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Syndromic data involves data variation that can be difficult to handle by traditional methods of analysis, e.g. mass gatherings, extreme weather and other high-profile events. For the purpose of optimizing baselines for outbreak detection, we carried out a power analysis of data transformations, e.g. ratios and geometric means. ANOVAs were applied to power simulations, using the gamma distribution to generate baseline and outbreak distributions. The results were compared with empirical findings on syndromic surveillance (Swedish Health Care Direct 1177). The study supports the potential value of data transformations to increase detection power and control for sporadic events.

Describes a tool for the identification of spatio-temporal clusters of legionellosis in Toronto for the purpose of early outbreak detection - the Legionella Alert Mapping Program (LAMP).

This panel convenes an interdisciplinary group of nationally recognized experts in biosurveillance (BSV) to provide their perspectives on integrating novel data sources into standards of practice for various BSV functions by public health agencies at different levels. The discussion will focus on the utility of novel, electronic data sources (e.g., social media) that originate outside of traditional healthcare and public health channels. An audience response "clicker" system will be used to foster audience engagement. In alignment with the 2013 ISDS conference theme, the desired outcome is to identify priorities for research and evaluation that will advance BSV science and practice.

Seasonal autoregressive integrated moving average (ARIMA) models can generate future forecasts, making it a potential method for modeling syndromic data for aberration detection. We built ARIMA models for five routinely monitored syndromes in New York City and tested the models' ability to prospectively detect outbreaks using datasets spiked with simulated outbreaks. Less than 10% of all outbreaks were detected at a fixed alert threshold of 1 signal per 100 days. These models did not perform well in detecting outbreaks and may require frequent monitoring and readjustment of model parameters.

Potential benefits obtained through multi-species surveillance have been widely discussed. We examine an electronic disease reporting solution where cases in humans and other animals are entered into a single platform and accessible in a single database. Real Instances of potential zoonotic disease are described and the practical gains from integrated reporting and analysis are evaluated.

Weekly aggregated local telephone triage data collected from 2007 to 2012 in eight municipalities in Jamtland County, Sweden, were analyzed by seasonal autoregressive integrated moving average (ARIMA) modeling of time series of the two common syndromes acute gastroenteritis (AGE) and influenza-like illness (ILI). Data analysis was done using the free software R (http://www.r-project.org). The seasonal ARIMA model detected AGE and ILI outbreak signals in the majority of the investigated communities. This forecast model can prove to be an important tool for monitoring local levels of AGE and ILI to enable early detection of outbreaks of these conditions.

Despite the number of infections, hospitalizations, and deaths from influenza each year, developing the ability to predict the timing of these outbreaks has remained elusive. We have developed the Above Local Elevated Respiratory illness Threshold (ALERT) algorithm to help public health practitioners develop simple rules to define a period of increased disease incidence in a given location. The ALERT algorithm defines a period of elevated disease incidence in a community or hospital that systematically collects surveillance data on a particular disease. By defining this period of increased risk, the ALERT algorithm enables fast response to emerging outbreaks in healthcare settings.

Incorporating prior knowledge (e.g., the spatial distribution of zip codes and background population effects) into a model using Bayesian methods could potentially improve outbreak detection. We adapted a previously described Bayesian model-based spatiotemporal surveillance technique to daily respiratory syndrome counts in NYC Emergency Department data in 2009, the year of the H1N1 influenza pandemic. Citywide, 56 alarms were produced across 15 zip codes, all during days of elevated respiratory visits. Future work includes evaluating our choice of baseline length, considering other alarm thresholds, and conducting a formal evaluation of the method across five syndromes in NYC.

International audience; In response to increasing risks of emerging infectious diseases, syndromic surveillance can be a suitable approach to detect outbreaks of such diseases across a large territory in an early phase. To implement a syndromic surveillance system, the primary challenge is to find appropriate health-related data. The objective of this study was to evaluate whether routinely collected dates of reproductive events in dairy cattle could be used to build indicators of health anomalies for syndromic surveillance. The evaluation was performed on data collected in France between 2003 and 2009. First, a set of 5 indicators was proposed to assess several types of reproductive disorders. For each indicator, the demographic coverage over the total number of cattle at risk was analyzed in time and space. Second, the ability to detect an emerging disease in an early phase was retrospectively evaluated during epidemics of bluetongue serotypes 1 and 8 (BTV-1, BTV-8) in France in 2007 and 2008. Reproductive indicators were analyzed weekly during these epidemics for each indicator in each infected French district (16 in 2007 and 50 in 2008 out of 94 districts). The indicators were able to detect the BTV epidemics despite their low demographic coverage on a weekly basis relatively to total number of cattle (median=1.21%; range=0-11.7%). Four indicators related to abortions, late embryonic death, and short gestations were abnormally elevated during both BTV epidemics. Median times to abnormal elevations in these indicators were 20 to 71 d after the first notification of clinical signs of BTV by veterinarians. These results demonstrate that reproduction data can be used as indicators of disease emergences, whereas in the specific case of these BTV epidemics, detection via these indicators was later than clinical detection by veterinarians. The emergence of bluetongue in 2007 in France was associated with gestations that were a few days shorter than expected. A short gestation indicator underwent high elevations relative to prior random fluctuations and was the earliest (out of the 4 indicators) to show abnormal elevations, making it possible to detect this emergence.

We compared aberration detection methods requiring historical data to those that require little background by using simulated data. Methods that require less historical data are as sensitive and specific as those that require 3–5 years of data. These simulations can determine which method produces appropriate sensitivity and specificity.

Surveillance systems of contagious diseases record information on cases to monitor incidence of disease and to evaluate effectiveness of interventions. These systems focus on a well-defined population; a key question is whether observed cases are infected through local transmission within the population or whether cases are the result of importation of infection into the population. Local spread of infection calls for different intervention measures than importation of infection. Besides standardized information on time of symptom onset and location of cases, pathogen genotyping or sequencing offers essential information to address this question. Here we introduce a method that takes full advantage of both the genetic and epidemiological data to distinguish local transmission from importation of infection, by comparing inter-case distances in temporal, spatial and genetic data. Cases that are part of a local transmission chain will have shorter distances between their geographical locations, shorter durations between their times of symptom onset and shorter genetic distances between their pathogen sequences as compared to cases that are due to importation. In contrast to generic clustering algorithms, the proposed method explicitly accounts for the fact that during local transmission of a contagious disease the cases are caused by other cases. No pathogen-specific assumptions are needed due to the use of ordinal distances, which allow for direct comparison between the disparate data types. Using simulations, we test the performance of the method in identifying local transmission of disease in large datasets, and assess how sensitivity and specificity change with varying size of local transmission chains and varying overall disease incidence.

Objective We describe a novel graph-based event detection approach which can accurately identify and track dynamic outbreaks (where the affected region changes over time). Our approach enforces soft constraints on temporal consistency, allowing detected regions to grow, shrink, or move while penalizing implausible region dynamics. Using simulated contaminant plumes diffusing through a water distribution system, we demonstrate that our method improves both detection time and spatial-temporal accuracy when tracking dynamic water-borne outbreaks. Introduction Space-time scan statistics are often used to identify emerging spatial clusters of disease cases [1,2]. They operate by maximizing a score function (likelihood ratio statistic) over multiple spatio-temporal regions. The temporal component is typically incorporated by aggregating counts across a given time window, thus assuming that the affected region does not change over time. To relax this hard constraint on spatial-temporal “shape” and increase detection power and accuracy when tracking spreading outbreaks, we implement a new graph-based event detection approach which enables identification of dynamic clusters while enforcing temporal consistency constraints between temporally-adjacent spatial regions. Methods In the subset scanning framework, temporal consistency constraints may be interpreted as influencing the prior probability pit of location i being included in the optimal spatial subset at time t. We model this prior probability for each location as log(pit/(1−pit))=B0+B1Xit−1 where Xit−1 is 1 if location i was included in the previous time step and 0 otherwise, and maximize the penalized log-likelihood ratio over dynamic spatio-temporal regions. Our efficient algorithm incorporates these constraints into the Graph-Scan method [3] by iteratively optimizing the spatial subset for each time slice conditioned on the previous and next slices. Each individual optimization step is made possible by expressing the score function as an additive function (conditioned on the relative risk), which enables the priors to be included while maintaining computational efficiency. Results Outbreak plumes were simulated in a water distribution system for 12 one-hour periods. We assumed noisy binary sensors (with 10% false positive and 90% true positive rates) observed hourly at each pipe junction. Our method (“Dynamic”) was compared to the “Static” method, which aggregates counts across time for each spatial region and is therefore constrained to only return temporal cylinders, and the “Independent” method, which separately optimizes the spatial subset for each time slice without taking temporal consistency into account. The methods were evaluated on spatial-temporal overlap (Figure 1), defined as the number of sensors contained in both the detected and affected space-time regions divided by the number of sensors in either the detected or affected space-time regions. A measure of 1 is a perfect match of spatial subsets across each time window and 0 would reflect disjoint space-time regions. Additionally, average time to detect an outbreak (at a fixed false positive rate of 1/month) was 4.24, 4.56, and 6.65 hours for the dynamic, static, and independent methods respectively. Conclusions Relaxing constraints on spatial-temporal region shape must be done carefully. Allowing independent selection of spatial regions loses important temporal information while hard constraints on the spatial-temporal region will fail to capture the dynamics of the outbreak. Our approach for detecting dynamic space-time clusters, while incorporating temporal consistency constraints, addresses these issues and results in higher spatial-temporal accuracy and detection power. Spatial-temporal overlap for three competing detection methods.

Objective The purpose of our work is to develop a system for automatic contact tracing with the goal of identifying individuals who are most likely infected, even if we do not have direct diagnostic information on their health status. Control of the spread of respiratory pathogens (e.g. novel influenza viruses) in the population using vaccination is a challenging problem that requires quick identification of the infectious agent followed by large-scale production and administration of a vaccine. This takes a significant amount of time. A complementary approach to control transmission is contact tracing and quarantining, which are currently applied to sexually transmitted diseases (STDs). For STDs, identifying the contacts that might have led to disease transmission is relatively easy; however, for respiratory pathogens, the contacts that can lead to transmission include a huge number of face-to-face daily social interactions that are impossible to trace manually. Introduction The evolution of novel influenza viruses in humans is a biological phenomenon that can not be stopped. All existing data suggest that vaccination against the morbidity and mortality of the novel influenza viruses is our best line of defence. Unfortunately, vaccination requires that the infectious agent to be quickly identified and a safe vaccine in large quantities is produced and administered. As was witnessed with the 2009 H1N1 influenza pandemic, these steps took a frustratingly long period during which the novel influenza virus continued its unstoppable and rapid global spreading. In addition to the different vaccination strategies (e.g. random mass vaccination, age structured vaccination), isolation and quarantining of infected individuals is another effective method used by the public health agencies to control the spreading of infectious diseases. Isolation is effective against any infectious disease, however it can be very hard to detect infectious individuals in the population when: Symptoms are ambiguous or easily misdiagnosed (e.g. 2009 H1N1 influenza outbreak shared many symptoms with many other influenza like illnesses)When the symptoms emerge after the individual become infectious. Methods We developed a dynamic Bayesian network model to process sensor information from users’ cellphones together with (possibly incomplete) diagnosis information to track the spread of disease in a population. Our model tracks real-time proximity contacts and can provide public health agencies with the probability of infection for each individual in the model. For testing our algorithm, we used a real-world mobile sensor dataset with 120 individuals collected over a period of 9 months, and we simulated an outbreak. Results We ran several experiments where different sub-populations were “infected” and “diagnosed.” By using the contact information, our model was able to automatically identify individuals in the population who were likely to be infected even though they were not directly “diagnosed” with an illness. Conclusions Automatic contact tracing for respiratory pathogens is a powerful idea, however we have identified several implementation challenges. The first challenge is scalability: we note that a contact tracing system with a hundred thousand individuals requires a Bayesian model with a billion nodes. Bayesian inference on models of this scale is an open problem and an active area of research. The second challenge is privacy protection: although the test data were collected in an academic setting, deploying any system will require appropriate safeguards for user privacy. Nonetheless, our work llustrates the potential for broader use of contact tracing for modeling and controlling disease transmission.

Objective Evaluate the user experience of a novel electronic disease reporting and analysis system deployed across the DoD global laboratory surveillance network Introduction Lessons learned from the 2009 influenza pandemic have driven many changes in the standards and practices of respiratory disease surveillance worldwide. In response to the needs for timely information sharing of emerging respiratory pathogens (1), the DoD Armed Forces Health Surveillance Center (AFHSC) collaborated with the Johns Hopkins University Applied Physics Laboratory (JHU/APL) to develop an Internet-based data management system known as the Respiratory Disease Dashboard (RDD). The goal of the RDD is to provide the AFHSC global respiratory disease surveillance network a centralized system for the monitoring and tracking of lab-confirmed respiratory pathogens, thereby streamlining the data reporting process and enhancing the timeliness for detection of potential pandemic threats. This system consists of a password-protected internet portal that allows users to directly input respiratory specimen data and visualize data on an interactive, global map. Currently, eight DoD partner laboratories are actively entering respiratory pathogen data into the RDD, encompassing specimens from sentinel sites in eleven countries: Cambodia, Colombia, Kenya, Ecuador, Egypt, Honduras, Nicaragua, Paraguay, Peru, Uganda, and the United States. A user satisfaction survey was conducted to guide further development of the RDD and to support other disease surveillance efforts at the AFHSC. Methods User training was provided to partner laboratories during a transition of data submission from Excel spreadsheet to RDD electronic data entry between November 2011 and May 2012. A user experience survey was distributed to the participating laboratories in August 2012 and based on the experience of 139 entries. The survey adopted elements of the SWOT (Strength-Weaknesses-Opportunities-Threats) analysis to determine the system’s strengths and weaknesses as well as to solicit users’ perspectives on the efficiency of the system in assisting with disease surveillance data entry and visualization. Questionnaires in an open-ended (free-text response) format were distributed to all eight participating laboratories. Common themes were identified based on the solicited responses. Results Although only four of eight participating laboratory partners replied to the survey (50% survey response rate), all survey were completed without any omission of questions (100% completion rate). 2/25 (8%) total responses were neutral comments and therefore omitted in the thematic analysis (Table 1). In general, there was a distinct dichotomy in opinion between overseas laboratories and domestic laboratories with regard to the usefulness of the RDD, with overseas laboratories viewing the RDD as more useful than domestic laboratories. A review of the comparison between weekly specimens submitted to the AFHSC via Excel spreadsheet and data entered directly into the RDD revealed misunderstandings about the meaning of the data entry labels in the RDD interface. It was noted by four laboratories that a “Quick Start” user manual would be useful to clarify the definitions of some data labels. Conclusions Overall, this user experience evaluation has identified the needs for additional training on RDD data entry procedures and a “Quick Start” user manual to support the standardization of surveillance definitions. In general, users appreciate the visualization of the global DoD laboratory network data. This evaluation demonstrated the importance of active participation from data contributors and the invaluable organizational support in the development of the RDD as an electronic disease reporting and analysis system.

Objective Disjunctive anomaly detection (DAD) algorithm [1] can efficiently search across multidimensional biosurveillance data to find multiple simultaneously occurring (in time) and overlapping (across different data dimensions) anomalous clusters. We introduce extensions of DAD to handle rich cluster interactions and diverse data distributions. Introduction Modern biosurveillance data contains thousands of unique time series defined across various categorical dimensions (zipcode, age groups, hospitals). Many algorithms are overly specific (tracking each time series independently would often miss early signs of outbreaks), or too general (detections at state level may lack specificity reflective of the actual process at hand). Disease outbreaks often impact multiple values (disjunctive sets of zipcodes, hospitals, multiple age groups) along subsets of multiple dimensions of data. It is not uncommon to see outbreaks of different diseases occurring simultaneously (e.g. food poisoning and flu) making it hard to detect and characterize the individual events. We proposed Disjunctive Anomaly Detection (DAD) algorithm [1] to efficiently search across millions of potential clusters defined as conjunctions over dimensions and disjunctions over values along each dimension. An example anomalous cluster detectable by DAD may identify zipcode = {z1 or z2 or z3 or z5} and age_group = {child or senior} to show unusual activity in the aggregate. Such conjunctive-disjunctive language of cluster definitions enables finding real-world outbreaks that are often missed by other state-of-art algorithms like What’s Strange About Recent Events (WSARE) [3] or Large Average Submatrix (LAS) [2]. DAD is able to identify multiple interesting clusters simultaneously and better explain complex anomalies in data than those alternatives. Methods We define the observed counts of patients reporting on a given day as a random variable for each unique combination of values along all dimensions. DAD iteratively identifies K subsets of these variables along with corresponding ranges of their values and time intervals that show increased activity that cannot be explained by random fluctuations (K is generally unknown and could be 0). The resulting set of clusters maximizes data likelihood while controlling for overall complexity. We have successfully derived a versatile set of scoring functions that allow Normal, Poisson, Exponential or Non-parametric assumptions about the underlying data distributions, and accommodate additive-scaled, additive-unscaled or multiplicative-scaled models for the clusters. Results We present results of testing DAD on two real-world datasets. One of them contains daily outpatient visit counts from 26 regions in Sri Lanka involving 9 common diseases. The other data contains semi-synthetically generated terrorist activities throughout regions of Afghanistan (Sigacts). Both span multiple years and are representative of data seen in biosurveillance applications. Figure 1 shows DAD systematically outperforming WSARE and LAS. Each algorithm’s parameters were tuned to generate one false positive per month in baseline data. The graphs represent average days-to-detect performance of 100 sets with synthetically injected clusters using additive-scaled (AS), additive-unscaled (AU), and multiplicative-scaled (MS) models of cluster interactions. Conclusions We extend applicability of DAD algorithm to handle wide variety of input data distributions and various outbreak models. DAD efficiently scans over millions of potential outbreak patterns and accurately and timely reports complex outbreak interactions with speed that meets requirements of practical applications.

Objective To predict the performance of outbreak detection algorithms under different circumstances which will guide the method selection and algorithm configuration in surveillance systems, to characterize the dependence of the performance of detection algorithms on the type and severity of outbreak, to develop quantitative evidence about determinants of detection performance. Introduction The choice of outbreak detection algorithm and its configuration can result in important variations in the performance of public health surveillance systems. Our work aims to characterize the performance of detectors based on outbreak types. We are using Bayesian networks (BN) to model the relationships between determinants of outbreak detection and the detection performance based on a significant study on simulated data. Methods The simulated surveillance data that we used was generated by Surveillance Lab of McGill University using Simulation Analysis Platform [1] considering surveillance in an urban area to detect waterborne outbreaks due to the failure of a water treatment plant. We focus on predicting the performance of the C-family of algorithms, because they are widely used, state-of-art outbreak detection algorithms [2]. We investigate the influence of algorithm characteristics and outbreak characteristics in determining outbreak detection performance. The C1, C2, and C3 are distinguished by the configuration of 2 parameters,the guardband and memory. Generally, gradually increasing outbreaks can bias the test statistic upward, so the detection algorithm will fail to flag the outbreak. To avoid this situation, the C2 and C3 use a 2-day gap, guardband, between the baseline interval and the test interval. The C3 includes 2 recent observations, called memory, in the computation of the test statistic. The W2 algorithm is a modified version of the C2 which takes weekly patterns of surveillance time series into account [3]. In the W2, the baseline data is stratified to 2 distinct baselines: one for weekdays, the other for weekends. The W3 includes 2 recent observations of each baseline while calculating the test statistic in the corresponding baseline. We ran the C1, C2, C3, W2, and W3 on 18k simulated time series and measured the sensitivity and specificity of detection. Then we created the training data set of 5400000 instances. Each instance was the result of performance evaluation of an outbreak detection algorithm with a specific setting of parameters. In order to investigate the determinants of detection performance and reveal their effects quantitatively, we used BN to predict the performance based on algorithm characteristics and outbreak characteristics. Results We developed 2 BN models in the Weka machine learning software [4] using 5-fold cross-validation. The first BN determines the effect of the guardband, memory, alerting threshold, and the weekly pattern indicator (0 for C-algorithms, 1 for W-algorithms) and outbreak characteristics (contamination level and duration) on the sensitivity of detection. The value of sensitivity was mapped to 4 classes: (0, 0.3], (0.3, 0.6], (0.6, 0.9], (0.9, 1]. The developed BN correctly classified 67.74% of instances. The misclassification error was 0.9407. The second BN for predicting the specificity of detection correctly classified 95.895% of instances in 10 classes and the misclassification error was 0.2975. Conclusions The contamination level and duration of outbreaks, alerting threshold, memory, guardband, and whether the weekly pattern was considered or not influence the sensitivity and specificity of outbreak detection and given the C-algorithm parameter settings, we can predict outbreak detection performance quantitatively. In future work, we plan to investigate other predictors of performance and study how these predictions can be used in algorithm and policy choices.

Objective For the purpose of developing a national system of outbreak surveillance, we compared local outbreak signals in three sources of syndromic data – telephone triage of acute gastroenteritis (Swedish Health Care Direct 1177), web queries about symptoms of gastrointestinal illness (Stockholm County’s website for healthcare information), and OTC pharmacy sales of anti-diarrhea medication. Introduction A large part of the applied research on syndromic surveillance targets seasonal epidemics, e.g. influenza, winter vomiting disease, rotavirus and RSV, in particular when dealing with preclinical indicators, e.g. web traffic (Hulth et al, 2009). The research on local outbreak surveillance is more limited. Two studies of teletriage data (NHS Direct) have shown positive and negative results respectively (Cooper et al, 2006; Smith et al, 2008). Studies of OTC pharmacy sales have reported similar equivocal performance (Edge et al, 2004; Kirian and Weintraub, 2010). As far as we know, no systematic comparison of data sources with respect to multiple point-source outbreaks has so far been published (cf. Buckeridge, 2007). In the current study, we evaluated the potential of three data sources for syndromic surveillance by analyzing the correspondence between signal properties and point-source outbreak characteristics. Methods The extracted data streams were compared with respect to nine waterborne and foodborne outbreaks in Sweden in 2007–2011. The analysis consisted of three parts: (1) the validation of outbreak signals by comparing signal counts during outbreak and baseline periods, (2) the estimation of detection limits by modeling signal rates (signal-to-case ratios), and (3) the evaluation of early warning potential by means of signal detection analysis. Results The four largest outbreaks generated strong and clear outbreak signals in the 1177 triage data. The two largest outbreaks produced signals in OTC sales of anti-diarrhea. No signals could be identified in the web query data. The outbreak detection limit based on triage data was about 100–1000 cases. For two outbreaks, triage data on diarrhea provided outbreak signals early on, weeks and months respectively, potentially serving the purpose of early warning. Conclusions The sensitivity and specificity were highest for telephone triage data on patient symptoms. It provided the most promising source of syndromic data for surveillance of point-source outbreaks. Currently, a project has been initialized to develop and implement a national system in Sweden for daily syndromic surveillance based on 1177 Health Care Direct, supporting regional and local outbreak detection and investigation.

11 Eylül 2001 saldırılarının ardından, biyolojik teröre karşı erken uyarı sistemleri önem kazanmıştır. Bu bağlamda EARS(Early Abberation Reporting System) geliştirilmiş ve olası bir biyolojik terör saldırısının, aykırı bir durum olarak otomatik tespiti hedeflenmiştir. EARS yazılımının kullandığı klasik algılama yöntemlerinin, yavaş yayılan salgınların kaynağını bulmadaki başarımı düşük olduğundan, araştırmacılar, özellikle Cusum(Cumulative Sum), EWMA(Exponentially Weighting Moving Average), NBC(Negative Binomial Cumulative Sum) gibi istatistiksel süreç yöntemlerini (SPC) kullanarak kıyaslama yapmış; bunun sonucunda sistem başarımının veri kümesine bağımlı olduğu tespit edilmiştir. Bu tez kapsamında, bu amaca dönük olarak, WEB tabanlı bir yazılım aracı geliştirilmiş; bu araç ile, bir araştırmacının elindeki veri kümesine uygun veri ön-işleme ve uygun istatistiksel algılama (salgın tespit) yöntemini belirleyebilmesi amaçlanmıştır. After the 9/11 terrorist attacks, early outbreak detection systems against bio-terrorism have gained a lot of importance. In this context, EARS (Early Abberation Reporting System) has been developed in order to detect automatically bio-terror attacks which can be observed as an anomaly in public health data. Because methods used in EARS have shown weaknesses in detecting slowly propagated epidemics, much more elaborated statistical methods such as Cusum, EWMA, NBC have been developed to replace old methods. Studies have shown that the performance of the method used is closely related to the nature of the data set scanned. In this thesis a WEB based software tool is designed and implemented to compare methods in order to find the adequate one for a given set of data. 99

BACKGROUND\ud The increasing frequency and intensity of dengue outbreaks in endemic and non-endemic countries requires a rational, evidence based response. To this end, we aimed to collate the experiences of a number of affected countries, identify strengths and limitations in dengue surveillance, outbreak preparedness, detection and response and contribute towards the development of a model contingency plan adaptable to country needs.\ud \ud METHODS\ud The study was undertaken in five Latin American (Brazil, Colombia, Dominican Republic, Mexico, Peru) and five in Asian countries (Indonesia, Malaysia, Maldives, Sri Lanka, Vietnam). A mixed-methods approach was used which included document analysis, key informant interviews, focus-group discussions, secondary data analysis and consensus building by an international dengue expert meeting organised by the World Health Organization, Special Program for Research and Training in Tropical Diseases (WHO-TDR).\ud \ud RESULTS\ud Country information on dengue is based on compulsory notification and reporting ("passive surveillance"), with laboratory confirmation (in all participating Latin American countries and some Asian countries) or by using a clinical syndromic definition. Seven countries additionally had sentinel sites with active dengue reporting, some also had virological surveillance. Six had agreed a formal definition of a dengue outbreak separate to seasonal variation in case numbers. Countries collected data on a range of warning signs that may identify outbreaks early, but none had developed a systematic approach to identifying and responding to the early stages of an outbreak. Outbreak response plans varied in quality, particularly regarding the early response. The surge capacity of hospitals with recent dengue outbreaks varied; those that could mobilise additional staff, beds, laboratory support and resources coped best in comparison to those improvising a coping strategy during the outbreak. Hospital outbreak management plans were present in 9/22 participating hospitals in Latin-America and 8/20 participating hospitals in Asia.\ud \ud CONCLUSIONS\ud Considerable variation between countries was observed with regard to surveillance, outbreak detection, and response. Through discussion at the expert meeting, suggestions were made for the development of a more standardised approach in the form of a model contingency plan, with agreed outbreak definitions and country-specific risk assessment schemes to initiate early response activities according to the outbreak phase. This would also allow greater cross-country sharing of ideas.

Background The World Health Organization (WHO) collects and publishes surveillance data and statistics for select diseases, but traditional methods of gathering such data are time and labor intensive. Event-based biosurveillance, which utilizes a variety of Internet sources, complements traditional surveillance. In this study we assess the reliability of Internet biosurveillance and evaluate disease-specific alert criteria against epidemiological data. Methods We reviewed and compared WHO epidemiological data and Argus biosurveillance system data for pandemic (H1N1) 2009 (April 2009 – January 2010) from 8 regions and 122 countries to: identify reliable alert criteria among 15 Argus-defined categories; determine the degree of data correlation for disease progression; and assess timeliness of Internet information. Results Argus generated a total of 1,580 unique alerts; 5 alert categories generated statistically significant (p Conclusion Confirmed pandemic (H1N1) 2009 cases collected by Argus and WHO methods returned consistent results and confirmed the reliability and timeliness of Internet information. Disease-specific alert criteria provide situational awareness and may serve as proxy indicators to event progression and escalation in lieu of traditional surveillance data; alerts may identify early-warning indicators to another pandemic, preparing the public health community for disease events.

Recent advances in information and communication technologies have made the development and operation of complex disease surveillance systems technically feasible, and many systems have been proposed to interpret diverse data sources for health-related signals. Implementing these systems for daily use and efficiently interpreting their output, however, remains a technical challenge. This thesis presents a method for understanding disease surveillance systems structurally, examines four existing systems, and discusses the implications of developing such systems. The discussion is followed by two papers. The first paper describes the design of a national outbreak detection system for daily disease surveillance. It is currently in use at the Swedish Institute for Communicable Disease Control. The source code has been licenced under GNU v3 and is freely available. The second paper discusses methodological issues in computational epidemiology, and presents the lessons learned from a software development project in which a spatially explicit micro-meso-macro model for the entire Swedish population was built based on registry data. QC 20110520

We analyzed retrospectively the use of Physician Desk Reference Database searches to identify epidemics of tularemia, nephropathy, Pogosta disease, and Lyme disease and compared the searches with mandatory laboratory reports to the National Infectious Diseases Register in Finland during 1995. Continuous recording of such searches may be a tool for early detection of epidemics.

We propose a method to generate a warning system for the early detection of time clusters in discrete time series. Two approaches are developed, one using an approximation of the return period of an extreme event, independently of the nature of the data, the other using an estimation of the return period via standard EVT tools after a smoothing of our discrete data into continuous ones. This method allows us to define a surveillance and prediction system which is applied to finance and public health surveillance., Nous proposons une méthode basée sur la notion de quantiles extrêmes pour générer un système d'alertes pour la détection de clusters temporels d'extrêmes dans une série chronologique. A cette fin, nous développons deux approches, l'une utilisant une approximation du temps de retour d'un événement extrême, quelle que soit la nature des données, et l'autre basée sur la théorie classique des valeurs extrêmes après lissage des données discrètes en données continues. Cette méthode permet ainsi de définir un système de surveillance et de prévision. Une illustration en est proposée dans le cadre de la prévision pour des applications en finance ainsi que pour la mise en place d'un système de surveillance en épidémiologie sur données réelles.

We examine some of the methodologies implemented by the Centers for Disease Control and Prevention's (CDC) BioSense program. The program uses data from hospitals and public health departments to detect outbreaks using the Early Aberration Reporting System (EARS). The EARS method W2 allows one to monitor syndrome counts (W2count) from each source and the proportion of counts of a particular syndrome relative to the total number of visits (W2rate). We investigate the performance of the W2r method designed using an empiric recurrence interval (RI) in this dissertation research. An adaptive threshold monitoring method is introduced based on fitting sample data to the underlying distributions, then converting the current value to a Z-score through a p-value. We compare the upper thresholds on the Z-scores required to obtain given values of the recurrence interval for different sets of parameter values. We then simulate one-week outbreaks in our data and calculate the proportion of times these methods correctly signal an outbreak using Shewhart and exponentially weighted moving average (EWMA) charts. Our results indicate the adaptive threshold method gives more consistent statistical performance across different parameter sets and amounts of baseline historical data used for computing the statistics. For the power analysis, the EWMA chart is superior to its Shewhart counterpart in nearly all cases, and the adaptive threshold method tends to outperform the W2 rate method. Two modified W2r methods proposed in the dissertation also tend to outperform the W2r method in terms of the RI threshold functions and in the power analysis. Ph. D.

The manuscript starts with substantial summaries of the thesis (one in English, one in French, each 1700 words long, giving more details than the short summaries presented in the metadata).; The French National Occupational Disease Surveillance and Prevention Network (RNV3P) records on a national Datawarehouse, a structured summary of all consultations realized in the occupational diseases consultation centres of the French university hospitals. The aim of this Thesis is to propose and test methods that RNV3P could use to improve its ability in early detecting "potential emerging occupational diseases", which are defined as associations of "disease x occupational exposure", not previously identified, and which, according to RNV3P data, would appear to require additional investigation. This works starts with the analysis of the surveillance methods and outbreak detection methods used by vigilance systems in other health fields (Part 1). We have selected, tested and compared seven "data mining" methods based on disproportionality measures that are used in pharmacovigilance for the analysis of databases of case reports of spontaneous reporting of suspected adverse drug reactions. Accordingly, a method for detecting signals with "Proportional Reporting Ratio" method (PRR), and then investigating theses signals taking into account data both within and outside the RNV3P database has been suggested (Part 2). A 2nd detecting method of emerging health events, based on the qualitative added value of RNV3P's network of experts, has been formalized (Part 3). Finally, this work lies in the continuum between the outbreak of a health event in the occupational health domain, until it is taken into account in terms of prevention, so as to identify the main strengths and limits of RNV3P for each of theses stages, and to propose some avenues for improvement.; Le Réseau National de Vigilance et de Prévention des Pathologies Professionnelles (RNV3P) enregistre au sein d'une base nationale un résumé structuré de chaque consultation réalisée au sein des centres de consultations de pathologies professionnelles. L'objectif est de proposer et tester des méthodes avec lesquelles le RNV3P pourrait être en mesure de renforcer sa capacité de détection précoce de " pathologies professionnelles potentiellement émergentes ", définies comme étant des associations " pathologie x exposition professionnelle ", non préalablement identifiées, et qui semblent justifier d'une exploration complémentaire de part les données internes du RNV3P. Ce travail débute par une analyse des méthodes de surveillance et de détection d'évènements émergents utilisés au sein d'autres vigilances sanitaires (Partie 1). Sept méthodes de Data Mining utilisées en pharmacovigilance pour explorer les bases de notifications spontanées d'effets indésirables médicamenteux suspectés, ont été identifiées, testées et comparées. Finalement, la méthode globale retenue s'appuie sur la détection de signaux avec la méthode " Proportional Reporting Ratio " (PRR), puis leur investigation secondaire à partir de données internes mais aussi externes à la base (Partie 2). Une 2e méthode de mise en évidence d'évènements de santé émergents valorisant directement l'expertise clinique, point fort du réseau, a été formalisée (Partie 3). Enfin, les forces et limites du RNV3P ont été identifiées pour chacune des étapes entre la survenue d'un phénomène de santé émergent en santé-travail, jusqu'à sa prise en charge en termes de prévention, afin de proposer des pistes d'amélioration.

The manuscript starts with substantial summaries of the thesis (one in English, one in French, each 1700 words long, giving more details than the short summaries presented in the metadata).; The French National Occupational Disease Surveillance and Prevention Network (RNV3P) records on a national Datawarehouse, a structured summary of all consultations realized in the occupational diseases consultation centres of the French university hospitals. The aim of this Thesis is to propose and test methods that RNV3P could use to improve its ability in early detecting "potential emerging occupational diseases", which are defined as associations of "disease x occupational exposure", not previously identified, and which, according to RNV3P data, would appear to require additional investigation. This works starts with the analysis of the surveillance methods and outbreak detection methods used by vigilance systems in other health fields (Part 1). We have selected, tested and compared seven "data mining" methods based on disproportionality measures that are used in pharmacovigilance for the analysis of databases of case reports of spontaneous reporting of suspected adverse drug reactions. Accordingly, a method for detecting signals with "Proportional Reporting Ratio" method (PRR), and then investigating theses signals taking into account data both within and outside the RNV3P database has been suggested (Part 2). A 2nd detecting method of emerging health events, based on the qualitative added value of RNV3P's network of experts, has been formalized (Part 3). Finally, this work lies in the continuum between the outbreak of a health event in the occupational health domain, until it is taken into account in terms of prevention, so as to identify the main strengths and limits of RNV3P for each of theses stages, and to propose some avenues for improvement.; Le Réseau National de Vigilance et de Prévention des Pathologies Professionnelles (RNV3P) enregistre au sein d'une base nationale un résumé structuré de chaque consultation réalisée au sein des centres de consultations de pathologies professionnelles. L'objectif est de proposer et tester des méthodes avec lesquelles le RNV3P pourrait être en mesure de renforcer sa capacité de détection précoce de " pathologies professionnelles potentiellement émergentes ", définies comme étant des associations " pathologie x exposition professionnelle ", non préalablement identifiées, et qui semblent justifier d'une exploration complémentaire de part les données internes du RNV3P. Ce travail débute par une analyse des méthodes de surveillance et de détection d'évènements émergents utilisés au sein d'autres vigilances sanitaires (Partie 1). Sept méthodes de Data Mining utilisées en pharmacovigilance pour explorer les bases de notifications spontanées d'effets indésirables médicamenteux suspectés, ont été identifiées, testées et comparées. Finalement, la méthode globale retenue s'appuie sur la détection de signaux avec la méthode " Proportional Reporting Ratio " (PRR), puis leur investigation secondaire à partir de données internes mais aussi externes à la base (Partie 2). Une 2e méthode de mise en évidence d'évènements de santé émergents valorisant directement l'expertise clinique, point fort du réseau, a été formalisée (Partie 3). Enfin, les forces et limites du RNV3P ont été identifiées pour chacune des étapes entre la survenue d'un phénomène de santé émergent en santé-travail, jusqu'à sa prise en charge en termes de prévention, afin de proposer des pistes d'amélioration.

Motivated by the importance of infectious disease informatics (IDI) and the challenges to IDI system development and data sharing, we design and implement BioPortal, a Web-based IDI system that integrates cross-jurisdictional data to support information sharing, analysis, and visualization in public health. In this paper, we discuss general challenges in IDI, describe BioPortal's architecture and functionalities, and highlight encouraging evaluation results obtained from a controlled experiment that focused on analysis accuracy, task performance efficiency, user information satisfaction, system usability, usefulness, and ease of use.

Information systems are concerned with data capture, storage, analysis and retrieval. In the context of food safety management they are vital to assist decision making in a short time frame, potentially allowing decisions to be made and practices to be actioned in real time. Databases with information on microorganisms pertinent to the identification of foodborne pathogens, response of microbial populations to the environment and characteristics of foods and processing conditions are the cornerstone of food safety management systems. Such databases find application in: ¿ Identifying pathogens in food at the genus or species level using applied systematics in automated ways. ¿ Identifying pathogens below the species level by molecular subtyping, an approach successfully applied in epidemiological investigations of foodborne disease and the basis for national surveillance programs. ¿ Predictive modelling software, such as the Pathogen Modeling Program and Growth Predictor (that took over the main functions of Food Micromodel) the raw data of which were combined as the genesis of an international web based searchable database (ComBase). ¿ Expert systems combining databases on microbial characteristics, food composition and processing information with the resulting ¿pattern match¿ indicating problems that may arise from changes in product formulation or processing conditions. ¿ Computer software packages to aid the practical application of HACCP and risk assessment and decision trees to bring logical sequences to establishing and modifying food safety management practices. In addition there are many other uses of information systems that benefit food safety more globally, including: ¿ Rapid dissemination of information on foodborne disease outbreaks via websites or list servers carrying commentary from many sources, including the press and interest groups, on the reasons for and consequences of foodborne disease incidents. ¿ Active surveillance networks allowing rapid dissemination of molecular subtyping information between public health agencies to detect foodborne outbreaks and limit the spread of human disease. ¿ Traceability of individual animals or crops from (or before) conception or germination to the consumer as an integral part of food supply chain management. ¿ Provision of high quality, online educational packages to food industry personnel otherwise precluded from access to such courses.

Clusters of nosocomial infection often occur undetected, at substantial cost to the medical system and individual patients. We evaluated binary cumulative sum (CUSUM) and moving average (MA) control charts for automated detection of nosocomial clusters. We selected two outbreaks with genotyped strains and used resistance as inputs to the control charts. We identified design parameters for the CUSUM and MA (window size, k, alpha, Beta, p(0), p(1)) that detected both outbreaks, then calculated an associated positive predictive value (PPV) and time until detection (TUD) for sensitive charts. For CUSUM, optimal performance (high PPV, low TUD, fully sensitive) was for 0.1or = alphaor = 0.25 and 0.2or = Betaor = 0.25, with p(0) = 0.05, with a mean TUD of 20 (range 8-43) isolates. Mean PPV was 96.5% (relaxed criteria) to 82.6% (strict criteria). MAs had a mean PPV of 88.5% (relaxed criteria) to 46.1% (strict criteria). CUSUM and MA may be useful techniques for automated surveillance of resistant infections.

Background Early identification of outbreaks remains a key component in continuing to reduce the burden of infectious disease in the United States. Previous studies have applied statistical methods to detect unexpected cases of disease in space or time. The objectives of our study were to assess the ability and timeliness of three spatio-temporal methods to detect known outbreaks of tuberculosis. Methods We used routinely available molecular and surveillance data to retrospectively assess the effectiveness of three statistical methods in detecting tuberculosis outbreaks: county-based log-likelihood ratio, cumulative sums, and a spatial scan statistic. Results Our methods identified 8 of the 9 outbreaks, and 6 outbreaks would have been identified 1–52 months (median = 10 months) before local public health authorities identified them. Assuming no delays in data availability, 46 (59.7%) of the 77 patients in the 9 outbreaks were identified after our statistical methods would have detected the outbreak but before local public health authorities became aware of the problem. Conclusions Statistical methods, when applied retrospectively to routinely collected tuberculosis data, can successfully detect known outbreaks, potentially months before local public health authorities become aware of the problem. The three methods showed similar results; no single method was clearly superior to the other two. Further study to elucidate the performance of these methods in detecting tuberculosis outbreaks will be done in a prospective analysis.