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Time Series

Description

Syndromic surveillance data such as the incidence of influenza-like illness (ILI) is broadly monitored to provide awareness of respiratory disease epidemiology. Diverse algorithms have been employed to find geospatial trends in surveillance data, however, these methods often do not point to a route of transmission. We seek to use correlations between regions in time series data to identify patterns that point to transmission trends and routes. Toward this aim, we employ network analysis to summarize the correlation structure between regions, whereas also providing an interpretation based on infectious disease transmission. 

Cross-correlation has been used to quantify associations between climate variables and disease transmission. The related method of autocorrelation has been widely used to identify patterns in time series surveillance data. This research seeks to improve interpretation of time series data and shed light on the spatial–temporal transmission of respiratory infections based on cross-correlation of ILI case rates.

 

Objective

Time series of ILI events are often used to depict case rates in different regions. We explore the suitability of network visualization to highlight geographic patterns in this data on the basis of cross-correlation of the time series data. 

Submitted by hparton on
Description

Monitoring of long-term infectious disease mortality trends is of great value to national public health systems both in estimation of the efficacy of preventive programs, and in development of the new strategies of preventive measures. In the developed countries, there are a number of studies with long-term time series of infectious disease mortality analysis in epidemiological and historical aspects. Our research was based on the work by Armstrong GL, Conn LA and Pinner RW, 1999. Literature review revealed that such analysis has been never carried out in Ukraine up to now.

Objective: The aim of our work is to determine the main trends and structure in infectious disease mortality in Ukraine over the last 50 years.

Submitted by elamb on
Description

Recently published studies evaluate statistical alerting methods for disease surveillance based on detection of modeled signals in a data background of either authentic historical data or randomized samples. Differences in regional and jurisdictional data, collection and filtering methods, investigation resources, monitoring objectives, and systemrequirements have hindered acceptance of standard monitoring methodology. The signature of a disease outbreak and the baseline data behavior depend on various factors, including population coverage, quality and timeliness of data, symptomatology, and the careseeking behavior of the monitored population. For this reason, statistical process control methods based on standard data distributions or stylized signals may not alert as desired. Practical algorithm evaluation and adjustment may be possible by judging algorithmperformance according to the preferences of experienced human monitors.

 

Objective

This presentation gives a method of monitoring surveillance time series on the basis of the human expert preference. The method does not require detailed history for the current series, modeling expertize, or a well-defined data signal. It is designed for application to many data types and without need for a sophisticated environment or historical data analysis. 

Submitted by hparton on
Description

Current methods for influenza surveillance include laboratory confirmed case reporting, sentinel physician reporting of Influenza-Like-Illness (ILI) and chief-complaint monitoring from emergency departments (EDs).

The current methods for monitoring influenza have drawbacks. Testing for the presence of the influenza virus is costly and delayed. Specific, sentinel physician reporting is subject to incomplete, delayed reporting. Chief complaint (CC) based surveillance is limited in that a patient’s chief complaint will not contain all signs and symptoms of a patient.

A possible solution to the cost, delays, incompleteness and low specificity (for CC) in current methods of influenza surveillance is automated surveillance of ILI using clinician-provided free-text ED reports.

 

Objective

This paper describes an automated ILI reporting system based on natural language processing of transcribed ED notes and its impact on public health practice at the Allegheny County Health Department.

Submitted by hparton on
Description

Time series data involving counts are frequently encountered in many biomedical and public health applications. For example, in disease surveillance, the occurrence of rare infections over time is often monitored by public health officials, and the time series data collected can be used for the purpose of monitoring changes in disease activity. For rare diseases with low infection rates, the observed counts typically contain a high frequency of zeros (zero-inflated), but the counts can also be very large (overdispersed) during an outbreak period. Failure to account for zero-inflation and overdispersion in the data may result in misleading inference and the detection of spurious associations.

 

Objective

The purpose of this study is to develop novel statistical methods to analyze zero-inflated and overdispersed time series consisting of count data.

Submitted by elamb on
Description

Public health officials and epidemiologists have been attempting to eradicate syphilis for decades, but national incidence rates are again on the rise. It has been suggested that the syphilis epidemic in the US is a "rare example of unforced, endogenous oscillations in disease incidence, with an 8-11-yr period that is predicted by the natural dynamics of syphilis infection, to which there is partially protective immunity." While the time series of aggregate case counts seems to support this claim, between 1990 and 2010 there seems to have been a significant change in the spatial distribution of the syphilis epidemic. It is unclear if this change can also be attributed to "endogenous" factors or whether it is due to exogenous factors such as behavioral changes (e.g., the widespread use of the internet for anonymous sexual encounters). For example, it is pointed out that levels of syphilis in 1989 were abnormally high in counties in North Carolina (NC) immediately adjacent to highways. The hypothesis was that this may be due truck drivers and prostitution, and/or the emerging cocaine market. Our results indicate that syphilis distribution in NC has changed since 1989, diffusing away from highway counties.

 

Objective

To study the spatial distribution of syphilis at the county level for specific states and nationally, and to determine how this might have changed over time in order to improve disease surveillance.

Submitted by elamb on
Description

Influenza is a major cause of mortality. In developed countries, mortality is at its highest during winter months, not only as a result of deaths from influenza and pneumonia but also as a result of deaths attributed to other diseases (e.g. cardiovascular disease). Understandably, much of the surveillance of influenza follows predefined geographic regions (e.g. census regions or state boundaries). However, the spread of influenza and its resulting mortality does not respect such boundaries.

 

Objective

To cluster cities in the United States based on their levels of mortality from influenza and pneumonia.

Submitted by elamb on
Description

Influenza-like illness (ILI) data is collected via an Influenza Sentinel Provider Surveillance Network at the state level. Because participation is voluntary, locations of the sentinel providers may not reflect optimal geographic placement. This study analyzes two different geographic placement schemes - a maximal coverage model (MCM) and a K-median model, two location-allocation models commonly used in geographic information systems. The MCM chooses sites in areas with the densest population. The K-median model chooses sites which minimize the average distance traveled by individuals to their nearest site. We have previously shown how a placement model can be used to improve population coverage for ILI surveillance in Iowa when considering the sites recruited by the Iowa Department of Public Health. We extend this work by evaluating different surveillance placement algorithms with respect to outbreak intensity and timing (i.e., being able to capture the start, peak and end of the influenza season).

 

Objective

To evaluate the performance of several sentinel surveillance site placement algorithms for ILI surveillance systems. We explore how these different approaches perform by capturing both the overall intensity and timing of influenza activity in the state of Iowa.

Submitted by elamb on
Description

Cardiovascular event prediction has long been of interest in the practice of intensive care. It has been approached using signal-processing of vital signs [1-4], including the use of graphical models [3,4]. Our approach is novel in making data segmentation as well as hidden state segmentation an unsupervised process, and in simultaneously tracking evolution of multiple vital signs. The proposed models are adaptable to the individual patient's vitals online and in real time, without requiring patient-specific training data if the patient-specific feedback signal is available. Additionally, they can incorporate expert interventions, produce explanations for alarm predictions, and consider effects of medication on state changes to reduce false alert probability.

Objective

To enable prediction of clinical alerts via joint monitoring of multiple vital signs, while enabling timely adaptation of the model to particulars of a given patient.

Submitted by elamb on
Description

Optimal sequential management of disease outbreaks has been shown to dramatically improve the realized outbreak costs when the number of newly infected and recovered individuals is assumed to be known (1,2). This assumption has been relaxed so that infected and recovered individuals are sampled and therefore the rate of information gain about the infectiousness and morbidity of a particular outbreak is proportional to the sampling rate (3). We study the effect of no recovered sampling and signal delay, features common to surveillance systems, on the costs associated with an outbreak.

Objective

Development of general methodology for optimal decisions during disease outbreaks that incorporate uncertainty in both parameters governing the outbreak and the current outbreak state in terms of the number of current infected, immune, and susceptible individuals.

Submitted by elamb on