Reconciling Risk Allocation and Prevalence Estimation in Public Health Using Batched Bandits

• URL: http://arxiv.org/abs/2110.13306v1
• Date: Mon, 25 Oct 2021 22:33:46 GMT
• Title: Reconciling Risk Allocation and Prevalence Estimation in Public Health Using Batched Bandits
• Authors: Ben Chugg, Daniel E. Ho
• Abstract summary: In many public health settings, there is a perceived tension between allocating resources to known vulnerable areas and learning about the overall prevalence of the problem. Inspired by a door-to-door Covid-19 testing program we helped design, we combine multi-armed bandit strategies and insights from sampling theory to demonstrate how to recover accurate prevalence estimates while continuing to allocate resources to at-risk areas.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In many public health settings, there is a perceived tension between allocating resources to known vulnerable areas and learning about the overall prevalence of the problem. Inspired by a door-to-door Covid-19 testing program we helped design, we combine multi-armed bandit strategies and insights from sampling theory to demonstrate how to recover accurate prevalence estimates while continuing to allocate resources to at-risk areas. We use the outbreak of an infectious disease as our running example. The public health setting has several characteristics distinguishing it from typical bandit settings, such as distribution shift (the true disease prevalence is changing with time) and batched sampling (multiple decisions must be made simultaneously). Nevertheless, we demonstrate that several bandit algorithms are capable out-performing greedy resource allocation strategies, which often perform worse than random allocation as they fail to notice outbreaks in new areas.

Related papers

• Planning and Learning in Risk-Aware Restless Multi-Arm Bandit Problem [4.178382980763478]
  In restless multi-arm bandits, a central agent is tasked with optimally distributing limited resources across several bandits (arms) In this work, we generalize the traditional restless multi-arm bandit problem with a risk-neutral objective by incorporating risk-awareness. We establish indexability conditions for the case of a risk-aware objective and provide a solution based on Whittle index.
  arXiv  Detail & Related papers  (2024-10-30T13:59:30Z)
• Identifiable latent bandits: Combining observational data and exploration for personalized healthcare [7.731569068280131]
  Bandit algorithms hold great promise for improving personalized decision-making but are notoriously sample-hungry. In most health applications, it is infeasible to fit a new bandit for each patient, and observable variables are often insufficient to determine optimal treatments. Latent bandits offer both rapid exploration and personalization beyond what context variables can reveal. We propose bandit algorithms based on nonlinear independent component analysis that can be provably identified from observational data to a degree sufficient to infer the optimal action in a new bandit instance consistently.
  arXiv  Detail & Related papers  (2024-07-23T07:26:38Z)
• Information Capacity Regret Bounds for Bandits with Mediator Feedback [55.269551124587224]
  We introduce the policy set capacity as an information-theoretic measure for the complexity of the policy set. Adopting the classical EXP4 algorithm, we provide new regret bounds depending on the policy set capacity. For a selection of policy set families, we prove nearly-matching lower bounds, scaling similarly with the capacity.
  arXiv  Detail & Related papers  (2024-02-15T19:18:47Z)
• A Risk-Averse Framework for Non-Stationary Stochastic Multi-Armed Bandits [0.0]
  In areas of high volatility like healthcare or finance, a naive reward approach often does not accurately capture the complexity of the learning problem. We propose a framework of adaptive risk-aware strategies that operate in non-stationary environments.
  arXiv  Detail & Related papers  (2023-10-24T19:29:13Z)
• Deep Reinforcement Learning for Efficient and Fair Allocation of Health Care Resources [47.57108369791273]
  Scarcity of health care resources could result in the unavoidable consequence of rationing. There is no universally accepted standard for health care resource allocation protocols. We propose a transformer-based deep Q-network to integrate the disease progression of individual patients and the interaction effects among patients.
  arXiv  Detail & Related papers  (2023-09-15T17:28:06Z)
• Evaluating COVID-19 vaccine allocation policies using Bayesian $m$-top exploration [53.122045119395594]
  We present a novel technique for evaluating vaccine allocation strategies using a multi-armed bandit framework. $m$-top exploration allows the algorithm to learn $m$ policies for which it expects the highest utility. We consider the Belgian COVID-19 epidemic using the individual-based model STRIDE, where we learn a set of vaccination policies.
  arXiv  Detail & Related papers  (2023-01-30T12:22:30Z)
• Adaptive Sequential Surveillance with Network and Temporal Dependence [1.7205106391379026]
  Strategic test allocation plays a major role in the control of both emerging and existing pandemics. Infectious disease surveillance presents unique statistical challenges. We propose an Online Super Learner for adaptive sequential surveillance.
  arXiv  Detail & Related papers  (2022-12-05T17:04:17Z)
• Tracking disease outbreaks from sparse data with Bayesian inference [55.82986443159948]
  The COVID-19 pandemic provides new motivation for estimating the empirical rate of transmission during an outbreak. Standard methods struggle to accommodate the partial observability and sparse data common at finer scales. We propose a Bayesian framework which accommodates partial observability in a principled manner.
  arXiv  Detail & Related papers  (2020-09-12T20:37:33Z)
• Robustness Guarantees for Mode Estimation with an Application to Bandits [131.21717367564963]
  We introduce a theory for multi-armed bandits where the values are the modes of the reward distributions instead of the mean. We show in simulations that our algorithms are robust to perturbation of the arms by adversarial noise sequences.
  arXiv  Detail & Related papers  (2020-03-05T21:29:27Z)
• Survival Cluster Analysis [93.50540270973927]
  There is an unmet need in survival analysis for identifying subpopulations with distinct risk profiles. An approach that addresses this need is likely to improve characterization of individual outcomes.
  arXiv  Detail & Related papers  (2020-02-29T22:41:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.