Since 2023, Moderna and Korea University have been developing a new mRNA vaccine for hantavirus. The work has been promising so far, but a finished product isn't likely coming any time soon. US-based pharmaceutical company Moderna confirmed that it has been working on the development of hantavirus vaccines in collaboration with the Vaccine Innovation Center of Korea University College of Medicine (VIC-K). This comes after an outbreak of hantavirus occurred on a Dutch cruise ship that sailed from Argentina and disembarked its passengers and crew in the Canary Islands on May 10. At least three people aboard the MV died, and several cases were reported as serious.

Days before the cruise ship affected by a hantavirus outbreak is set to dock in southern Tenerife in the Canary Islands, locals are expressing a mix of concern and calm. According to Oceanwide Expeditions, there are currently no symptomatic individuals on board the ship. Iran's president says he has spoken to the country's Supreme Leader

Here's What You Need to Know About the Hantavirus While the outbreak aboard a cruise ship in the Atlantic is concerning, the virus isn't easily transmitted through casual contact. Cruises are so closely associated with illness that the highly contagious norovirus is commonly called the "cruise ship virus." But a ship headed for Spain's Canary Islands has attracted global attention due to a rare outbreak of hantavirus that's left three dead. While alarming, health officials and infectious disease experts say the risk to the general public right now is low because hantavirus is less contagious than other respiratory diseases like the coronavirus responsible for the Covid-19 pandemic . "This is not Covid, this is not influenza. It spreads very, very differently," Maria Van Kerkhove, director of epidemic and pandemic preparedness and prevention at the World Health Organization, said at a press conference on Thursday.

We investigate stochastic combinatorial semi-bandits, where the entire joint distribution of outcomes impacts the complexity of the problem instance (unlike in the standard bandits). Typical distributions considered depend on specific parameter values, whose prior knowledge is required in theory but quite difficult to estimate in practice; an example is the commonly assumed sub-Gaussian family. We alleviate this issue by instead considering a new general family of sub-exponential distributions, which contains bounded and Gaussian ones. We prove a new lower bound on the expected regret on this family, that is parameterized by the unknown covariance matrix of outcomes, a tighter quantity than the sub-Gaussian matrix. We then construct an algorithm that uses covariance estimates, and provide a tight asymptotic analysis of the regret. Finally, we apply and extend our results to the family of sparse outcomes, which has applications in many recommender systems.

The No-U-Turn Sampler (NUTS) is the computational workhorse of modern Bayesian software libraries, yet its qualitative and quantitative convergence guarantees were established only recently. A significant gap remains in the theoretical comparison of its two main variants: NUTS-mul and NUTS-BPS, which use multinomial sampling and biased progressive sampling, respectively, for index selection. In this paper, we address this gap in three contributions. First, we derive the first necessary conditions for geometric ergodicity for both variants. Second, we establish the first sufficient conditions for geometric ergodicity and ergodicity for NUTS-mul. Third, we obtain the first mixing time result for NUTS-BPS on a standard Gaussian distribution. Our results show that NUTS-mul and NUTS-BPS exhibit nearly identical qualitative behavior, with geometric ergodicity depending on the tail properties of the target distribution. However, they differ quantitatively in their convergence rates. More precisely, when initialized in the typical set of the canonical Gaussian measure, the mixing times of both NUTS-mul and NUTS-BPS scale as $O(d^{1/4})$ up to logarithmic factors, where $d$ denotes the dimension. Nevertheless, the associated constants are strictly smaller for NUTS-BPS.

Conditional effects are commonly used measures for understanding how treatment effects vary across different groups, and are often used to target treatments/interventions to groups who benefit most. In this work we review existing methods and propose novel ones, focusing on the odds ratio (OR) and the risk ratio (RR). While estimation of the conditional average treatment effect (ATE) has been widely studied, estimators for the OR and RR lag behind, and cutting edge estimators such as those based on doubly robust transformations or orthogonal risk functions have not been generalized to these parameters. We propose such a generalization here, focusing on the DR-learner and the R-learner. We derive orthogonal risk functions for the OR and RR and show that the associated pseudo-outcomes satisfy second-order conditional-mean remainder properties analogous to the ATE case. We also evaluate estimators for the conditional ATE, OR, and RR in a comprehensive nonparametric Monte Carlo simulation study to compare them with common alternatives under hundreds of different data-generating distributions. Our numerical studies provide empirical guidance for choosing an estimator. For instance, they show that while parametric models are useful in very simple settings, the proposed nonparametric estimators significantly reduce bias and mean squared error in the more complex settings expected in the real world. We illustrate the methods in the analysis of physical activity and sleep trouble in U.S. adults using data from the National Health and Nutrition Examination Survey (NHANES). The results demonstrate that our estimators uncover substantial treatment effect heterogeneity that is obscured by traditional regression approaches and lead to improved treatment decision rules, highlighting the importance of data-adaptive methods for advancing precision health research.

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