Facilitating the Use of Epidemiological Models for Infectious Disease Surveillance

Abstract

Objective
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.
Introduction
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.
Methods
We define, “operational” as the application of an epidemiological
model to a real-world event for decision support and can be used by
experts and non-experts alike. The term “model” covers three major
types, risk mapping, disease dynamics and anomaly detection.
To develop a framework for characterizing epidemiological models
we collected information via a three-step process: a literature search
of model characteristics, a review of current operational infectious
disease epidemiological models, and subject matter expert (SME)
panel consultation. We limited selection of operational models to
five infectious diseases: influenza, malaria, dengue, cholera and
foot-and-mouth disease (FMD). These diseases capture a variety
of transmission modes, represent high or potentially high epidemic
or endemic burden, and are well represented in the literature. We
also developed working criteria for what attributes can be used to
comprehensively describe an operational model including a model’s
documentation, accessibility, and sustainability.
To apply the model characterization framework, we built the
BARD, which is publicly available (http://brd.bsvgateway.org).
A document was also developed to describe the usability requirements
for the BARD; potential users (and non-users) and use cases are
formally described to explain the scope of use.
Results
1. Framework for model characterization
The framework is divided into six major components (Figure 1):
Model Purpose, Model Objective, Model Scope, Biosurveillance
(BSV) goals, Conceptual Model and Model Utility; each of which
has several sub-categories for characterizing each aspect of a model.
2. Application to model characterization
Models for five infectious diseases—cholera, malaria, influenza,
FMD and dengue were characterized
using the framework and are included in the BARD database. Our
framework characterized disparate models in a streamlined fashion.
Model information could be binned into the same categories, allowing
easy manual comparison and understanding of the models.
3. Development of the BARD
Our model characterization framework was implemented into an
actionable tool which provides specific information about a model
that has been systematically categorized. It allows manual categoryto-
category comparison of multiple models for a single disease and
while the tool does not rank models it provides model information in
a format that allows a user to make a ranking or an assessment of the
utility of the model.
Conclusions
With the model characterization framework we hope to encourage
model developers to start describing the many features of their models
using a common format. We illustrate the application of the framework
through the development of the BARD which is a scientific and
non-biased tool for selecting an appropriate epidemiological model
for infectious disease surveillance. Epidemiological models are not
necessarily being developed with decision makers in mind. This gap
between model developers and decision makers needs to be narrowed
before modeling becomes routinely implemented in decision making.
The characterization framework and the tool developed (BARD) are
a first step towards addressing this gap.
Keywords
epidemiological models; database; decision support