Brownstein John

Informal surveillance systems like HealthMap are effective at the early detection of outbreaks. However, reliance on informal sources such as news media makes the efficiency of these systems vulnerable to newsroom constraints, namely high-profile disease events drawing reporting resources at the expense of other potential outbreaks and diminished staff over weekends and holidays. To our knowledge, this effect on informal or syndromic surveillance systems has yet to be studied.

Objective

Reporting about large public health events may reduce effective disease surveillance by syndromic or informal surveillance systems. The goal is to determine to what extent this problem exists and characterize situations in which it is likely to occur.

With an estimated 500 million people infected each year, dengue ranks as one of the most significant mosquito-borne viral human diseases, and one of the most rapidly emerging vectorborne diseases. A variety of obstacles including bureaucracy and lack of resources have interfered with timely detection and reporting of dengue cases in many endemic countries. Surveillance efforts have turned to modern data sources, such as Internet search queries, which have been shown to be effective for monitoring influenza-like illnesses. However, few have evaluated the utility of web search query data for other diseases, especially those of high morbidity and mortality or where a vaccine may not exist.

Objective

We aimed to assess whether web search queries are a viable data source for the early detection and monitoring of dengue epidemics.

Chronic diseases are the leading causes of mortality and morbidity for Americans but public health surveillance for these conditions is limited. Health departments currently use telephone interviews, medical surveys, and death certificates to gather information on chronic diseases but these sources are limited by cost, timeliness, limited clinical detail, and/or poor population coverage. Continual and automated extraction, analysis, and summarization of EHR data could advance surveillance in each of these domains.

Objective

Develop methods for automated chronic disease surveillance and visualization using electronic health record (EHR) data.

We have previously shown that timeliness of detection is influenced both by the data source (e.g., ambulatory vs. emergency department) and demographic characteristics of patient populations (e.g., age). Because epidemic waves are thought to move outward from large cities, patient distance from an urban center also may affect disease susceptibility and hence timing of visits. Here, we describe spatial models of local respiratory illness spread across two major metropolitan areas and identify recurring early hotspots of risk. These models are based on methods that explicitly track illness as a traveling wave across local geography.

Objective

To characterize yearly spatial epidemic waves of respiratory illness to identify early hotspots of infection.

HealthMap (www.healthmap.org) is a freely accessible, automated real-time system that monitors, organizes, integrates, filters, and maps online news about emerging diseases. The system performs geographic parsing (“geo-parsing”) of disease outbreaks by assigning incoming alerts to low resolution geographic descriptions, such as  country, with the help of a purposely crafted gazetteer. However, the system is limited by the size of the gazetteer, precluding high resolution assignment of place. In this study, we use the prior knowledge encoded in the gazetteer to expand the capabilities of the geo-parsing system.

 

Objective

Discovering geographic references in text is a task that human readers perform using both their lexical and contextual knowledge. Automating this task for real-time surveillance of informal sources on epidemic intelligence therefore requires efforts beyond dictionary-based pattern matching. Here, we describe an automated approach to learning the particular context in which outbreak locations appear and by this means extending prior knowledge encoded in a gazetteer.

While traditional means of surveillance by governments, multi-national agencies, and institutional networks assist in reporting and confirming infectious disease outbreaks, these formal sources of information are limited by their geographic coverage and timeliness of information flow. In contrast, rapid global reach of electronic communication has resulted in the advent of informal sources of information on outbreaks. Informal resources include discussion sites, online news media, individual and organization reports and even individual search records. The earliest descriptions of the severe acute respiratory syndrome outbreak in Guangdon Province, south China came from informal reports. However, system development to date has been geared toward knowledge management and strategies for interpreting these data are underdeveloped. There is a need to move from simple knowledge reorganization to an analytic approach for disseminating timely yet specific signals.

Objective

Internet-based resources such as discussion sites and online news sources have become invaluable sources for a new wave of surveillance systems. The WHO relies on these informal sources for about 65% of their outbreak investigations. Despite widespread use of unstructured information there has been little, if any, data evaluation.

Though spatio-temporal patterns of influenza spread have often suggested that environmental factors, such as temperature, solar radiation and humidity play a key role, few studies have directly assessed their effect on the timing of annual epidemics. Finkelman et al observed a significant positive relationship between the latitudinal position of temperate countries and epidemic timing. It is hypothesized that during winter months, in temperate regions, decreased skin exposure to sunlight affects immune function by altering the production of certain immunomodulators (e.g. melatonin and Vitamin D3). Other studies have linked temperature and humidity conditions to the rate of transmission of the influenza virus.

Objective 

To assess the strength of the association between peak influenza activity and dew point, average daily temperature, solar radiation, latitude and longitude so that we may better understand the factors that affect virus transmission and/or innate immunity and to determine whether these climate variables should be used as covariates in the surveillance of influenza.

To measure the impact, within administrative geographic areas, of household income on rates of visits due influenza and RSV among children using real-time syndromic surveillance.

This paper describes a modular approach to surveillance system design, ensuring flexibility, scalability and fault tolerance.

Methods for locating spatial clusters of diseases are typically variations of the circular scan statistic method. They restrict the number of potential clusters by considering all circular, rectangular, or elliptical regions, and then apply a likelihood ratio test to evaluate the statistical significance of each potential cluster. Because disease outbreaks may have variable shapes, there has been recent interest in developing methods to detect irregularly-shaped clusters. Starting with a neighborhood graph of the administrative regions in the study area, certain sub-graphs are evaluated. These include all connected subgraphs within a circular window and sub-graphs of the minimum spanning tree of a weighted neighborhood graph formed by deleting one edge. These methods restrict the maximum cluster size or identify large clusters having greater likelihood ratios than true clusters in the data, suggesting a limitation of using the likelihood ratio to detect arbitrarily-shaped clusters.

Objective

A method for detecting spatial clusters of diseases of any shape based on the Euclidean minimum spanning tree is described and compared to the circular scan statistic.