Decision-Support

Epidemiological models that simulate the spread of Foot-and-Mouth Disease within a herd are the foundation of decision support tools used by governments to help advise and inform strategy to combat outbreaks. Contact transmission data used to parameterize these models, contrary to assumption, contain a significant amount of variability and uncertainty. The implications of this finding suggest that the resultant model output might not accurately simulate the spread of an outbreak. If this is true, the potential impact due to uncertainty inherent to the decision support tools used by governments might be significant.

Objective

The objective of this project is to understand how parametric un- certainty within intra-herd Foot-and-Mouth disease epidemiological models affects the outbreak simulations and what implications this has on surveillance and control strategy and policy.

Screening for Influenza Like Illness (ILI) is an important infection control activity within emergency departments (ED). When ILI screening is routinely completed in the ED it becomes clinically useful in isolating potentially infectious persons and protecting others from exposure to disease. When routinely collected, ILI screening in an electronic clinical application, with real time reporting, can be useful in Public Health surveillance activities and can support resource allocation decisions e.g. increasing decontamination cleaning. However, the reliability of documentation is unproven. Efforts to support the adoption of ILI screening documentation in a computer application, without mandatory field support, can lead to long term success and increased adherence.

VA is the U.S. federal agency responsible for providing services to America’s Veterans. Within VA, VHA is the organization responsible for administration of health care services. VHA, with 152 Medical Centers and over 900 outpatient clinics located throughout the U.S. and territories, provided care to over 5 million patients in 2011. After the 2009 H1N1 influenza pandemic, OSP, which oversees VA senior level briefing of preparedness issues, conceptualized and initiated SMEC-bio as a protocol-based mechanism to incorporate timely VHA subject matter expertise into leadership decision making via the VA IOC. Previous work has examined collection and integration of data from VA and interagency sources for trend and predictive analyses (1). This current work is an initial assessment of SMEC-bio reporting, which has been in development for the past year and functions on an ad hoc basis for decision support; needs and gaps can be assessed toward a formalized communication plan with the VA IOC.

Objective

To assess Reports sent from the United States VA Subject Matter Expertise Center for Biological Events (SMEC-bio) – a proof-of concept decision support initiative – to the VA Integrated Operations Center (VA IOC).

The ESSENCE demonstration module was built to help DoD health monitors make routine decisions based on disparate evidence sources such as daily counts of ILI-related chief complaints, ratios of positive lab tests for influenza, patient age distribution, and counts of antiviral prescriptions [1]. The module was a population-based (rather than individual-based) Bayesian network (PBN) in that inputs were algorithmic results from these multiple aggregate data streams, and output was the degree of belief that the combined evidence required investigation. The module reduced total alerts substantially and retained sensitivity to the majority of documented outbreaks while clarifying underlying sources of evidence. The current effort was to advance the prototype to production by refining components of the fusion methodology to improve sensitivity while retaining the reduced alert rate.

Objective

The project involves analytic combination of multiple evidence sources to monitor health at hundreds of care facilities. A demonstration module featuring a population-based Bayes Network [1] was refined and expanded for application in the Department of Defense Electronic Surveillance System for Community-Based Epidemics (ESSENCE).

There appears to be a growing number of prioritization exercises, for example of diseases, in health related settings (1). The decision process around these exercises involves comparing competing alternatives, i.e. diseases, and irreducible objectives. In addition to the multi-dimensional nature of the problem, the lack of reliable data, group dynamics associated to the involvement of experts, and the multiplicity of stakeholders, among other contextual factors, add complexity to the decision process. Here we review trends in such prioritization exercises and applications in different settings and for different events of interest, for example the management of emerging risks. Based on our findings, we discuss a conceptual framework based on multi-attribute utility theory presented to the World Organization for Animal Health (OIE) for the modification of its qualitative assessment of veterinary services performance into a quantifiable decision support system.

 

Temporal alerting algorithms commonly used in syndromic surveillance systems are often adjusted for data features such as cyclic behavior but are subject to overfitting or misspecification errors when applied indiscriminately. In a project for the Armed Forces Health Surveillance Center to enable multivariate decision support, we obtained 4.5 years of outpatient, prescription and laboratory test records from all US military treatment facilities. A proof-of-concept project phase produced 16 events with multiple evidence corroboration for comparison of alerting algorithms for detection performance. We used the representative streams from each data source to compare sensitivity of 6 algorithms to injected spikes, and we used all data streams from 16 known events to compare them for detection timeliness.

Objective

For a multi-source decision support application, we sought to match univariate alerting algorithms to surveillance data types to optimize detection performance.