Dataset Information

An adaptive prediction and detection algorithm for multistream syndromic surveillance.

ABSTRACT:

Background

Surveillance of Over-the-Counter pharmaceutical (OTC) sales as a potential early indicator of developing public health conditions, in particular in cases of interest to biosurvellance, has been suggested in the literature. This paper is a continuation of a previous study in which we formulated the problem of estimating clinical data from OTC sales in terms of optimal LMS linear and Finite Impulse Response (FIR) filters. In this paper we extend our results to predict clinical data multiple steps ahead using OTC sales as well as the clinical data itself.

Methods

The OTC data are grouped into a few categories and we predict the clinical data using a multichannel filter that encompasses all the past OTC categories as well as the past clinical data itself. The prediction is performed using FIR (Finite Impulse Response) filters and the recursive least squares method in order to adapt rapidly to nonstationary behaviour. In addition, we inject simulated events in both clinical and OTC data streams to evaluate the predictions by computing the Receiver Operating Characteristic curves of a threshold detector based on predicted outputs.

Results

We present all prediction results showing the effectiveness of the combined filtering operation. In addition, we compute and present the performance of a detector using the prediction output.

Conclusion

Multichannel adaptive FIR least squares filtering provides a viable method of predicting public health conditions, as represented by clinical data, from OTC sales, and/or the clinical data. The potential value to a biosurveillance system cannot, however, be determined without studying this approach in the presence of transient events (nonstationary events of relatively short duration and fast rise times). Our simulated events superimposed on actual OTC and clinical data allow us to provide an upper bound on that potential value under some restricted conditions. Based on our ROC curves we argue that a biosurveillance system can provide early warning of an impending clinical event using ancillary data streams (such as OTC) with established correlations with the clinical data, and a prediction method that can react to nonstationary events sufficiently fast. Whether OTC (or other data streams yet to be identified) provide the best source of predicting clinical data is still an open question. We present a framework and an example to show how to measure the effectiveness of predictions, and compute an upper bound on this performance for the Recursive Least Squares method when the following two conditions are met: (1) an event of sufficient strength exists in both data streams, without distortion, and (2) it occurs in the OTC (or other ancillary streams) earlier than in the clinical data.

SUBMITTER: Najmi AH

PROVIDER: S-EPMC1266371 | biostudies-literature | 2005 Oct

REPOSITORIES: biostudies-literature

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Publications

An adaptive prediction and detection algorithm for multistream syndromic surveillance.

Najmi Amir-Homayoon AH Magruder Steve F SF

BMC medical informatics and decision making 20051012

<h4>Background</h4>Surveillance of Over-the-Counter pharmaceutical (OTC) sales as a potential early indicator of developing public health conditions, in particular in cases of interest to biosurvellance, has been suggested in the literature. This paper is a continuation of a previous study in which we formulated the problem of estimating clinical data from OTC sales in terms of optimal LMS linear and Finite Impulse Response (FIR) filters. In this paper we extend our results to predict clinical d ...[more]

PMID: 16221308

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Project description:BackgroundSurveillance of univariate syndromic data as a means of potential indicator of developing public health conditions has been used extensively. This paper aims to improve the performance of detecting outbreaks by using a background forecasting algorithm based on the adaptive recursive least squares method combined with a novel treatment of the Day of the Week effect.MethodsPrevious work by the first author has suggested that univariate recursive least squares analysis of syndromic data can be used to characterize the background upon which a prediction and detection component of a biosurvellance system may be built. An adaptive implementation is used to deal with data non-stationarity. In this paper we develop and implement the RLS method for background estimation of univariate data. The distinctly dissimilar distribution of data for different days of the week, however, can affect filter implementations adversely, and so a novel procedure based on linear transformations of the sorted values of the daily counts is introduced. Seven-days ahead daily predicted counts are used as background estimates. A signal injection procedure is used to examine the integrated algorithm's ability to detect synthetic anomalies in real syndromic time series. We compare the method to a baseline CDC forecasting algorithm known as the W2 method.ResultsWe present detection results in the form of Receiver Operating Characteristic curve values for four different injected signal to noise ratios using 16 sets of syndromic data. We find improvements in the false alarm probabilities when compared to the baseline W2 background forecasts.ConclusionThe current paper introduces a prediction approach for city-level biosurveillance data streams such as time series of outpatient clinic visits and sales of over-the-counter remedies. This approach uses RLS filters modified by a correction for the weekly patterns often seen in these data series, and a threshold detection algorithm from the residuals of the RLS forecasts. We compare the detection performance of this algorithm to the W2 method recently implemented at CDC. The modified RLS method gives consistently better sensitivity at multiple background alert rates, and we recommend that it should be considered for routine application in bio-surveillance systems.

Project description:BackgroundWaterborne outbreaks are still a risk in high-income countries, and their early detection is crucial to limit their societal consequences. Although syndromic surveillance is widely used for the purpose of detecting outbreaks days earlier than traditional surveillance systems, evidence of the effectiveness of such systems is lacking. Thus, our objective was to conduct a systematic review of the effectiveness of syndromic surveillance to detect waterborne outbreaks.MethodWe searched the Cochrane Library, Medline/PubMed, EMBASE, Scopus, and Web of Science for relevant published articles using a combination of the keywords 'drinking water', 'surveillance', and 'waterborne disease' for the period of 1990 to 2018. The references lists of the identified articles for full-text record assessment were screened, and searches in Google Scholar using the same key words were conducted. We assessed the risk of bias in the included articles using the ROBINS-I tool and PRECEPT for the cumulative body of evidence.ResultsFrom the 1959 articles identified, we reviewed 52 articles, of which 18 met the eligibility criteria. Twelve were descriptive/analytical studies, whereas six were simulation studies. There is no clear evidence for syndromic surveillance in terms of the ability to detect waterborne outbreaks (low sensitivity and high specificity). However, one simulation study implied that multiple sources of signals combined with spatial information may increase the timeliness in detecting a waterborne outbreak and reduce false alarms.ConclusionThis review demonstrates that there is no conclusive evidence on the effectiveness of syndromic surveillance for the detection of waterborne outbreaks, thus suggesting the need to focus on primary prevention measures to reduce the risk of waterborne outbreaks. Future studies should investigate methods for combining health and environmental data with an assessment of needed financial and human resources for implementing such surveillance systems. In addition, a more critical thematic narrative synthesis on the most promising sources of data, and an assessment of the basis for arguments that joint analysis of different data or dimensions of data (e.g. spatial and temporal) might perform better, should be carried out.Trial registrationPROSPERO: International prospective register of systematic reviews. 2019. CRD42019122332 .

Project description:IntroductionDental agenesis (DA), brings together the anodontia, oligodontia, hypodontia, characterized by a deficit in the development of a variable number of teeth. The objectives of the study were to illustrate the phenotypic variability of non-syndromic DA, to identify cases of DA with hereditary genetic transmission, and establish the mode of DA genetic pattern in these cases, together with the determination of DA prevalence in the population group study.Patients, materials and methodsThe cross-sectional observational study was performed on a mixed population group, consisting of 861 Caucasian patients, between January 2018-December 2019. The clinical evaluation protocol of patients with DA, used to illustrate their phenotype, included the following stages: oral examination, photographic examination, and radiological examination. The evaluation protocol specific to the family genetic study of patients with DA, involved the following three stages: family survey, construction of the family tree and analysis of the pedigree structure.ResultsThe prevalence of DA in the population group was 2.78%. Regarding the phenotype, DA mainly affected the upper arch (50% of cases); bilateral DA had a significantly increased incidence (83.33% of cases) compared to unilateral form; in most cases (75%), a patient lacked one to two teeth, the lack of two teeth being the most common form (83.33% of cases); the upper lateral incisors were the teeth most frequently involved in DA (31.11% of the total missing teeth). Regarding the family genetic study, hereditary DA with autosomal dominant inheritance was present in 37.50% of cases. In the other cases (62.50%), isolated, sporadic forms of DA were registered, suggesting a spontaneous de novo mutation or a disorder of odontogenesis of a non-genetic nature.ConclusionsWe consider that this study is of interest for current scientific research with applicability in dental medicine, by bringing actual information on the prevalence of non-syndromic DA in South-East Romania, the variety of phenotypic spectrum of DA for this geographic area, and the role of heredity in the DA genetic determinism in the studied population.

Dataset Information

An adaptive prediction and detection algorithm for multistream syndromic surveillance.

Background

Methods

Results

Conclusion

Publications

An adaptive prediction and detection algorithm for multistream syndromic surveillance.

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