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

The Pathway to a Universal Influenza Vaccine

Development of a universal influenza vaccine is a research priority for the National Institute of Allergy and Infectious Diseases (NIAID) at the National Institutes of Health. To facilitate this goal, we convened a workshop in Rockville, Maryland to identify knowledge gaps in influenza research and develop strategies to fill them

The Pathway to a Universal Influenza Vaccine

Development of a universal influenza vaccine is a research priority for the National Institute of Allergy and Infectious Diseases (NIAID) at the National Institutes of Health. To facilitate this goal, we convened a workshop in Rockville, Maryland to identify knowledge gaps in influenza research and develop strategies to fill them

Long-term follow-up of post-cardiac surgery Mycobacterium chimaera infections: A 5-center case series

OBJECTIVES: In multiple countries, endovascular/disseminated Mycobacterium chimaera infections have occurred in post-cardiac surgery patients in association with contaminated, widely-distributed cardiac bypass heater-cooler devices. To contribute to long-term characterization of this recently recognized infection, we describe the clinical course of 28 patients with 3-7 years of follow-up for survivors. METHODS: Identified at five hospitals in the United States 2010-2016, post-cardiac surgery patients in the cohort had growth of Mycobacterium avium complex (MAC)/M. chimaera from a sterile site or surgical wound, or a clinically compatible febrile illness with granulomatous inflammation on biopsy. Case follow-up was conducted in May 2019. RESULTS: Of 28 patients, infection appeared to be localized to the sternum in four patients. Among 18 with endovascular/disseminated infection who received combination anti-mycobacterial treatment and had sufficient follow-up, 39% appeared to have controlled infection (>12 months), 56% died, and one patient is alive with relapsed bacteremia. While the number of patients is small and interpretation is limited, four (67%) of six patients who had cardiac prosthesis removal/replacement appeared to have controlled infection compared to three (25%) of 12 with retained cardiac prosthesis (p >0.14; Fisher's exact test). CONCLUSIONS: Given poor response to treatment and potential for delayed relapses, post-cardiac surgery M. chimaera infection warrants aggressive treatment and long-term monitoring

Remdesivir for the Prevention of Invasive Mechanical Ventilation or Death in Coronavirus Disease 2019 (COVID-19): A Post Hoc Analysis of the Adaptive COVID-19 Treatment Trial-1 Cohort Data.

This post hoc analysis of the Adaptive Coronavirus Disease 2019 (COVID-19) Treatment Trial-1 (ACTT-1) shows a treatment effect of remdesivir (RDV) on progression to invasive mechanical ventilation (IMV) or death. Additionally, we create a risk profile that better predicts progression than baseline oxygen requirement alone. The highest risk group derives the greatest treatment effect from RDV