Bayesian Best-Arm Identification for Selecting Influenza Mitigation Strategies

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

On the Complexity of Differentially Private Best-Arm Identification with Fixed Confidence

Best Arm Identification (BAI) problems are progressively used for data-sensitive applications, such as designing adaptive clinical trials, tuning hyper-parameters, and conducting user studies to name a few. Motivated by the data privacy concerns invoked by these applications, we study the problem of BAI with fixed confidence under $\epsilon$-global Differential Privacy (DP). First, to quantify the cost of privacy, we derive a lower bound on the sample complexity of any $\delta$-correct BAI algorithm satisfying $\epsilon$-global DP. Our lower bound suggests the existence of two privacy regimes depending on the privacy budget $\epsilon$. In the high-privacy regime (small $\epsilon$), the hardness depends on a coupled effect of privacy and a novel information-theoretic quantity, called the Total Variation Characteristic Time. In the low-privacy regime (large $\epsilon$), the sample complexity lower bound reduces to the classical non-private lower bound. Second, we propose AdaP-TT, an $\epsilon$-global DP variant of the Top Two algorithm. AdaP-TT runs in arm-dependent adaptive episodes and adds Laplace noise to ensure a good privacy-utility trade-off. We derive an asymptotic upper bound on the sample complexity of AdaP-TT that matches with the lower bound up to multiplicative constants in the high-privacy regime. Finally, we provide an experimental analysis of AdaP-TT that validates our theoretical results

Experimenting with sequential allocation procedures

In experiments that consider the use of subjects, a crucial part is deciding which treatment to allocate to which subject – in other words, constructing the treatment allocation procedure. In a classical experiment, this treatment allocation procedure often simply constitutes randomly assigning subjects to a number of different treatments. Subsequently, when all outcomes have been observed, the resulting data is used to conduct an analysis that is specified a priori. Practically, however, the subjects often arrive at an experiment one-by-one. This allows the data generating process to be viewed differently: instead of considering the subjects in a batch, intermediate data from previous interactions with other subjects can be used to influence the decisions of the treatment allocation in future interactions. A heavily researched formalization that helps developing strategies for sequentially allocating subjects is the multi-armed bandit problem. In this thesis, several methods are developed to expedite the use of sequential allocation procedures by (social) scientists in field experiments. This is done by building upon the extensive literature of the multi-armed bandit problem. The thesis also introduces and shows many (empirical) examples of the usefulness and applicability of sequential allocation procedures in practice

Coronaviruses Research in BRICS Countries

SARS-CoV-2 has infected more than 105 million people worldwide. During this pandemic, researchers and clinicians have been working to understand the molecular mechanisms that underpin viral pathogenesis by studying viral–host interactions. Now, with the global rollout of various COVID-19 vaccines—based on the neutralization of the spike protein using different technologies—viral immunology and cell-based immunity are being investigated. Researchers are also studying how various SARS-CoV-2 genetic mutations will impact the efficacy of these COVID-19 vaccines. At the same time, various antiviral drugs have been identified or repurposed that have potential as anti-SARS-CoV-2 treatments. BRICS (Brazil, Russia, India, China, and South Africa) is the acronym used to associate five major emerging national economies. The BRICS countries are known for their significant influence on regional affairs, including being leaders in scientific and clinical research and innovation. This Special Issue includes researchers from BRICS countries, in particular South Africa, involved in the study of SARS-CoV-2 and COVID-19. Original articles, as well as new perspectives or reviews on the matter, were welcomed. Research in the fields of vaccine studies, pathogenesis, genetic mutations, viral immunology, and antiviral drugs were especially encouraged

Correlation of influenza infection with glycan array

Poster Presentation: SPB1 / SPB2 - Virus Host Interaction/Pathogensis/Transmission: abstract no. B109PINTRODUCTION: The past 6 years has seen the introduction of glycan arrays containing large numbers of sialic acid (Sia) containing compounds and these arrays have been used to demonstrate the relative binding affinity of influenza viruses to different glycans. Though infor...postprin