A model-adjusted space–time scan statistic with an application to syndromic surveillance

Kleinman, Ken; Abrams, Alan; Kulldorff, Martin; Platt, Richard

doi:10.1017/s0950268804003528

Cited by 112 publications

(109 citation statements)

References 12 publications

Supporting

Mentioning

106

Contrasting

Unclassified

Order By: Relevance

“…Additionally, many other variants of the scan statistic have recently been proposed, including Gaussian , robust (Neill & Sabhnani, 2007), model-adjusted (Kleinman, Abrams, Kulldorff, & Platt, 2005), nonparametric (Neill & Lingwall, 2007), and Bayesian (Neill, Moore, & Cooper, 2006, 2007 methods. We believe that some of these more complex methods may further improve detection performance, and we are in the process of conducting a large-scale evaluation of these methods using hospital Emergency Department and over-the-counter medication sales data.…”

Section: Discussionmentioning

confidence: 99%

Expectation-based scan statistics for monitoring spatial time series data

Neill

2009

International Journal of Forecasting

View full text Add to dashboard Cite

We consider the simultaneous monitoring of a large number of spatially localized time series in order to detect emerging spatial patterns. For example, in disease surveillance, we detect emerging outbreaks by monitoring electronically available public health data, e.g. aggregate daily counts of Emergency Department visits. We propose a two-step approach based on the expectation-based scan statistic: we first compute the expected count for each recent day for each spatial location, then find spatial regions (groups of nearby locations) where the recent counts are significantly higher than expected. By aggregating information across multiple time series rather than monitoring each series separately, we can improve the timeliness, accuracy, and spatial resolution of detection. We evaluate several variants of the expectation-based scan statistic on the disease surveillance task (using synthetic outbreaks injected into real-world hospital Emergency Department data), and draw conclusions about which models and methods are most appropriate for which surveillance tasks.

show abstract

Section: Discussionmentioning

confidence: 99%

Expectation-based scan statistics for monitoring spatial time series data

Neill

2009

International Journal of Forecasting

View full text Add to dashboard Cite

show abstract

“…While Kulldorff's original spatial scan statistic (Kulldorff 1997) did not take the time dimension into account, later work generalized this method to the "space-time scan statistic" by scanning over variable size temporal windows (Kulldorff et al 1998;Kulldorff 2001). Recent extensions such as the expectationbased scan statistic (Neill et al 2005b) and model-based scan statistic (Kleinman et al 2005) also take the time dimension into account by using historical data to model the expected distribution of counts in each spatial location.…”

Section: Related Workmentioning

confidence: 99%

A multivariate Bayesian scan statistic for early event detection and characterization

Neill

Cooper

2009

Mach Learn

View full text Add to dashboard Cite

We present the multivariate Bayesian scan statistic (MBSS), a general framework for event detection and characterization in multivariate spatial time series data. MBSS integrates prior information and observations from multiple data streams in a principled Bayesian framework, computing the posterior probability of each type of event in each space-time region. MBSS learns a multivariate Gamma-Poisson model from historical data, and models the effects of each event type on each stream using expert knowledge or labeled training examples. We evaluate MBSS on various disease surveillance tasks, detecting and characterizing outbreaks injected into three streams of Pennsylvania medication sales data. We demonstrate that MBSS can be used both as a "general" event detector, with high detection power across a variety of event types, and a "specific" detector that incorporates prior knowledge of an event's effects to achieve much higher detection power. MBSS has many other advantages over previous event detection approaches, including faster computation and easy interpretation and visualization of results, and allows faster and more accurate event detection by integrating information from the multiple streams. Most importantly, MBSS can model and differentiate between multiple event types, thus distinguishing between events requiring urgent responses and other, less relevant patterns in the data.

show abstract

“…Let c st be the observed number of cases in the geographical location s during time period t. Let n st be either the population or the expected number of cases in location s during time period t. Let C = s,t c st be the total number of cases and let N = s,t n st be the total population/expected cases. The expected may be calculated in different ways adjusting for various covariates such as age, gender, urbanicity, day-of-week or seasonal effects [2,17,18].…”

Section: The Univariate Scan Statisticmentioning

confidence: 99%

Multivariate scan statistics for disease surveillance

Kulldorff

Mostashari

Duczmal

et al. 2007

Statistics in Medicine

191

138

View full text Add to dashboard Cite

In disease surveillance, there are often many different data sets or data groupings for which we wish to do surveillance. If each data set is analyzed separately rather than combined, the statistical power to detect an outbreak that is present in all data sets may suffer due to low numbers in each. On the other hand, if the data sets are added by taking the sum of the counts, then a signal that is primarily present in one data set may be hidden due to random noise in the other data sets.In this paper, we present an extention of the spatial and space-time scan statistic that simultaneously incorporates multiple data sets into a single likelihood function, so that a signal is generated whether it occurs in only one or in multiple data sets. This is done by defining the combined log likelihood as the sum of the individual log likelihoods for those data sets for which the observed case count is more than the expected.Using data from the National Bioterrorism Syndromic Surveillance Demonstration Project, we illustrate the new method using physician telephone calls, regular physician visits and urgent care visits by Harvard Pilgrim Health Care members cared for by Harvard Vanguard Medical Associates, a large multi-specialty group practice in Massachusetts. For upper and lower gastrointestinal illness, there were on

show abstract

A model-adjusted space–time scan statistic with an application to syndromic surveillance

Cited by 112 publications

References 12 publications

Expectation-based scan statistics for monitoring spatial time series data

Expectation-based scan statistics for monitoring spatial time series data

A multivariate Bayesian scan statistic for early event detection and characterization

Multivariate scan statistics for disease surveillance

Contact Info

Product

Resources

About