Zheng Hongzhang

A comprehensive electronic medical record (EMR) represents a rich source of information that can be harnessed for epidemic surveillance. At this time, however, we do not know how EMR-based data elements should be combined to improve the performance of surveillance systems. In a manual EMR review of over 15 000 outpatient encounters, we observed that two-thirds of the cases with an acute respiratory infection (ARI) were seen in the emergency room or other urgent care areas, but that these areas received only 15% of total outpatient visits. Because of this seemingly favorable signal-to-noise ratio, we hypothesized that an ARI surveillance system that focused on urgent visits would outperform one that monitored all outpatient visits.

The spatial scan statistic detects significant spatial clusters of disease by maximizing a likelihood ratio statistic over a large set of spatial regions. Several recent approaches have extended spatial scan to multiple data streams. Burkom aggregates actual and expected counts across streams and applies the univariate scan statistic, thus assuming a constant risk for the affected streams. Kulldorff et al. separately apply the univariate statistic to each stream and then aggregate scores across streams, thus assuming independent risks for each affected stream. Neill proposes a ‘fast subset scan’ approach, which maximizes the scan statistic over proximity-constrained subsets of locations, improving the timeliness of detection for irregularly shaped clusters. In the univariate event detection setting, many commonly used scan statistics satisfy the ‘linear-time subset scanning’ (LTSS) property, enabling exact and efficient detection of the highest-scoring space-time clusters.

Objective

We extend the recently proposed ‘fast subset scan’ framework from univariate to multivariate data, enabling computationally efficient detection of irregular space-time clusters even when the numbers of spatial locations and data streams are large. These fast algorithms enable us to perform a detailed empirical comparison of two variants of the multivariate spatial scan statistic, demonstrating the tradeoffs between detection power and characterization accuracy

Effective responses to epidemics of infectious diseases hinge not only on early outbreak detection, but also on an assessment of disease severity. In recent work, we combined previously developed ARI case-detection algorithms (CDA) [1] with text analyses of chest imaging reports to identify ARI patients whose providers thought had pneumonia. In this work, we asked if a surveillance system aimed at patients with pneumonia would outperform one that monitors the full severity spectrum of ARI.

Objective

To determine if influenza surveillance should target all patients with acute respiratory infections (ARI) or only track pneumonia cases.