The Coronavirus disease 2019 (COVID-19) global pandemic has transformed almost every facet of human society throughout the world. Against an emerging, highly transmissible disease, governments worldwide have implemented non-pharmaceutical interventions (NPIs) to slow the spread of the virus. Examples of such interventions include community actions, such as school closures or restrictions on mass gatherings, individual actions including mask wearing and self-quarantine, and environmental actions such as cleaning public facilities. We present the Worldwide Non-pharmaceutical Interventions Tracker for COVID-19 (WNTRAC), a comprehensive dataset consisting of over 6,000 NPIs implemented worldwide since the start of the pandemic. WNTRAC covers NPIs implemented across 261 countries and territories, and classifies NPIs into a taxonomy of 16 NPI types. NPIs are automatically extracted daily from Wikipedia articles using natural language processing techniques and then manually validated to ensure accuracy and veracity. We hope that the dataset will prove valuable for policymakers, public health leaders, and researchers in modeling and analysis efforts to control the spread of COVID-19.

Autonomous cyber-physical agents play an increasingly large role in our lives. To ensure that they behave in ways aligned with the values of society, we must develop techniques that allow these agents to not only maximize their reward in an environment, but also to learn and follow the implicit constraints of society. We detail a novel approach that uses inverse reinforcement learning to learn a set of unspecified constraints from demonstrations and reinforcement learning to learn to maximize environmental rewards. A contextual bandit-based orchestrator then picks between the two policies: constraint-based and environment reward-based. The contextual bandit orchestrator allows the agent to mix policies in novel ways, taking the best actions from either a reward-maximizing or constrained policy. In addition, the orchestrator is transparent on which policy is being employed at each time step. We test our algorithms using Pac-Man and show that the agent is able to learn to act optimally, act within the demonstrated constraints, and mix these two functions in complex ways.

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PurposeDrug repurposing is an effective means of increasing treatment options for diseases, however identifying candidate molecules for the indication of interest from the thousands of approved drugs is challenging. We have performed a computational analysis of published literature to rank existing drugs according to predicted ability to reduce alpha synuclein (aSyn) oligomerization and analyzed real‐world data to investigate the association between exposure to highly ranked drugs and PD.MethodsUsing IBM Watson for Drug Discoveryâ (WDD) we identified several antihypertensive drugs that may reduce aSyn oligomerization. Using IBM MarketScanâ Research Databases we constructed a cohort of individuals with incident hypertension. We conducted univariate and multivariate Cox proportional hazard analyses (HR) with exposure as a time‐dependent covariate. Diuretics were used as the referent group. Age at hypertension diagnosis, sex, and several comorbidities were included in multivariate analyses.ResultsMultivariate results revealed inverse associations for time to PD diagnosis with exposure to the combination of the combination of angiotensin receptor II blockers (ARBs) and dihydropyridine calcium channel blockers (DHP‐CCB) (HR = 0.55, p < 0.01) and angiotensin converting enzyme inhibitors (ACEi) and diuretics (HR = 0.60, p‐value <0.01). Increased risk was observed with exposure to alpha‐blockers alone (HR = 1.81, p < 0.001) and the combination of alpha‐blockers and CCB (HR = 3.17, p < 0.05).ConclusionsWe present evidence that a computational approach can efficiently identify leads for disease‐modifying drugs. We have identified the combination of ARBs and DHP‐CCBs as of particular interest in PD.

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