The utility of syndromic surveillance systems to augment health departments’ traditional surveillance for naturally occurring disease has not been prospectively evaluated.
Objective
In this interim report we describe the signals detected by a real-time ambulatory care-based syndromic surveillance system and discuss their relationship to true outbreaks of illness.
SaTScan is a freely available software that uses the scan statistic to detect clusters in space, time or space-time. SaTScan uses Monte Carlo hypothesis testing in order to produce a p-value for the null hypothesis that no clusters are present. Monte Carlo hypothesis testing can be a powerful tool when asymptotic theoretical distributions are inconvenient or impossible to discover; the main drawback to this approach is that precision for small p-values can only be obtained through greatly increasing the number of Monte Carlo replications, which is both computer-intensive and time consuming. Depending on the type of analysis being done, the number of geographical areas included, the amount of historical data, and the number of Monte Carlo replications, SaTScan can take anywhere from seconds to hours to run. In doing daily surveillance of many syndromes, we need to limit the amount of time it takes to generate each p-value while still retaining enough precision in the p-value to determine how unusual a cluster is. Since the type of analysis done and the geographic regions being used cannot be changed in most cases, we focus here on trying to reduce the number of Monte Carlo replicates needed.
Objective
Our goal was to increase the precision of the p-value produced from SaTScan while reducing the amount of CPU time needed by decreasing the number of Monte Carlo replicates.
Electronic laboratory-based surveillance can significantly improve the diagnostic specificity and response time of traditional infectious disease surveillance. Under the project “Models of Infectious Disease Agent Study”, we wished to evaluate the application of space-time outbreak detection algorithms utilizing SaTScan to a national database of routinely collected microbiology laboratory data.
Objective
This paper describes the application of the WHONET software integrated with SaTScan to the detection of Shigella outbreaks in a national database using a space-time cluster detection algorithm in simulated real-time and comparison of findings to outbreaks reported to the Ministry of Health.
Historical data are essential for development of detection algorithms. Spatio-temporal data, however, are difficult to come by due to variety of issues concerning patient confidentiality. Several approaches have been used to generate benchmark data using statistical methods. Here, we demonstrate how to generate benchmark data using a discrete event model simulating inter- and intra-contact network transmission dynamics of infectious diseases in space and time using publicly available population data.
OBJECTIVE
The objective of this study is to generate benchmark data from a discrete event model simulating the transmission dynamics of an infectious disease within and between contact networks in urban settings using real population data. Such data can be used to test the performance of various temporal and spatio-temporal detection algorithms when real data are scarce or cannot be shared.
There is a need for regular evaluation of surveillance strategies. The emergence of new diagnostic tests and new sources of data, changes in the spatio-temporal distribution of diseases and other factors must be periodically assessed to guarantee that the objectives of the surveillance effort are met. Underlying this evaluation process is the need to increase the efficient use of resources.
OBJECTIVE
We have developed a flexible model which can evaluate surveillance strategies at different hierarchical levels. It identifies key elements in the performance of the surveillance and recommends optimal sampling designs.
In 2006, approximately 6.8 million children and 16.1 million adults were reported to have asthma in the US. The CDC BioSense System currently receives data from >540 hospital emergency departments (EDs; 522 send patient chief complaints and 182 send physician diagnoses), and captures about 11% of all U.S. ED visits.
OBJECTIVE
To describe the potential utility of BioSense data for surveillance of asthma.
Recent extreme weather events have caused serious health and social problems across Europe. During the summer heat waves of 2003 across Europe, France recorded an excess of over 14,000 deaths contributed to heat-related causes. Other countries such as Italy and Portugal experienced over 3,000 and over 2,000 excess deaths respectively. The extreme rises in mortality were initially unobserved by traditional public health surveillance techniques; morbidity related to heat-related exposures also went initially unnoticed by public health authorities.
Real-time monitoring of clinical data has been proposed as one method of surveillance that may be used to alert public health authorities during extreme weather conditions when heat-related morbidity may be higher than expected. Previous studies have shown increased ambulance calls during heat alert conditions in Canada. These potential data sources, including electronic medical records for emergency department visits, are already in existence in many of the countires affected by the heat waves of 2003. Syndromic surveillance methods such as those described by Mandl et al could be applied to these data to help detect when heat-related morbidity and possibly heat-related mortality begins to rise.
Objective
The specific objectives of the study are to evaluate the usefulness of syndromic surveillance data to monitor heat-related morbidity and mortality during extreme weather conditions. During such conditions, real time data monitoring could potentially help drive interventions to reduce morbidity and mortality.
A time periodic geographic disease surveillance system based on a cylindrical space-time scan statistic proposed by Kulldorff [1] has been used extensively for disease surveillance along with the SaTScan software. This statistic is based on a circular spatial scan statistic. On the other hand, many different tests have been proposed to detect purely spatial disease clusters. In particular, some spatial scan statistics such as those developed by Duczmal and Assuncao(2004), Patil and Taillie (2004), and Tango and Takahashi(2005) are aimed at detecting irregularly shaped clusters which may not be detected by the circular spatial scan statistic. However, due to the unlimited geometric freedom of cluster shapes, these statistics have a risk to detect quite large and unlikely peculiarly shaped clusters. A flexible spatial scan statistic proposed by Tango and Takahashi[2], which has been used along with the FleXScan software[3], has a parameter K as the pre-set maximum length of neighbors to be scanned, to be avoid detecting a cluster of unlikely peculiar shape. The flexible spatial scan statistic can be easily extended to space-time alerting methods in syndromic surveillance. Objective: This paper proposes a flexible space-time scan statistic for early detection of disease outbreaks.
This paper describes a comparison between two statistics ñ SaTScan and FleXScan, applying to a data of absentees in primary school in Japan.
Infectious diseases, though initially tend to be limited geographically to a reservoir; a subsequent spatial variation in disease prevalence (including spread & intensity) arises from the underlying differences in physical-biological conditions that support pathogen, its vectors & reservoirs. Different factors like spatial proximity, physical & social connectivity, & local environmental conditions which add to its susceptibility influence the occurrence[2]. In Disease management, analysis of historical data over various aspects of geography, epidemiology, social structures & network dynamics need to be accounted for. Large amounts of data raise issues of data processing, storage, pattern identification, etc. In addition, identifying the source of disease occurrence & its pattern can be of immense value. ST-DM of disease data can be an effective tool for endemic preparedness[3], as it extracts implicit knowledge, spatial & temporal relationships, or other patterns inherent in such databases. Here, Core Region is defined as a set of spatial entities(eg.counties) aggregated over time, which occur frequently at places having high values in a defined region (considering areas of influence around them)[1].
Objective:
This work leverages spatio-temporal data mining (ST-DM), the MiSTIC (Mining Spatio-Temporally Invariant Cores)[1,6] method for infectious disease surveillance, by identifying a) Extent of spatial spread of disease core regions across populations-scale of disease prevalence b) Possible causes of the observed patterns-for better prediction, detection & management of infectious disease & its outbreaks.