill, did not appear particularly unusual when compared with the 14-day baseline period used for spatial analyses.

CONCLUSIONS

This drill with a cluster of patients with febrile respiratory illness failed to trigger a signal from the NYC-DOHMH emergency department chief complaint syndromic surveillance system. This highlighted several limitations and challenges to syndromic surveillance monitoring.

Alerting and Surveillance Using Poisons Information Systems (ASPIS): Outcomes from an international working group

BACKGROUND

In recent years, awareness of the risks of chemical and poison exposure has increased, and a number of highly publicized terrorist events have heightened community fears. In particular, there is concern surrounding the potential risk of a covert release, which underpins the need to develop robust methods of population surveillance.

AIMS AND METHODS

This article outlines the proceedings of a working group and focuses on the need for greater international cooperation and understanding of existing toxicological surveillance systems in a variety of countries. Poison control centers have well-established local networks and experienced staff, which make them well-placed to detect chemical and poison release. This needs further development to ensure timely detection of signals, which might be better achieved by robust international networks and consistent use of data collection tools. We illustrate some of the strengths and weaknesses of existing surveillance methods, and present a position statement on the minimum dataset required by future surveillance systems.

CONCLUSIONS

Poison control centers provide a useful platform for developing surveillance activity. Having proposed a number of common aims and objectives, it is hoped that these consensus statements will inform decision makers and stimulate discussion of how international toxicological surveillance programs might best be developed.

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.

Evaluating detection of an inhalational anthrax outbreak

Timely detection of an inhalational anthrax outbreak is critical for clinical and public health management. Syndromic surveillance has received considerable investment, but little is known about how it will perform relative to routine clinical case finding for detection of an inhalational anthrax outbreak. We conducted a simulation study to compare clinical case finding with syndromic surveillance for detection of an outbreak of inhalational anthrax. After simulated release of 1 kg of anthrax spores, the proportion of outbreaks detected first by syndromic surveillance was 0.59 at a specificity of 0.9 and 0.28 at a specificity of 0.975. The mean detection benefit of syndromic surveillance was 1.0 day at a specificity of 0.9 and 0.32 days at a specificity of 0.975. When syndromic surveillance was sufficiently sensitive to detect a substantial proportion of outbreaks before clinical case finding, it generated frequent false alarms.

[Biodefense: a new challenge for microbiology and public health]

Bioterrorism and the potential use of biological weapons has become an important concern of governments and responsible authorities. An example of this threat occurred in 2001 in the USA, when letters were sent containing spores of the agent that produces anthrax; this resulted in some deaths, and caused panic and negative effects on the world economy. If this small-scale event was able to cause such a huge impact, the repercussions of a massive attack could be catastrophic. In many countries, these events have resulted in the implementation of measures directed toward preventing and responding to bioterrorist threats and acts. As a whole, these measures are known as biodefense. This article briefly analyzes several aspects related to detecting and identifying acts of bioterrorism, and considers the biological agents that are implicated. The microbiological diagnosis that allows identification of the causal agent, a key point for taking suitable control measures, is also included.

Outbreak detection through automated surveillance: a review of the determinants of detection

Public health agencies and other groups have invested considerable resources in automated surveillance systems over the last decade. These systems generally follow syndromes in pre-diagnostic data drawn from sources such as emergency department visits. A main goal of syndromic surveillance systems is to detect outbreaks rapidly and the number of studies evaluating outbreak detection has increased recently. This paper reviews these studies with the goal of identifying the determinants of outbreak detection in automated syndromic surveillance systems. The review identified 35 studies with 22 studies (63%) relying on naturally occurring outbreaks and 13 studies (37%) relying on simulated outbreaks. In general, the results from these studies suggest that syndromic surveillance systems are capable of detecting some types of disease outbreaks rapidly with high sensitivity. The determinants of detection included characteristics of the system and of the outbreak. Influential system characteristics included representativeness, the outbreak detection algorithm, and the specificity of the algorithm. Important outbreak characteristics included the magnitude and shape of the signal and the timing of the outbreak. Future evaluations should aim to address inconsistencies in the evidence noted in this review and to identify the potential influence of other factors on outbreak detection.

The Biological Threat to U.S. Water Supplies: Toward a National Water Security Policy

In addition to providing potable drinking water, U.S. water systems are critical to the maintenance of many vital public services, such as fire suppression and power generation. Disruption of these systems would produce severe public health and safety risks, as well as considerable economic losses. Thus, water systems have been designated as critical to national security by the U.S. government. Previous outbreaks of waterborne disease have demonstrated the vulnerability of both the water supply and the public's health to biological contamination of drinking water. Such experiences suggest that a biological attack, or even a credible threat of an attack, on water infrastructure could seriously jeopardize the public's health, its confidence, and the economic vitality of a community. Despite these recognized vulnerabilities, protecting water supplies from a deliberate biological attack has not been sufficiently addressed. Action in this area has suffered from a lack of scientific understanding of the true vulnerability of water supplies to intentional contamination with bioweapons, insufficient tools for detecting biological agents, and a lack of funds to implement security improvements. Much of what is needed to address the vulnerability of the national water supply falls outside the influence of individual utilities. This includes developing a national research agenda to appropriately identify and characterize waterborne threats and making funds available to implement security improvements.