Deshpande Alina

Using influenza like illness (ILI) data from the repository held by AFHSC, and publically available malaria data we characterized similarities and differences between military and civilian outbreaks. Pete Riley et al. utilized a similar ILI dataset to investigate civilian and military outbreaks similarity during the 2009 flu epidemic. They found, overall, high similarity between civilian and military outbreaks, with military peaking roughly one week after civilian. Our analysis is meant to extend their analysis temporally, geographically, and to see if such trends hold true for other diseases.

Objective

Compare and contrast military and civilian outbreaks for malaria and influenza like illness to identify indicators for early warning and detection

The CDC defines a foodborne outbreak as two or more people getting the same illness from the same contaminated food or drink. These illnesses are often characterized as gastroenteritis until the causative agent is identified (bacterial or viral). Due to the globally interconnected food distribution system, local foodborne disease outbreaks often have global impacts. Therefore, the rapid detection of a gastroenteritis outbreak is of utmost importance for effective control. Situational awareness is important for early warning or detection of a disease outbreak, and tools that provide such information facilitate mitigation actions by civil/military health professionals. We have developed the Surveillance Window app (SWAP), a web based tool that can be used to help understand an unfolding outbreak. The app matches user input information to a library of historical outbreak information and provides context. This presentation will describe our analysis of global civilian and military gastrointestinal outbreaks and the adaptation of the SWAP to enhance situational awareness in the event of such outbreaks.

Objective

The objectives of this project are to identify properties that influence the progression of an outbreak, evaluate the ability of a property-based algorithm to differentiate between military and civilian outbreaks and different pathogens, and develop a decision support tool to enhance situational awareness during an unfolding outbreak.

Each year several thousands contract the seasonal flu, and it is estimated that these viruses are responsible for the deaths of over six thousand individuals [1]. Further, when a new strain is detected (e.g. 2009), the result can be substantially more dramatic [2]. Because of the potential threats flu viruses pose, the United States, like many developed countries, has a very well established flu surveillance system consisting of 10 components collecting laboratory data, mortality data, hospitalization data and sentinel outpatient care data [3]. Currently, this surveillance system is estimated to lag behind the actual seasonal outbreak by one to two weeks. As new data streams come online, it is important to understand what added benefit they bring to the flu surveillance system complex. For data streams to be effective, they should provide data in a more timely fashion or provide additional data that current surveillance systems cannot provide. Two types of multiplexed diagnostic tools designed to test syndromically relevant pathogens and wirelessly upload data for rapid integration and interpretation were evaluated to see how they fit into the influenza surveillance scheme in California.

Objective

Evaluate utility of point of need diagnostic tests in relationship to current standard influenza detection methods.

Infectious disease remains costly in human and economic terms. Effective and timely disease surveillance is a critical component of prevention and mitigation strategies. The limitations of traditional disease surveillance systems have motivated new techniques based upon internet data sources such as search queries and social media. However, 4 challenges remain before internet-based disease surveillance models can be reliably integrated into an operational system: openness, breadth, transferability, and forecasting. We evaluated a new data source, Wikipedia access logs, in these 4 challenges for global disease surveillance and forecasting

Objective

To explore the use of Wikipedia as a data source for disease surveillance.

Epidemiological modeling for infectious disease is useful for disease management and routine implementation needs to be facilitated through better description of models in an operational context. A standardized model characterization process that allows selection or making manual comparisons of available models and their results is currently lacking. Los Alamos National Laboratory (LANL) has developed a comprehensive framework that can be used to characterize an infectious disease model in an operational context. We offer this framework and an associated database to stakeholders of the infectious disease modeling field as a tool for standardizing model

description and facilitating the use of epidemiological models. Such a framework could help the understanding of diverse models by various stakeholders with different preconceptions, backgrounds, expertise, and needs, and can foster greater use of epidemiological models as tools in infectious disease surveillance.

Objectives

1. To develop a comprehensive model characterization framework to describe epidemiological models in an operational context.

2. To apply the framework to characterize “operational” models for specific infectious diseases and provide a web-based directory, the biosurveillance analytics resource directory (BARD) to the global infectious disease surveillance community.