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.Keywords Foot-and-mouth disease (FMD) · Epidemic control · Decision support · Bayesian forecasting · Multi-level hierarchic Markov processes (MLHMP) L. Ge ( ) LEI Wageningen UR, Hollandseweg 1, 6706KN,

From biological models to economic optimization

“…Nevertheless, the use of Bayesian updating in a dynamic context is now quite well described and understood when it comes to normally distributed data, where Nielsen et al (2011) presented a framework. When it comes to categorical variables, however, the concept has not been described very well even though Ge et al (2010Ge et al ( , 2014 applies a Bayesian updating technique to a variable following a Poisson distribution. Bayesian updating for categorical variables in Markov decision processes is certainly an area where more research is desirable.…”

“…A few examples in literature have applied Markov decision processes also at population level (e.g. Ge et al, 2010Ge et al, , 2014Viet et al, 2012) for disease control decisions, but the main application area remains the animal level.…”

“…Only very few attempts have been done to implement such a framework even though Ge et al (2010Ge et al ( , 2014 implemented a population level model for foot and mouth disease control with a latent disease risk sequentially estimated by Bayesian updating.…”

Handbook of Operations Research in Agriculture and the Agri-Food Industry

“…Examples are IT investment status, epidemic situation, infected and month, etc. Stage lengths vary from one day as in Ge et al (2010a) to a year (van Asseldonk et al 1999). Two papers use ordinary MDPs based on value iteration optimizing expected discounted reward and three papers use hierarchical models, with two implemented using the MLHMP software .…”

Generalized Markov models of infectious disease spread: A novel framework for developing dynamic health policies

“…These methods have proven to be efficient and effective for dynamic decision making in a wide variety of areas (e.g., medical treatment optimization (Schaefer et al, 2005), economics (Van and Dana, 2003), operations research (Winston, 2003; Bertsekas, 2005; Powell, 2007)). Yet, the use of these techniques for assisting the selection of infectious disease control strategies is limited (to the best of our knowledge, Lefevre (1981), Ludkovski and Niemi (2010) and Ge et al (2010) are the only examples). In part, this may reflect the failure of existing models of infectious diseases to satisfy the requirements of dynamic optimization techniques.…”