Actinyl ions (AnO2n+), the form in which actinides are commonly found in aqueous solution, are important species in the nuclear fuel cycle. These ions can form stable complexes with ionic ligands such as OH-, NO3-, Cl-, and even other actinyl ions in the aqueous phase. Knowledge of the relative stabilities of these complexes is important for the efficient design of separation processes used in recycling. These complexes also play a major role in the formation of actinide nanoclusters. A quantitative treatment of the stability of these actinyl ion complexes is therefore warranted. In the present work, molecular dynamics (MD) simulations have been performed to calculate the potential of mean force (PMF) between two actinyl ions (UO22+ and NpO2+) and various ligands (F-, Cl-, OH-, NO3-, SO42-, CO32-, Na+, and H2O) in explicitly-modeled aqueous solution. Equilibrium constants were calculated from the PMFs, and are consistent with experimental trends. Dication actinyls show stronger affinity for anions compared to monocation actinyls, whereas the opposite is true for cation-cation interactions between actinyls. Finally, the dynamics of actinyl-ligand contact ion pair (CIP) dissociation were characterized by calculating rate constants from transition state theory. The transmission coefficient, a dynamical correction factor used to correct for reaction barrier recrossing, was calculated for each actinyl-ligand CIP dissociation event.

The control of the initial outbreak and spread of SARS-CoV-2/COVID-19 via the application of population-wide non-pharmaceutical mitigation measures have led to remarkable successes in dampening the pandemic globally. However, with countries beginning to ease or lift these measures fully to restart activities, concern is growing regarding the impacts that such reopening of societies could have on the subsequent transmission of the virus. While mathematical models of COVID-19 transmission have played important roles in evaluating the impacts of these measures for curbing virus transmission, a key need is for models that are able to effectively capture the effects of the spatial and social heterogeneities that drive the epidemic dynamics observed at the local community level. Iterative forecasting that uses new incoming epidemiological and social behavioral data to sequentially update locally-applicable transmission models can overcome this gap, potentially resulting in better predictions and policy actions. Here, we present the development of one such data-driven iterative modelling tool based on publicly available data and an extended SEIR model for forecasting SARS-CoV-2 at the county level in the United States. Using data from the state of Florida, we demonstrate the utility of such a system for exploring the outcomes of the social measures proposed by policy makers for containing the course of the pandemic. We provide comprehensive results showing how the locally identified models could be employed for accessing the impacts and societal tradeoffs of using specific social protective strategies. We conclude that it could have been possible to lift the more disruptive social interventions related to movement restriction/social distancing measures earlier if these were accompanied by widespread testing and contact tracing. These intensified social interventions could have potentially also brought about the control of the epidemic in low- and some medium-incidence county settings first, supporting the development and deployment of a geographically-phased approach to reopening the economy of Florida. We have made our data-driven forecasting system publicly available for policymakers and health officials to use in their own locales, so that a more efficient coordinated strategy for controlling SARS-CoV-2 region-wide can be developed and successfully implemented.

Background: The development and authorization of COVID-19 vaccines has provided the clearest path forward to eliminate community spread and thus end the ongoing SARS-CoV-2 pandemic. However, the limited pace at which the vaccine can be administered motivates the question, to what extent must we continue to adhere to social intervention measures such as mask wearing and social distancing? Methods: We develop a mathematical model of COVID-19 spread incorporating both vaccine dynamics and socio-epidemiological parameters. We use this model to study two important measures of disease control and eradication, the effective reproductive number Rt and the peak intensive care unit (ICU) caseload, over three key parameters: social measure adherence, vaccination rate, and vaccination coverage. Results: Our results suggest that, due to the slow pace of vaccine administration, social measures must be maintained by a large proportion of the population until a sufficient proportion of the population becomes vaccinated for the pandemic to be eradicated. By contrast, with reduced adherence to social measures, hospital ICU cases will greatly exceed capacity, resulting in increased avoidable loss of life. We then investigate the threat of localized outbreaks in low-vaccinated populations that have removed all social intervention mandates, and show that such populations could remain highly susceptible to major outbreaks particularly in the face of more easily transmissible variants.  Conclusions: These findings highlight the complex interplay involved between vaccination and social protective measures, and indicate the practical importance of continuing with extant social measures while vaccines are scaled up to allow the development of the herd immunity needed to end or control SARS-CoV-2 sustainably.