A New Decision Support Framework for Managing Foot-and-mouth Disease Epidemics

Research output: Contribution to journalArticleAcademicpeer-review

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 languageEnglish
Pages (from-to)49-62
JournalAnnals of Operations Research
Volume219
Issue number1
DOIs
Publication statusPublished - 2014

Fingerprint

Decision support
Foot and mouth disease
Disease control
Markov process
Damage
Contingency planning
Animals
Process model
Uncertainty
Bayesian forecasting
Livestock production
Threat
Food production
Decision making
Model uncertainty
Forecasting method

Keywords

  • hierarchical markov-processes
  • eradication
  • prevention
  • costs
  • fmd

Cite this

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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",
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A New Decision Support Framework for Managing Foot-and-mouth Disease Epidemics. / Ge, L.; Kristensen, A.R.; Mourits, M.C.M.; Huirne, R.B.M.

In: Annals of Operations Research, Vol. 219, No. 1, 2014, p. 49-62.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - A New Decision Support Framework for Managing Foot-and-mouth Disease Epidemics

AU - Ge, L.

AU - Kristensen, A.R.

AU - Mourits, M.C.M.

AU - Huirne, R.B.M.

PY - 2014

Y1 - 2014

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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KW - eradication

KW - prevention

KW - costs

KW - fmd

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DO - 10.1007/s10479-010-0774-2

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JO - Annals of Operations Research

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