Welcome to the Surveillance Knowledge Repository

Click on a topic under the Key Topic Areas section in the left column, then select a resource  from the list of resources that appear for that topic. You may also search for specific topics by entering one or more keywords in the Search bar. You can filter the search results by Content Type, Year, or Author Name.

Submit

Key Topic Areas

Reset filters

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... Read more

Content type: Abstract

The spatial scan statistic [1] is the most used measure for cluster strenght. The evaluation of all possible subsets of regions in a large dataset is computationally infeasible. Many heuristics have appeared recently to compute approximate values that maximizes the logarithm of the likelihood... Read more

Content type: Abstract

Data obtained through public health surveillance systems are used to detect and locate clusters of cases of diseases in space-time, which may indicate the occurrence of an outbreak or an epidemic. We present a methodology based on adaptive likelihood ratios to compare the null hypothesis (no... Read more

Content type: Abstract

Ordering-based approaches [1,2] and quadtrees [3] have been introduced recently to detect multiple spatial clusters in point event datasets. The Autonomous Leaves Graph (ALG) [4] is an efficient graph-based data structure to handle the communication of cells in discrete domains. This adaptive... Read more

Content type: Abstract

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... Read more

Content type: Abstract

Scan statistics are highly successful for the evaluation of space-time clusters. Recently, concepts from the graph theory were applied to evaluate the set of potential clusters. Wieland et al. introduced a graph theoretical method for detecting arbitrarily shaped clusters on the basis of the ... Read more

Content type: Abstract

Chagas’ disease, caused by the protozoan Trypanosoma cruzi, is spread mostly by Triatominae bugs. High carbon dioxide emission and strong infra-red (IR) radiation are indicative of their presence. Periods of low atmospheric water saturation favor their dispersal, when the bugs’ IR perception is... Read more

Content type: Abstract

Consider the most likely disease cluster produced by any given method, like SaTScan,  for the detection and inference of spatial clusters in a map divided into areas; if this cluster is found to be statistically significant, what could be said of the external areas adjacent to the cluster? Do we... Read more

Content type: Abstract

Multiple data sources are essential to provide reliable information regarding the emergence of potential health threats, compared to single source methods [1,2]. Spatial Scan Statistics have been adapted to analyze multivariate data sources [1]. In this context, only ad hoc procedures have been... Read more

Content type: Abstract

Heuristics to detect irregularly shaped spatial clusters were reviewed recently. The spatial scan statistic is a widely used measure of the strength of clusters. However, other measures may also be useful, such as the geometric compactness penalty, the non-connectivity penalty and other measures... Read more

Content type: Abstract

Pages

Didn't find what you're looking for? Then try searching our archives.

Contact Us

NSSP Community of Practice

Email: syndromic@cste.org

 

This website is supported by Cooperative Agreement # 6NU38OT000297-02-01 Strengthening Public Health Systems and Services through National Partnerships to Improve and Protect the Nation's Health between the Centers for Disease Control and Prevention (CDC) and the Council of State and Territorial Epidemiologists. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of CDC. CDC is not responsible for Section 508 compliance (accessibility) on private websites.

Site created by Fusani Applications