Finding optimal vaccination strategies under parameter uncertainty using stochastic programming

Tanner, Matthew W.; Sattenspiel, Lisa; Ntaimo, Lewis

doi:10.1016/j.mbs.2008.07.006

Cited by 76 publications

(70 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…We ran our tests on two test sets, the first is an application developed in Tanner et al [30] involving the optimal allocation of vaccines under parameter uncertainty. The second is a chance-constrained multistage production planning problem adapted from the standard models in the literature [20].…”

Section: Computational Resultsmentioning

confidence: 99%

“…We have created a test set of random instances extending the deterministic LP devised by Becker and Starczak [5] to find optimal vaccination policies for a population divided into a community of households. The background on this application is available in Tanner et al [30]. We have provided details of the formulation of the chance-constrained problem as well as the probability distributions assumed for the random parameters of the original disease model in Appendix 1 for the interested reader.…”

Section: Optimal Vaccine Allocationmentioning

confidence: 99%

See 1 more Smart Citation

IIS branch-and-cut for joint chance-constrained stochastic programs and application to optimal vaccine allocation

Tanner

Ntaimo

2010

European Journal of Operational Research

Self Cite

View full text Add to dashboard Cite

Section: Computational Resultsmentioning

confidence: 99%

Section: Optimal Vaccine Allocationmentioning

confidence: 99%

IIS branch-and-cut for joint chance-constrained stochastic programs and application to optimal vaccine allocation

Tanner

Ntaimo

2010

European Journal of Operational Research

Self Cite

View full text Add to dashboard Cite

“…The minimax-regret criterion selects the vaccination rate that minimizes maximum regret across all states of nature. Thus, the minimax-regret vaccination rate solves Dðt; sÞ; [11] where D(t, s) was defined in Section 2.2. Proposition 4 derives the vaccination rate that solves this problem.…”

Section: Minimax-regret Vaccinationmentioning

confidence: 99%

“…Tanner et al exposit the model in ref. 11, under the name stochastic programming. They admonish vaccination researchers about the need to recognize uncertainty in policy evaluation, writing (pp.…”

mentioning

confidence: 99%

Vaccination with partial knowledge of external effectiveness

Manski

2010

Proc. Natl. Acad. Sci. U.S.A.

View full text Add to dashboard Cite

Economists studying public policy have generally assumed that the relevant planner knows how policy affects population behavior. Planners typically do not possess all of this knowledge, so there is reason to consider policy formation with partial knowledge of policy impacts. Here I consider choice of a vaccination policy when a planner has partial knowledge of the effect of vaccination on illness rates. To begin, I pose a planning problem whose objective is to minimize the utilitarian social cost of illness and vaccination. The consequences of candidate vaccination rates depend on the extent to which vaccination prevents illness. I study the planning problem when the planner has partial knowledge of the external-response function, which expresses how the illness rate of unvaccinated persons varies with the vaccination rate. I suppose that the planner observes the illness rate of a study population whose vaccination rate has been chosen previously. He knows that the illness rate of unvaccinated persons weakly decreases as the vaccination rate increases, but he does not know the magnitude of the preventive effect of vaccination. In this setting, I first show how the planner can eliminate dominated vaccination rates and then how he can use the minimax or minimaxregret criterion to choose an undominated vaccination rate. Economists and other researchers performing normative study of public policy have typically assumed that the policy maker, or planner, knows how policy affects population outcomes. A standard exercise specifies a set of feasible policies and a social welfare function. The planner is presumed to know the welfare achieved by each policy. The objective of the exercise is to determine the optimal policy.There are many examples. Economists have studied optimal income taxation under the assumption that the planner knows how the tax schedule affects the population distribution of labor supply (1). Economists have also studied optimal criminal justice systems under the assumption that the planner knows how policing and sanctions affect crime rates (2). Researchers studying optimal vaccination against infectious disease have typically assumed the planner knows how vaccination affects illness rates (3-9).Whatever the policy choice may be, inferential problems commonly make it difficult to learn how policy affects outcomes. Perhaps the most fundamental difficulty is the identification problem arising from the unobservability of counterfactual outcomes. At most one can observe the outcomes that occur under realized policies-the outcomes of unrealized policies are logically unobservable. Yet determination of an optimal policy requires comparison of all feasible policies. For this and many other reasons, planners usually have only partial knowledge of the welfare achieved by alternative policies. This limits the relevance of the standard exercise to actual policy analysis.This paper considers choice of a vaccination policy when a planner has partial knowledge of the effect of vaccination on illness rates. There ...

show abstract

“…For example, Tanner et al (2008) presents a stochastic programming framework for finding the optimal vaccination policy for controlling infectious disease epidemics under parameter under uncertainty. They formulated the problem to seek the minimum cost vaccination policy under a chance-constraint.…”

Section: Vaccination Strategies Under Parameter Uncertainty For Emergmentioning

confidence: 99%

Optimization models for resource allocation under perturbation.

Bridgeman

View full text Add to dashboard Cite

who have given me invaluable educational opportunities.iii Optimization Models for Resource allocation are investing in how to make the best use of available but limited resources in order to achieve the best results. In strategic planning, resource allocation is a plan for using available resources, especially in the near future, to achieve the goals of the future. It is a process of allocating resources during the entire planning horizon and among the various units. Resource allocation plans can be decided by using mathematical programming. In this dissertation, the research has been focused on how to allocate resources in the uncertain environment. The mathematical programming formulations for the resource allocation model under severe uncertainty will be studied. In particular, we will focus on solving the stability issues of the traditional probabilistic model. We propose an approach consisting of solving a sequence of convex robust optimization models with unknown-but-bounded random variables along with the stochastic programming to pursue the allocation performance for the expected overall objective value. Our theoretical results show that the proposed approach can always obtain an equivalent or a better expected revenue with the corresponding allocation, while significantly reducing the risk under perturbations. Although this method requires solving two convex mathematical v programming models, both models are solved within a timely manner thanks to their convex model instances and with effective, and less, computationally demanding algorithms.With the increasing threats from public health emergencies, such as earthquakes, tornados, pandemic flus, or terrorist attacks, high attention has been paid to the public health response to a pandemic from federal to national level, together with local health departments, and the health-care community. Various organizations cooperate with each other to strengthen the preparedness for the pandemic and disastrous emergencies, thus to improve the public pilot study is done with the support of Kentucky SNS to determine the capacity allocation plan for each county in order to maximize the health benefit under various uncertainties, which can never be accurately estimated. We thereby employ a heuristic method named "resource reservation" to suggest the resource allocation plan for Kentucky SNS.vi

show abstract

Finding optimal vaccination strategies under parameter uncertainty using stochastic programming

Cited by 76 publications

References 31 publications

IIS branch-and-cut for joint chance-constrained stochastic programs and application to optimal vaccine allocation

IIS branch-and-cut for joint chance-constrained stochastic programs and application to optimal vaccine allocation

Vaccination with partial knowledge of external effectiveness

Optimization models for resource allocation under perturbation.

Contact Info

Product

Resources

About