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Quantifying the spatial-temporal diffusion of diseases such as seasonal influenza is difficult at the urban scale for a variety of reasons including the low specificity of the extant data, the heterogenous nature of healthcare seeking behavior and the speed with which diseases spread throughout the city. Nevertheless, the New York City Department of Health and Mental Hygiene’s syndromic surveillance system attempts to detect spatial clusters resulting from outbreaks of influenza. The success of such systems is dependent on there being a discernible spatial-temporal pattern of disease at the neighborhood (sub-urban) scale.

We explore ways to extend global methods such as serfling regression that estimate excess burdens during outbreak periods to characterize these patterns. Traditionally, these methods are aggregated at the national or regional scale and are used only to estimate the total burden of a disease outbreak period. Our extension characterizes the spatial-temporal pattern at the neighborhood scale by day. We then compare our characterizations to prospective spatial cluster detection efforts of our syndromic surveillance system and to demographic covariates.

 

Objective

To develop a novel method to characterize the spatial-temporal pattern of seasonal influenza and then use this characterization to: (1) inform the spatial cluster detection efforts of syndromic surveillance, (2) explore the relationship of spatial-temporal patterns and covariates and (3) inform conclusions made about the burden of seasonal and pandemic influenza. 

Referenced File
Submitted by hparton on

Funded by the Army’s Telemedicine and Advanced Technology Research Center, we developed the BioSINE toolset to provide visualization and collaboration capabilities to improve the accessibility and utility of health surveillance data. Investigation of public health (PH) practitioners’ needs with cognitive engineering methods revealed two key objectives: 

1. To provide analysts and decision makers with an intuitive, visually driven workspace. 

2. To support a web presence to provide rapid updating and facilitate greater interaction with data analysis in the PH community.

To better serve under-resourced PH organizations, both domestic and abroad, it is necessary to minimize information technology requirements and expertise in complex analytic tools.

BioSINE provides decision makers with the ability to create customized visualizations, focus on specific aspects of the data, or conduct hypothesis testing. Users can also view or hide variables, specify data ranges, and filter data relevant to their interests. Figure 1 shows a display in which a user investigated seasonal effects by narrowing the analysis to the summer months. Intuitive filtering is a key characteristic of the application to quickly produce snapshots of local interests.

 

Objective

BioSINE strives to improve situational awareness by making data visualization and collaboration capabilities intuitive and readily available for a wide range of PH stakeholders.

Referenced File
Submitted by hparton on

Influenza-like illness (ILI) data is collected by an Influenza Sentinel Provider Surveillance Network at the state (Iowa, USA) level. Historically, the Iowa Department of Public Health has maintained 19 different influenza sentinel surveillance sites. Because participation is voluntary, locations of the sentinel providers may not reflect optimal geographic placement. This study analyzes two different geographic placement algorithms - a maximal coverage model (MCM) and a K-median model. The MCM operates as follows: given a specified radius of coverage for each of the n candidate surveillance sites, we greedily choose the m sites that result in the highest population coverage. In previous work, we showed that the MCM can be used for site placement. In this paper, we introduce an alternative to the MCM - the K-median model. The K-median model, often called the P-median model in geographic literature, operates by greedily choosing the m sites which minimize the sum of the distances from each person in a population to that person’s nearest site. In other words, it minimizes the average travel distance for a population.

 

Objective

This paper describes an experiment to evaluate the performance of several alternative surveillance site placement algorithms with respect to the standard ILI surveillance system in Iowa.

Referenced File
Submitted by hparton on

The 2009 H1N1 novel flu pandemic demonstrates how a rapidly spreading, contagious illness can affect the world’s population in multiple ways including health, economics, education, transportation, and national security. Pandemic disease and the threat of bio-terrorism are prompting the need for a system that integrates disparate data, makes optimal use of the breadth of available health-related analysis and predictive models, and provides timely guidance to decision makers at multiple levels of responsibility.

 

Objective

Traditional real time surveillance systems such as RODS and ESSENCE have focused on the task of threat detection; however, experience with the use of these systems in pandemic and disaster response settings suggests that a more common application is threat characterization and response management. This paper describes EpiSentry: a novel second generation real-time surveillance software system under development at Lockheed Martin that uses simulation to aid in threat characterization, response management and to provide decision support for disease outbreaks or bio-terror events.

Referenced File
Submitted by hparton on

Situational awareness is important for both early warning and early detection of a disease outbreak, and analytics and tools that furnish information on how an infectious outbreak would either emerge or unfold provide enhanced situational awareness for decision makers/analysts/public health officials, and support planning for prevention or mitigation. Data sharing and expert analysis of incoming information are key to enhancing situational awareness of an unfolding event. In this presentation, we will describe a suite of tools developed at Los Alamos National Laboratory (LANL) that provide actionable information and knowledge for enhanced situational awareness during an unfolding event; The biosurveillance resource directory (BRD), the biosurveillance analytics resource directory (BaRD) and the surveillance window app (SWAP).

Objective

To develop a suite of tools that provides actionable information and knowledge for enhanced situational awareness during an unfolding event such as an infectious disease outbreak.

Submitted by elamb on

A new TB case can be classified as: 1) a source case for transmission leading to other, secondary active TB cases; 2) a secondary case, resulting from recent transmission; or 3) an isolated case, uninvolved in recent transmission (i.e. neither source nor recipient). Source and secondary cases require more intense intervention due to their involvement in a chain of transmission; thus, accurate and rapid classification of new patients should help public health personnel to effectively prioritize control activities. However, currently accepted method for the classification, DNA fingerprint analysis, takes many weeks to produce the results; therefore, public health personnel often solely rely on their intuition to identify the case who is most likely to be involved in transmission. Various clinical and socio-demographic features are known to be associated with TB transmission. By using these readily available data at the time of diagnosis, it is possible to rapidly estimate the probabilities of the case being source, secondary, and isolated.

Objective

To develop and validate a prediction model which estimates the probability of a newly diagnosed tuberculosis (TB) case being involved in ongoing chain of transmission, based on the case's clinical and socio-demographic attributes available at the time of diagnosis.

Referenced File
Submitted by elamb on

Disease screening facilitates the reduction of disease prevalence in two ways: (1) by preventing transmission and (2) allowing for treatment of infected individuals. Hospitals choosing an optimal screening level must weigh the benefits of decreased prevalence against the costs of screening and subsequent treatment. If screening decisions are made by multiple decision units (DU, e.g., hospital wards), they must consider the disease prevalence among admissions to their unit. Thus, the screening decisions made by one DU directly affect the disease prevalence of the other units when patients are shared. Because of this interdependent relationship, one DU may have an incentive to "free-ride" off the screening decisions of others as the disease prevalence declines. On the other hand, DUs may find it futile to invest in screening if they admit a large number of infected patients from neighbors who fail to screen properly. This problem is important in determining the optimal level of unit autonomy, since increasing a unit's level of autonomy in screening effectively increases the total number of DUs.

 

Objective

To analyze optimal disease screening in strategic multi-unit settings, and determine how the level of unit autonomy may effect screening decisions.

Referenced File
Submitted by elamb on

Optimal sequential management of disease outbreaks has been shown to dramatically improve the realized outbreak costs when the number of newly infected and recovered individuals is assumed to be known (1,2). This assumption has been relaxed so that infected and recovered individuals are sampled and therefore the rate of information gain about the infectiousness and morbidity of a particular outbreak is proportional to the sampling rate (3). We study the effect of no recovered sampling and signal delay, features common to surveillance systems, on the costs associated with an outbreak.

Objective

Development of general methodology for optimal decisions during disease outbreaks that incorporate uncertainty in both parameters governing the outbreak and the current outbreak state in terms of the number of current infected, immune, and susceptible individuals.

Referenced File
Submitted by elamb on

Early warning surveillance (EWS) is a key factor in the fight against tropical infectious diseases(1). However, the process of carrying out EWS is complex as it involves several actors and requires the use of diverse human, material and technological resources for data collection, analysis, and diffusion(2). Modern EWS systems make use of state of the art technologies and technics which require much financial input and adequate technological expertise for the users. More so, the culture and habits of users in DCs make it very difficult to run such EWS systems in this milieu. In this paper, we propose a generic early warning surveillance architecture that tackles the stages from just after data collection, through data analysis to feedback and that is adapted to the context of limited resource countries.

Objective

Build a computer aided Early warning disease surveillance system adapted for Developing Countries (DCs) facing limited financial, human, intellectual, organizational, technological, and infrastructural resources.

Submitted by knowledge_repo… on

NC DETECT is the Web-based early event detection and timely public health surveillance system in the North Carolina Public Health Information Network. The reporting system also provides broader public health surveillance reports for emergency department visits related to hurricanes, injuries, asthma,  vaccine-preventable diseases, environmental health and others. NC DETECT receives data on at least a daily basis from four data sources: emergency departments, the statewide poison center, the statewide EMS data collection system, a regional wildlife center and laboratory data from the NC State College of Veterinary Medicine. Data from select urgent care centers are in pilot testing.

 

Objective

Managers of the NC DETECT surveillance system wanted to augment standard tabular Web-based access with a Web-based spatial-temporal interface to allow users to see spatial and temporal characteristics of the surveillance data. Users need to see spatial and temporal patterns in the data to help make decisions about events that require further investigation. The innovative solution using Adobe Flash and Web services to integrate the mapping component with the backend database will be described in this paper.

Referenced File
Submitted by elamb on
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