# Bayesian Best-Arm Identification for Selecting Influenza Mitigation Strategies

arXiv: https://arxiv.org/abs/1711.06299 · HTML: https://mlsanity.com/paper/1711.06299/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Nov 2017.

## Built on by (most cited first, 8 of 8)

- [Exploring the Pareto front of multi-objective COVID-19 mitigation policies using reinforcement learning](https://mlsanity.com/paper/2204.05027/) (2022)
- [Challenges in Statistical Analysis of Data Collected by a Bandit Algorithm: An Empirical Exploration in Applications to Adaptively Randomized Experiments](https://mlsanity.com/paper/2103.12198/) (2021)
- [On the Complexity of Differentially Private Best-Arm Identification with Fixed Confidence](https://mlsanity.com/paper/2309.02202/) (2023)
- [Scalable Optimization for Wind Farm Control using Coordination Graphs](https://mlsanity.com/paper/2101.07844/) (2021)
- [Evaluating COVID-19 vaccine allocation policies using Bayesian m-top exploration](https://mlsanity.com/paper/2301.12822/) (2023)
- [Batched Bandits with Crowd Externalities](https://mlsanity.com/paper/2109.14733/) (2021)
- [Learning to Explore with Lagrangians for Bandits under Unknown Linear Constraints](https://mlsanity.com/paper/2410.18844/) (2024)
- [Optimal Regret of Bernoulli Bandits under Global Differential Privacy](https://mlsanity.com/paper/2505.05613/) (2025)

## Abstract (arXiv)

Pandemic influenza has the epidemic potential to kill millions of people. While various preventive measures exist (i.a., vaccination and school closures), deciding on strategies that lead to their most effective and efficient use remains challenging. To this end, individual-based epidemiological models are essential to assist decision makers in determining the best strategy to curb epidemic spread. However, individual-based models are computationally intensive and it is therefore pivotal to identify the optimal strategy using a minimal amount of model evaluations. Additionally, as epidemiological modeling experiments need to be planned, a computational budget needs to be specified a priori. Consequently, we present a new sampling technique to optimize the evaluation of preventive strategies using fixed budget best-arm identification algorithms. We use epidemiological modeling theory to derive knowledge about the reward distribution which we exploit using Bayesian best-arm identification algorithms (i.e., Top-two Thompson sampling and BayesGap). We evaluate these algorithms in a realistic experimental setting and demonstrate that it is possible to identify the optimal strategy using only a limited number of model evaluations, i.e., 2-to-3 times faster compared to the uniform sampling method, the predominant technique used for epidemiological decision making in the literature. Finally, we contribute and evaluate a statistic for Top-two Thompson sampling to inform the decision makers about the confidence of an arm recommendation.