Reducing dengue fever cases at the lowest budget: a constrained optimization approach applied to Thailand

Background With the challenges that dengue fever (DF) presents to healthcare systems and societies, public health officials must determine where best to allocate scarce resources and restricted budgets. Constrained optimization (CO) helps to address some of the acknowledged limitations of conventional health economic analyses and has typically been used to identify the optimal allocation of resources across interventions subject to a variety of constraints. Methods A dynamic transmission model was developed to predict the number of dengue cases in Thailand at steady state. A CO was then applied to identify the optimal combination of interventions (release of Wolbachia-infected mosquitoes and paediatric vaccination) within the constraints of a fixed budget, set no higher than cost estimates of the current vector control programme, to minimize the number of dengue cases and disability-adjusted life years (DALYs) lost. Epidemiological, cost, and effectiveness data were informed by national data and the research literature. The time horizon was 10 years. Scenario analyses examined different disease management and intervention costs, budget constraints, vaccine efficacy, and optimization time horizon. Results Under base-case budget constraints, the optimal coverage of the two interventions to minimize dengue incidence was predicted to be nearly equal (Wolbachia 50%; paediatric vaccination 49%) with corresponding coverages under lower bound (Wolbachia 54%; paediatric vaccination 10%) and upper bound (Wolbachia 67%; paediatric vaccination 100%) budget ceilings. Scenario analyses indicated that the most impactful situations related to the costs of Wolbachia and paediatric vaccination with decreases/ increases in costs of interventions demonstrating a direct correlation with coverage (increases/ decreases) of the respective control strategies under examination. Conclusions Determining the best investment strategy for dengue control requires the identification of the optimal mix of interventions to implement in order to maximize public health outcomes, often under fixed budget constraints. A CO model was developed with the objective of minimizing dengue cases (and DALYs lost) over a 10-year time horizon, within the constraints of the estimated budgets for vector control in the absence of vaccination and Wolbachia. The model provides a tool for developing estimates of optimal coverage of combined dengue control strategies that minimize dengue burden at the lowest budget.

Review on ranking and selection: A new perspective

In this paper, we briefly review the development of ranking and selection (R&S) in the past 70 years, especially the theoretical achievements and practical applications in the past 20 years. Different from the frequentist and Bayesian classifications adopted by Kim and Nelson (2006b) and Chick (2006) in their review articles, we categorize existing R&S procedures into fixed-precision and fixed-budget procedures, as in Hunter and Nelson (2017). We show that these two categories of procedures essentially differ in the underlying methodological formulations, i.e., they are built on hypothesis testing and dynamic programming, respectively. In light of this variation, we review in detail some well-known procedures in the literature and show how they fit into these two formulations. In addition, we discuss the use of R&S procedures in solving various practical problems and propose what we think are the important research questions in the field.

Pre-Training Acquisition Functions by Deep Reinforcement Learning for Fixed Budget Active Learning

There are many situations in supervised learning where the acquisition of data is very expensive and sometimes determined by a user’s budget. One way to address this limitation is active learning. In this study, we focus on a fixed budget regime and propose a novel active learning algorithm for the pool-based active learning problem. The proposed method performs active learning with a pre-trained acquisition function so that the maximum performance can be achieved when the number of data that can be acquired is fixed. To implement this active learning algorithm, the proposed method uses reinforcement learning based on deep neural networks as as a pre-trained acquisition function tailored for the fixed budget situation. By using the pre-trained deep Q-learning-based acquisition function, we can realize the active learner which selects a sample for annotation from the pool of unlabeled samples taking the fixed-budget situation into account. The proposed method is experimentally shown to be comparable with or superior to existing active learning methods, suggesting the effectiveness of the proposed approach for the fixed-budget active learning.

More for less: predicting and maximizing genomic variant discovery via Bayesian nonparametrics

While the cost of sequencing genomes has decreased dramatically in recent years, this expense often remains non-trivial. Under a fixed budget, scientists face a natural trade-off between quantity and quality: spending resources to sequence a greater number of genomes or spending resources to sequence genomes with increased accuracy. Our goal is to find the optimal allocation of resources between quantity and quality. Optimizing resource allocation promises to reveal as many new variations in the genome as possible. In this paper, we introduce a Bayesian nonparametric methodology to predict the number of new variants in a follow-up study based on a pilot study. When experimental conditions are kept constant between the pilot and follow-up, we find that our prediction is competitive with the best existing methods. Unlike current methods, though, our new method allows practitioners to change experimental conditions between the pilot and the follow-up. We demonstrate how this distinction allows our method to be used for more realistic predictions and for optimal allocation of a fixed budget between quality and quantity.

Fatter or Fitter? On rewarding and training in a contest

Competition between heterogeneous participants often leads to low effort provision in contests. We consider a principal who can divide her fixed budget between skill-enhancing training and the contest prize. Training can reduce heterogeneity, which increases effort. But it also reduces the contest prize, which makes effort fall. We set up an incomplete-information contest with heterogeneous players and show how this trade-off is related to the size of the budget when the principal maximizes expected effort. A selection problem can also arise in this framework in which there is a cost associated with a contest win by the inferior player. This gives the principal a larger incentive to train the expected laggard, reducing the size of the prize on offer.

The Lab Must Go On

Objectives The clinical laboratory community has faced unprecedented challenges in responding to the coronavirus disease 2019 (COVID-19) pandemic. Long-held assumptions about laboratory management have been reconsidered in light of these new circumstances. Methods Experience during the first 6 months of the COVID-19 pandemic at a clinical reference laboratory was reviewed in the context of several commonly held management principles to assess their relevance to clinical laboratory operations during a crisis. Results Management and operational ideas regarding different modes of communication, physical proximity and interaction, operating under a fixed budget, and maintaining a breadth of laboratory service offerings have been challenged during the COVID-19 pandemic. The importance of putting people first, maintaining collaboration, and providing effective leadership and communication throughout an organization have been highlighted. Conclusions The collaborative activities of highly interdependent teams and individuals have helped the clinical laboratory community respond to society’s needs in the COVID-19 crisis. Not all laboratory management principles apply equally well in the course of an international respiratory pandemic. When navigating crises, leaders need to distinguish situational management principles from those that are universal.