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ISDS Annual Conference Proceedings 2012. This is an Open Access article distributed under the terms of the Creative Commons Attribution-
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ISDS 2012 Conference Abstracts

Parametric Uncertainty in Intra-Herd Foot-and-Mouth
Disease Epidemiological Models
Eric Nicholas Generous*

Defense Systems Analysis Division, Los Alamos National Laboratory, Los Alamos, NM, USA

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.

Introduction
The rapid transmission and poor control policy response during

recent Foot-and-Mouth disease (FMD) outbreaks have underscored
the need for better decision support tools. At the foundation of these
decision support tools are the epidemiological models that are para-
meterized with the data generated from pathogenesis studies of the
FMD virus that contain contact transmission data. These values being
used to parameterize the model, contrary to assumption, contain a
significant amount of uncertainty, which propagates throughout the
model affecting output. To understand how parametric uncertainty
might affect output, a variety of disease transmission parameters were
generated from contact transmission data and parameterized to an
intra-herd model.

Methods
Data was initially collected and analyzed for papers that could

meet several criteria: they must be contact transmission studies, they
must measure viremia (the level of virus in the blood), and they must
observe clinical signs.

For the studies that met the criteria, tables were constructed and the
following information from each paper was collected: serotype,
strain, animal species, unique animal identifier, unit of measurement
utilized by virus quantification, duration and quantity of viremia, and
the time to first report of clinical signs.

Three different durations of disease states for the latent, sub-clin-
ically infectious, and clinically infectious periods were generated
from the viremia data for each individual animal and grouped in three
ways: by strain of virus, by similar experimental design, and all to-
gether. Gamma, weibull, and normal distributions were fitted to the
data in each group.

The distributions for each group were then used to parameterize a
stochastic, state transition intra-herd model. Output from the model

was analyzed by examining the uncertainty and variance in time to
50% herd infected, time to 2% herd clinically infected, and percent-
age of herd infected at 2% herd clinically infected for each distribu-
tion and group.

Results
There is a lack of a standardized definition for disease state dura-

tions of the Foot-and-Mouth Disease virus in the literature. As a re-
sult, many different models utilize slightly differing values generated
from the same data. This project discovered that depending on the
definitions used to determine the disease state durations, the model
output varied significantly. Additionally, durations of the disease state
periods do not follow a normal distribution as may be assumed by
many modelers, and are more accurately described by distributions
that allow for non-zero skewness.

Conclusions
The data being used to parameterize intra-herd Foot-and-Mouth

disease models contains a significant amount of uncertainty that can
cause the model output to vary significantly. This uncertainty needs
to be clearly communicated to decision makers who use results gen-
erated from FMD intra-herd models and illustrates the need for more
resources to be put into addressing the issue of basic parameters such
as contact rate and disease state duration. Currently no studies have
been conducted on the contact rate of animals on farms and the cur-
rent values used for disease state durations vary drastically depend-
ing on the data and methods used. Without a better understanding of
the basic parameters, even the most advanced models will not be ac-
curate.

Keywords
Control; Foot and Mouth Disease; Epidemiological Model; Uncer-
tainty; Parameters

*Eric Nicholas Generous
E-mail: generous@lanl.gov

Online Journal of Public Health Informatics * ISSN 1947-2579 * http://ojphi.org * 5(1):e147, 2013