Enhanced Vaccine Effectiveness during the Delta Phase of the COVID-19 Pandemic in the Medicare Population Supports a Multilayered Prevention Approach.

Throughout the pandemic, individuals 65 years and older have contributed most COVID-19 related deaths. To best formulate effective vaccination and other prevention policies to protect older adults, large scale observational studies of these higher risk individuals are needed. We conducted a Vaccine Effectiveness (VE) study during the B.1.617.2 Delta variant phase of the pandemic in July and August 2021 in a cohort of 17 million Medicare beneficiaries of which 5.7 million were fully vaccinated. We found that individuals fully vaccinated with the Pfizer-BioNTech BNT162b2 and Moderna mRNA-1273 vaccines in January 2021 had 2.5 times higher breakthrough infections and hospitalizations than those fully vaccinated in March 2021, consistent with waning of vaccine-induced immunity. Measuring VE weekly, we found that VE against hospitalization, and even more so against infection, increased from July 2021 through August 2021, suggesting that in addition to the protective role of vaccination, increased masking or social distancing might have contributed to the unexpected increase in VE. Ongoing monitoring of Medicare beneficiaries should be a priority as new variants continue to emerge, and the VE of the new bivalent vaccines remains to be established. This could be accomplished with a large Medicare claims database and the analytics platform used for this study.

A Predictive Model for Severe COVID-19 in the Medicare Population: A Tool for Prioritizing Primary and Booster COVID-19 Vaccination.

Recommendations for prioritizing COVID-19 vaccination have focused on the elderly at higher risk for severe disease. Existing models for identifying higher-risk individuals lack the needed integration of socio-demographic and clinical risk factors. Using multivariate logistic regression and random forest modeling, we developed a predictive model of severe COVID-19 using clinical data from Medicare claims for 16 million Medicare beneficiaries and socio-economic data from the CDC Social Vulnerability Index. Predicted individual probabilities of COVID-19 hospitalization were then calculated for population risk stratification and vaccine prioritization and mapping. The leading COVID-19 hospitalization risk factors were non-white ethnicity, end-stage renal disease, advanced age, prior hospitalization, leukemia, morbid obesity, chronic kidney disease, lung cancer, chronic liver disease, pulmonary fibrosis or pulmonary hypertension, and chemotherapy. However, previously reported risk factors such as chronic obstructive pulmonary disease and diabetes conferred modest hospitalization risk. Among all social vulnerability factors, residence in a low-income zip code was the only risk factor independently predicting hospitalization. This multifactor risk model and its population risk dashboard can be used to optimize COVID-19 vaccine allocation in the higher-risk Medicare population.

Formal methods for Hopfield-like networks.

Building a meaningful model of biological regulatory network is usually done by specifying the components (e.g. the genes) and their interactions, by guessing the values of parameters, by comparing the predicted behaviors to the observed ones, and by modifying in a trial-error process both architecture and parameters in order to reach an optimal fitness. We propose here a different approach to construct and analyze biological models avoiding the trial-error part, where structure and dynamics are represented as formal constraints. We apply the method to Hopfield-like networks, a formalism often used in both neural and regulatory networks modeling. The aim is to characterize automatically the set of all models consistent with all the available knowledge (about structure and behavior). The available knowledge is formalized into formal constraints. The latter are compiled into Boolean formula in conjunctive normal form and then submitted to a Boolean satisfiability solver. This approach allows to formulate a wide range of queries, expressed in a high level language, and possibly integrating formalized intuitions. In order to explore its potential, we use it to find cycles for 3-nodes networks and to determine the flower morphogenesis regulatory network of Arabidopsis thaliana. Applications of this technique are numerous and concern the building of models from data as well as the design of biological networks possessing specified behaviors.

"Immunetworks", intersecting circuits and dynamics.

This paper proposes a study of biological regulation networks based on a multi-level strategy. Given a network, the first structural level of this strategy consists in analysing the architecture of the network interactions in order to describe it. The second dynamical level consists in relating the patterns found in the architecture to the possible dynamical behaviours of the network. It is known that circuits are the patterns that play the most important part in the dynamics of a network in the sense that they are responsible for the diversity of its asymptotic behaviours. Here, we pursue further this idea and argue that beyond the influence of underlying circuits, intersections of circuits also impact significantly on the dynamics of a network and thus need to be payed special attention to. For some genetic regulation networks involved in the control of the immune system ("immunetworks"), we show that the small number of attractors can be explained by the presence, in the underlying structures of these networks, of intersecting circuits that "inter-lock".

Micro-RNAs: viral genome and robustness of gene expression in the host.

For comparing RNA rings or hairpins with reference or random ring sequences, circular versions of distances and distributions like those of Hamming and Gumbel are needed. We define these circular versions and we apply these new tools to the comparison of RNA relics (such as micro-RNAs and tRNAs) with viral genomes that have coevolved with them. Then we show how robust are the regulation networks incorporating in their boundary micro-RNAs as sources or new feedback loops involving ubiquitous proteins like p53 (which is a micro-RNA transcription factor) or oligopeptides regulating protein translation. Eventually, we propose a new coevolution game between viral and host genomes.

Robustness in regulatory interaction networks. A generic approach with applications at different levels: physiologic, metabolic and genetic.

Regulatory interaction networks are often studied on their dynamical side (existence of attractors, study of their stability). We focus here also on their robustness, that is their ability to offer the same spatiotemporal patterns and to resist to external perturbations such as losses of nodes or edges in the networks interactions architecture, changes in their environmental boundary conditions as well as changes in the update schedule (or updating mode) of the states of their elements (e.g., if these elements are genes, their synchronous coexpression mode versus their sequential expression). We define the generic notions of boundary, core, and critical vertex or edge of the underlying interaction graph of the regulatory network, whose disappearance causes dramatic changes in the number and nature of attractors (e.g., passage from a bistable behaviour to a unique periodic regime) or in the range of their basins of stability. The dynamic transition of states will be presented in the framework of threshold Boolean automata rules. A panorama of applications at different levels will be given: brain and plant morphogenesis, bulbar cardio-respiratory regulation, glycolytic/oxidative metabolic coupling, and eventually cell cycle and feather morphogenesis genetic control.

Robustness in regulatory networks: a multi-disciplinary approach.

We give in this paper indications about the dynamical impact (as phenotypic changes) coming from the main sources of perturbation in biological regulatory networks. First, we define the boundary of the interaction graph expressing the regulations between the main elements of the network (genes, proteins, metabolites, ...). Then, we search what changes in the state values on the boundary could cause some changes of states in the core of the system (robustness to boundary conditions). After, we analyse the role of the mode of updating (sequential, block sequential or parallel) on the asymptotics of the network, essentially on the occurrence of limit cycles (robustness to updating methods). Finally, we show the influence of some topological changes (e.g. suppression or addition of interactions) on the dynamical behaviour of the system (robustness to topology perturbations).

Potential automata. Application to the genetic code III.

In previous notes, we have described both mathematical properties of potential (n-switches) and potential-Hamiltonian (Liénard systems) continuous differential systems, and also biological applications, especially those concerning primitive cyclic RNAs related to the genetic code. In the present note, we give a general definition of a potential automaton, and we show that a discrete Hopfield-like system already introduced by Goles et al. is a good candidate for such a potential automaton: it has a Lyapunov functional that decreases on its trajectories and whose time derivative is just its discrete velocity. Then we apply this new notion of potential automaton to the genetic code. We show in particular that the consideration of only physicochemical properties of amino-acids, like their molecular weight, hydrophobicity and ability to create hydrogen bonds suffices to build a potential decreasing on trajectories corresponding to the synonymy classes of the genetic code. Such an 'a minima' construction reinforces the classical stereochemical hypothesis about the origin of the genetic code and authorizes new views about the optimality of its synonymy classes.