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Disease surveillance in animals remains inadequate to detect outbreaks resulting from novel pathogens and potential bioweapons. Mostly relying on confirmed diagnoses, another shortcoming of these systems is their ability to detect outbreaks in a timely manner. We investigated the feasibility of using veterinary laboratory test orders in a prospective system to detect outbreaks of disease earlier compared to traditional reporting methods. IDEXX Laboratories, Inc. automatically transferred daily records of laboratory test orders submitted from veterinary providers in Ohio via a secure file transfer protocol. Test products were classified to appropriate syndromic category using their unique identifying number. Counts of each category by county were analyzed to identify unexpected increases using a cumulative sums method. The results indicated that disease events can be detected through the prospective analysis of laboratory test orders and may provide indications of similar disease events in humans before traditional disease reporting.

There is a critical need for the development of chief complaint (CC) classification systems capable of processing non-English CCs as syndromic surveillance is being increasingly practiced around the world. In this paper, we report on an ongoing effort to develop a Chinese CC classification system based on the analysis of Chinese CCs collected from hospitals in Taiwan. We found that Chinese CCs contain important symptom-related information and provide a valid source of information for syndromic surveillance. Our technical approach consists of two key steps: (a) mapping Chinese CCs to English CCs using a mutual information-based mapping method, and (b) reusing existing English CC classification systems to process translated Chinese CCs. We demonstrate the effectiveness of this proposed approach through a preliminary evaluation study using a real-world dataset.

In epidemiology, contact tracing is a process to control the spread of an infectious disease and identify individuals who were previously exposed to patients with the disease. After the emergence of AIDS, Social Network Analysis (SNA) was demonstrated to be a good supplementary tool for contact tracing. Traditionally, social networks for disease investigations are constructed only with personal contacts. However, for diseases which transmit not only through personal contacts, incorporating geographical contacts into SNA has been demonstrated to reveal potential contacts among patients. In this research, we use Taiwan SARS data to investigate the differences in connectivity between personal and geographical contacts in the construction of social networks for these diseases. According to our results, geographical contacts, which increase the average degree of nodes from 0 to 108.62 and decrease the number of components from 961 to 82, provide much higher connectivity than personal contacts. Therefore, including geographical contacts is important to understand the underlying context of the transmission of these diseases. We further explore the differences in network topology between one-mode networks with only patients and multi-mode networks with patients and geographical locations for disease investigation. We find that including geographical locations as nodes in a social network provides a good way to see the role that those locations play in the disease transmission and reveal potential bridges among those geographical locations and households.

Influenza outbreaks occur seasonally and peak during winter season in temperate zones of the Northern and Southern hemisphere. The occurrence and recurrence of flu epidemics has been alluded to variability in mechanisms such temperature, climate, host contact and traveling patterns [4]. This work promotes a Gaussian–type regression model to study flu outbreak trends and predict new cases based on influenza–like–illness data for France (1985–2005). We show that the proposed models are appropriate descriptors of these outbreaks and can improve the surveillance of diseases such as flu. Our results show that limited data reduces our ability to predict unobserved cases. Based on laboratory surveillance data, we prototype each season according to the dominating virus (H3N2, H1N1, B) and show that high intensity outbreaks are correlated with early peak times. These findings are in accordance with the dynamics observed for influenza outbreaks in the US.

With the growing threat of pandemic influenza, efforts to improve national surveillance to better predict and prevent this disease from affecting the most vulnerable populations are being undertaken. This paper examines the utility of Medicare data to obtain age-specific influenza hospitalization rates for historical analyses. We present a novel approach to describing and analyzing age-specific patterns of hospitalizations using Medicare data and show the implications of a dynamic population age distribution on hospitalization rates. We use these techniques to highlight the utility of implementing a real-time nationwide surveillance system for influenza cases and vaccination, and discuss opportunities to improve the existing system to inform policy and reduce the burden of influenza nationwide.

This study presents and applies a methodology for selecting anomaly detection algorithms for biosurveillance time series data. The study employs both an authentic dataset and a simulated dataset which are freely available for replication of the results presented and for extended analysis. Using this approach, a public health monitor may choose algorithms that will be suited to the scale and behavior of the data of interest based on the calculation of simple discriminants from a limited sample. The tabular classification of typical time series behaviors using these discriminants is achieved using the ROC approach of detection theory, with realistic, stochastic, simulated signals injected into the data. The study catalogues the detection performance of 6 algorithms across data types and shows that for practical alert rates, sensitivity gains of 20% and higher may be achieved by appropriate algorithm selection.

The global health, threatened by emerging infectious diseases, pandemic influenza, and biological warfare, is becoming increasingly dependent on the rapid acquisition, processing, integration and interpretation of massive amounts of data. In response to these pressing needs, new information infrastructures are needed to support active, real time surveillance. Space-time detection techniques may have a high computational cost in both the time and space domains. High performance computing platforms may be the best approach for efficiently computing these techniques. Our work focuses on efficient parallelization of these computations on a Linux Beowolf cluster in order to attempt to meet these real time needs.

An integrated quantitative approach to data assimilation, prediction and anomaly detection over real-time public health surveillance data streams is introduced. The importance of creating dynamical probabilistic models of disease dynamics capable of predicting future new cases from past and present disease incidence data is emphasized. Methods for real-time data assimilation, which rely on probabilistic formulations and on Bayes’ theorem to translate between probability densities for new cases and for model parameters are developed. This formulation creates future outlook with quantified uncertainty, and leads to natural anomaly detection schemes that quantify and detect disease evolution or population structure changes. Finally, the implementation of these methods and accompanying intervention tools in real time public health situations is realized through their embedding in state of the art information technology and interactive visualization environments.

The majority of statistical research on detecting disease outbreaks from prediagnostic data has focused on tools for modeling background behavior of such data, and for monitoring the data for anomaly detection. Because pre-diagnostic data tends to include explainable patterns such as day-of-week, seasonality, and holiday effects, the monitoring process often calls for a two-step algorithm: first, a preprocessing technique is used for deriving a residual series, and then the residuals are monitored using a classic control chart. Most studies tend to apply a single combination of a pre-processing technique with a particular control chart to a particular type of data. Although the choice of preprocessing technique should be driven by the nature of the non-outbreak data and the choice of the control chart by the nature of the outbreak to be detected, often the nature of both is non-stationary and unclear, and varies considerable across different data series. We therefore take an approach that combines algorithms rather than choosing a single one. In particular, we propose a method for combining multiple preprocessing algorithms and a method for combining multiple control charts, both based on linear-programming. We show preliminary results for combining pre-processing techniques, applied to both simulated and authentic syndromic data.

This study decomposes existing temporal aberration detection algorithms into two, sequential stages and investigates the individual impact of each stage on outbreak detection performance. The data forecasting stage (stage 1) generates a prediction of the value of the time series a certain number of time steps in the future based on historical data. The anomaly measure stage (stage 2) compares one or more features of this prediction to the actual time series to compute a measure of the potential anomaly. This decomposition was found not only to yield valuable insight into the effects of the aberration detection algorithms but also to produce novel combinations of data forecasters and anomaly measures with enhanced detection performance.