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Statistical Methods

Description

The ability to rapidly detect any substantial change in disease incidence is of critical importance to facilitate timely public health response and, consequently, to reduce undue morbidity and mortality. Unlike testing methods (1, 2), modeling for spatio-temporal disease surveillance is relatively recent, and this is a very active area of statistical research (3). Models describing the behavior of diseases in space and time allow covariate effects to be estimated and provide better insight into etiology, spread, prediction and control. Most spatio-temporal models have been developed for retrospective analyses of complete data sets (4). However, data in public health registries accumulate over time and sequential analyses of all the data collected so far is a key concept to early detection of disease outbreaks. When the analysis of spatially aggregated data on multiple diseases is of interest, the use of multivariate models accounting for correlations across both diseases and locations may provide a better description of the data and enhance the comprehension of disease dynamics.

Objective

This study deals with the development of statistical methodology for on-line surveillance of small area disease data in the form of counts. As surveillance systems are often focused on more than one disease within a predefined area, we extend the surveillance procedure to the analysis of multiple diseases. The multivariate approach allows for inclusion of correlation across diseases and, consequently, increases the outbreak detection capability of the methodology

Submitted by elamb on
Description

The spatial scan statistic [1] detects significant spatial clusters of disease by maximizing a likelihood ratio statistic F(S) over a large set of spatial regions, typically constrained by shape. The fast localized scan [2] enables scalable detection of irregular clusters by searching over proximity-constrained subsets of locations, using the linear-time subset scanning (LTSS) property to efficiently search over all subsets of each location and its k - 1 nearest neighbors. However, for a fixed neighborhood size k, each of the 2[k] subsets are considered equally likely, and thus the fast localized scan does not take into account the spatial attributes of a subset. Hence, we wish to extend the fast localized scan by incorporating soft constraints which give preference to spatially compact clusters while still considering all subsets within a given neighborhood.

Objective

We present a new method for efficiently and accurately detecting irregularly-shaped outbreaks by incorporating "soft" constraints, rewarding spatial compactness and penalizing sparse regions.

Submitted by elamb on
Description

Syndromic surveillance systems use electronic health-related data to support near-real time disease surveillance. Over the last 10 years, the use of ILI syndromes defined from emergency department (ED) data has become an increasingly accepted strategy for public health influenza surveillance at the local and national levels. However, various ILI definitions exist and few studies have used patient-level data to describe validity for influenza specifically.

Objective

Estimate and compare the accuracy of various ILI syndromes for detecting lab-confirmed influenza in children.

Submitted by elamb on
Description

INDICATOR is a multi-stream open source platform for biosurveillance and outbreak detection, currently focused on Champaign County in Illinois. It has been in production since 2008 and is currently receiving data from emergency department, patient advisory nurse, outpatient convenient care clinic, school absenteeism, animal control, and weather sources. Historical data from some of these sources goes back to 2006.

 

Objective

To examine the correlation between different types of surveillance signals and climate information obtained from a well-defined geographic area.

Submitted by elamb on
Description

The ability to estimate and characterize the burden of disease on a population is important for all public health events, including extreme heat events. Preparing for such events is critical to minimize the associated morbidity and mortality [1, 2]. Since there are delays in obtaining hospital discharge or death records, monitoring of ED visits is the timeliest and an inexpensive method for surveillance of HRI [1]. Aside from air temperature, other environmental variables are used to issue heat advisories based on the heat index, including humidity and wind [3]. The purpose of this study was to evaluate the relationship between HRI ED visits and weather variables as predictors, in Ohio.

Objective

Correlation and linear regression analyses were completed to evaluate the relationship between a heat-related illness (HRI) classifier using emergency department (ED) chief complaint data and specific weather variables as predictors, in Ohio.

Submitted by elamb on
Description

The intrinsic variability that exists in the cases counting data for aggregated-area maps amounts to a corresponding uncertainty in the delineation of the most likely cluster found by methods based on the spatial scan statistics [3]. If this cluster turns out to be statistically significant it allows the characterization of a possible localized anomaly, dividing the areas in the map in two classes: those inside and outside the cluster. But, what about the areas that are outside the cluster but adjacent to it, sometimes sharing a physical border with an area inside the cluster? Should we simply discard them in a disease prevention program? Do all the areas inside the detected cluster have the same priority concerning public health actions? The intensity function [2], a recently introduced visualization method, answers those questions assigning a plausibility to each area of the study map to belong to the most likely cluster detected by the scan statistics. We use the intensity function to study cases of diabetes in Minas Gerais state, Brazil.

Objective

Cluster finder tools like SaTScan[1] usually do not assess the uncertainty in the location of spatial disease clusters. Using the nonparametric intensity function[2], a recently introduced visualization method of spatial clusters, we study the occurrence of several non-contageous diseases in Minas Gerais state, in Southeast Brazil.

Submitted by elamb on
Description

Dengue fever is endemic in over 100 countries and there are an estimated 50 - 100 million cases annually. There is no vaccine for dengue fever yet, and the mortality rate of the severe form of the disease, dengue hemorrhagic fever, ranges from 10-20% but may be greater than 40% if dengue shock occurs. A predictive method for dengue fever would forecast when and where an outbreak will occur before its emergence. This is a challenging task and truly predictive models for emerging infectious diseases are still in their infancy.

 

Objective

This paper addresses the problem of predicting high incidence rates of dengue fever in Peru several weeks in advance.

Referenced File
Submitted by elamb on
Description

The Voronoi Based Scan (VBScan)[1] is a fast method for the detection and inference of point data set space-time disease clusters. A Voronoi diagram is built for points representing population individuals (cases and controls). The number of Voronoi cells boundaries intercepted by the line segment joining two cases points defines the Voronoi distance between those points. That distance is used to approximate the density of the heterogeneous population and build the Voronoi distance Minimum Spanning Tree (MST) linking the cases. The successive removal of its edges generates sub-trees which are the potential space-time clusters, which are evaluated through the scan statistic [2]. Monte Carlo replications of the original data are used to evaluate cluster significance. In the present work we modify VBScan to find the best partition dividing the map into multiple low and high risk regions.

Objective

We describe a method to determine the partition of a map consisting of point event data, identifying all the multiple significant anomalies, which may be of high or low risk, thus monitoring the existence of possible outbreaks.

Submitted by elamb on
Description

Detection of biological threat agents (BTAs) is critical to the rapid initiation of treatment, infection control measures, and public health emergency response plans. Due to the rarity of BTAs, standard methodology for developing syndrome definitions and measuring their validity is lacking.

 

Objective

The objective of this study is to outline and demonstrate the robust methodology used by Geographic Utilization of Artificial Intelligence in Real-Time for Disease Identification and Alert Notification surveillance system to generate and validate BTA profiles.

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