Data Analysis

Spatial cluster analysis is considered an important technique for the elucidation of disease causes and epidemiological surveillance. Kulldorff's spatial scan statistic, defined as a likelihood ratio, is the usual measure of the strength of geographic clusters. The circular scan, a particular case of the spatial scan statistic, is currently the most used tool for the detection and inference of spatial clusters of disease.

Kulldorff's spatial scan statistic for aggregated area maps searches for clusters of cases without specifying their size (number of areas) or geographic location in advance. Their statistical significance is tested while adjusting for the multiple testing inherent in such a procedure. However, as is shown in this work, this adjustment is not done in an even manner for all possible cluster sizes. We propose a modification to the usual inference test of the spatial scan statistic, incorporating additional information about the size of the most likely cluster found.

Objective

We propose a modification to the usual inference test of the spatial scan statistic, incorporating additional information about the size of the most likely cluster found.

This report describes an exploratory analysis of the 2009-2010 Zimbabwe measles outbreak based on data publicly available in the World Health Organization's Zimbabwe cholera epidemiological bulletin archive. As of December 12th 2010, the outbreak appears to have ended after it is suspected to have caused 13,783 infections, 693 of those being confirmed IgM positive, and 631 deaths.

Objective

To systematically organize the World Health Organization data on the 2010 measles outbreak in Zimbabwe. To perform a post-hoc exploratory analysis to understand how the outbreak spread geographically and evaluate the effectiveness of a mass vaccination campaign.

Syndromic surveillance involves the analysis of time series of health indicators to identify changes in disease patterns. To this end, statistical modeling is used to reduce systematic data variation. Still, there is variation that cannot be accounted for in this approach, e.g. mass gatherings, extreme weather and other high-profile events. To filter sporadic events, data transformation can be applied, e.g. proportion data from correlated data streams (Peter, Najmi and Burkom, 2011; Reis, Kohane and Mandl, 2007). However, we lack systematic criteria for applying data transformations, e.g. ratios versus geometric means. To develop guidelines, we conducted a power analysis and compared the results with empirical findings (Andersson et al, 2013).

Objective

For the purpose of optimizing baselines for point-source outbreak detection, we carried out a power analysis of the effects of data transformations. More specifically, the aim was to develop statistical criteria for using composite baselines, i.e. ratios and geometric means of data streams. The results were validated by outbreak data on acute gastroenteritis (The Swedish National Telephone Health Service 1177).

This paper describes several interactive data exploration tools for examining surveillance data.

This paper describes a method to predict syndromic data for surveillance of public health using the method of recursive least squares and a new method of correcting for the day of week effect in order to have a prediction of the background upon which detections of actual events can be computed

One of the significant challenges that multi-user biosurveillance systems have is alarm management. Currently deployed syndromic surveillance systems [1–3] have a single user interface. However, different users have different objectives; the alarms that are important for one category of user are irrelevant to the objectives of another category of user. For example, a physician wants to identify disease on an individual-patient level, a county health authority is interested in identifying disease outbreak as early as possible within his local region, while an epidemiologist at the national level is interested in global situational awareness. The objective of a multi-agent decision support system is not only to recognize patterns of epidemiologically significant events but also to indicate their relevance to particular user groups’ objectives. Thus, instead of simply providing alerts of anomaly detections, the system architecture needs to provide analyzed information supporting multiple users’ decisions.

Disease surveillance systems are currently used for the early detection of disease outbreak before diagnosis is confirmed in order to mobilize a rapid response . The fear of epidemics or bioterrorism resulted in the development of systems for the general population; however research efforts for sensitive population groups are missing. Sensitive groups could be considered patients suffering from chronic diseases (such as diabetes and renal failure), elderly people and infants. It is well known that these groups are quite susceptible to diseases that can be easily spread under certain circumstances e.g. in a dialysis room where patients with renal failure receive their regular treatment. In addition to that, several diseases seem to affect them more. Therefore, the development of disease surveillance systems for sensitive population groups is an issue that should be addressed.

Objective

The aim of this study is to reveal the need for developing disease surveillance systems for sensitive populations.

The Veterans Health Administration (VHA) operates over 880 outpatient clinics across the nation. The Johns Hopkins Applied Physics Laboratory’s Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE) utilizes VHA ICD9 coded outpatient visit data for the detection of abnormal patterns of disease occurrence. The hemorrhagic illness (HI) syndrome category in ESSENCE is comprised of 25 different ICD9 codes, including 12 codes specific for viral hemorrhagic fever (VHF) (e.g., ebola, yellow fever, CrimeanCongo hemorrhagic fever, lassa, etc.) and 13 nonspecific conditions (e.g., purpura not otherwise specified (NOS), thrombocytopathy, and coagulation defect NOS).

Objective

We sought to evaluate the functionality of the diagnosis codes which fall into the syndrome category of hemorrhagic illness.