A new decision support framework for managing foot-and-mouth disease epidemics

Lan Ge; Anders Ringgaard Kristensen; Monique C. Mourits; Ruud B. Huirne

doi:10.1007/s10479-010-0774-2

A new decision support framework for managing foot-and-mouth disease epidemics

Lan Ge, Anders Ringgaard Kristensen, Monique C. Mourits, Ruud B. Huirne

7 Citations (Scopus)

Abstract

Animal disease epidemics such as the foot-and-mouth disease (FMD) pose recurrent threat to countries with intensive livestock production. Efficient FMD control is crucial in limiting the damage of FMD epidemics and securing food production. Decision making in FMD control involves a hierarchy of decisions made at strategic, tactical, and operational levels. These decisions are interdependent and have to be made under uncertainty about future development of the epidemic. Addressing this decision problem, this paper presents a new decision-support framework based on multi-level hierarchic Markov processes (MLHMP). The MLHMP model simultaneously optimizes decisions at strategic, tactical, and operational levels, using Bayesian forecasting methods to model uncertainty and learning about the epidemic. As illustrated by the example, the framework is especially useful in contingency planning for future FMD epidemics.

Original language	English
Journal	Annals of Operations Research
Volume	219
Issue number	1
Pages (from-to)	49-62
Number of pages	14
ISSN	0254-5330
DOIs	https://doi.org/10.1007/s10479-010-0774-2
Publication status	Published - Aug 2014

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title = "A new decision support framework for managing foot-and-mouth disease epidemics",

abstract = "Animal disease epidemics such as the foot-and-mouth disease (FMD) pose recurrent threat to countries with intensive livestock production. Efficient FMD control is crucial in limiting the damage of FMD epidemics and securing food production. Decision making in FMD control involves a hierarchy of decisions made at strategic, tactical, and operational levels. These decisions are interdependent and have to be made under uncertainty about future development of the epidemic. Addressing this decision problem, this paper presents a new decision-support framework based on multi-level hierarchic Markov processes (MLHMP). The MLHMP model simultaneously optimizes decisions at strategic, tactical, and operational levels, using Bayesian forecasting methods to model uncertainty and learning about the epidemic. As illustrated by the example, the framework is especially useful in contingency planning for future FMD epidemics.",

author = "Lan Ge and Kristensen, {Anders Ringgaard} and Mourits, {Monique C.} and Huirne, {Ruud B.}",

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AU - Ge, Lan

AU - Kristensen, Anders Ringgaard

AU - Mourits, Monique C.

AU - Huirne, Ruud B.

PY - 2014/8

Y1 - 2014/8

N2 - Animal disease epidemics such as the foot-and-mouth disease (FMD) pose recurrent threat to countries with intensive livestock production. Efficient FMD control is crucial in limiting the damage of FMD epidemics and securing food production. Decision making in FMD control involves a hierarchy of decisions made at strategic, tactical, and operational levels. These decisions are interdependent and have to be made under uncertainty about future development of the epidemic. Addressing this decision problem, this paper presents a new decision-support framework based on multi-level hierarchic Markov processes (MLHMP). The MLHMP model simultaneously optimizes decisions at strategic, tactical, and operational levels, using Bayesian forecasting methods to model uncertainty and learning about the epidemic. As illustrated by the example, the framework is especially useful in contingency planning for future FMD epidemics.

AB - Animal disease epidemics such as the foot-and-mouth disease (FMD) pose recurrent threat to countries with intensive livestock production. Efficient FMD control is crucial in limiting the damage of FMD epidemics and securing food production. Decision making in FMD control involves a hierarchy of decisions made at strategic, tactical, and operational levels. These decisions are interdependent and have to be made under uncertainty about future development of the epidemic. Addressing this decision problem, this paper presents a new decision-support framework based on multi-level hierarchic Markov processes (MLHMP). The MLHMP model simultaneously optimizes decisions at strategic, tactical, and operational levels, using Bayesian forecasting methods to model uncertainty and learning about the epidemic. As illustrated by the example, the framework is especially useful in contingency planning for future FMD epidemics.

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JO - Annals of Operations Research

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A new decision support framework for managing foot-and-mouth disease epidemics

Abstract

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