Shahram Vatani’s research while affiliated with Claude Bernard University Lyon 1 and other places

Pandemics are becoming a recurring threat to our global society. Henceforth it is of paramount importance to provide reliable and simple mathematical modelling of their spread to guide decision makers and healthcare stakeholders. We review a novel symmetry-based analysis of epidemiological data, based on ideas adapted from theoretical physics, also known as the epidemiological Renormalization Group (eRG) framework. One major result is the first consistent mathematical modelling of multi-wave patterns, which have been observed in infectious disease spread. Thanks to the plethora of data available for COVID-19, we studied the evolution of variants via an unsupervised machine learning approach of the spike protein genome. With the use of the eRG framework, we confirmed that the emergence of new virus variants is one of the major causes of the onset of a new wave. This result can shape the best strategy to control and tame ongoing and future pandemics.

We propose a new family structure for the Standard Model fermions, where the muon is assigned to the third family, taking the placeholder from the tau lepton. This reassignment, which is a mere choice of convention in the Standard Model, becomes physically meaningful in the presence of new physics assuming a direct link between quarks and leptons. In fact, when quarks and leptons are coupled by new interactions, the choice of which lepton is assigned to a particular quark generation brings physical consequences, revealing potentially meaningful patterns in the masses and mixings, while pointing to precise and testable predictions for experiments.

We propose a new family structure for the Standard Model fermions, where the muon is reassigned to the third family. This reveals potentially meaningful patterns in the masses and mixing, while pointing to precise and testable predictions for experiments.

Never before such a vast amount of data, including genome sequencing, has been collected for any viral pandemic than for the current case of COVID-19. This offers the possibility to trace the virus evolution and to assess the role mutations play in its spread within the population, in real time. To this end, we focused on the Spike protein for its central role in mediating viral outbreak and replication in host cells. Employing the Levenshtein distance on the Spike protein sequences, we designed a machine learning algorithm yielding a temporal clustering of the available dataset. From this, we were able to identify and define emerging persistent variants that are in agreement with known evidences. Our novel algorithm allowed us to define persistent variants as chains that remain stable over time and to highlight emerging variants of epidemiological interest as branching events that occur over time. Hence, we determined the relationship and temporal connection between variants of interest and the ensuing passage to dominance of the current variants of concern. Remarkably, the analysis and the relevant tools introduced in our work serve as an early warning for the emergence of new persistent variants once the associated cluster reaches 1% of the time-binned sequence data. We validated our approach and its effectiveness on the onset of the Alpha variant of concern. We further predict that the recently identified lineage AY.4.2 (‘Delta plus’) is causing a new emerging variant. Comparing our findings with the epidemiological data we demonstrated that each new wave is dominated by a new emerging variant, thus confirming the hypothesis of the existence of a strong correlation between the birth of variants and the pandemic multi-wave temporal pattern. The above allows us to introduce the epidemiology of variants that we described via the Mutation epidemiological Renormalisation Group framework.

We propose a physics-inspired mathematical model underlying the temporal evolution of competing virus variants that relies on the existence of (quasi) fixed points capturing the large time scale invariance of the dynamics. To motivate our result we first modify the time-honoured compartmental models of the SIR type to account for the existence of competing variants and then show how their evolution can be naturally re-phrased in terms of flow equations ending at quasi fixed points. As the natural next step we employ (near) scale invariance to organise the time evolution of the competing variants within the effective description of the epidemic Renormalization Group framework. We test the resulting theory against the time evolution of COVID-19 virus variants that validate the theory empirically.

Never before such a vast amount of data, including genome sequencing, has been collected for any viral pandemic than for the current case of COVID-19. This offers the possibility to trace the virus evolution and to assess the role mutations play in its spread within the population, in real time. To this end, we focused on the Spike protein for its central role in mediating viral outbreak and replication in host cells. Employing the Levenshtein distance on the Spike protein sequences, we designed a machine learning algorithm yielding a temporal clustering of the available dataset. From this, we were able to identify and define emerging persistent variants that are in agreement with known evidences. Our novel algorithm allowed us to define persistent variants as chains that remain stable over time and to highlight emerging variants of epidemiological interest as branching events that occur over time. Hence, we determined the relationship and temporal connection between variants of interest and the ensuing passage to dominance of the current variants of concern. Remarkably, the analysis and the relevant tools introduced in our work serve as an early warning for the emergence of new persistent variants once the associated cluster reaches 1% of the time-binned sequence data. We validated our approach and its effectiveness on the onset of the Alpha variant of concern. We further predict that the recently identified lineage AY.4.2 (‘Delta plus’) is causing a new emerging variant. Comparing our findings with the epidemiological data we demonstrated that each new wave is dominated by a new emerging variant, thus confirming the hypothesis of the existence of a strong correlation between the birth of variants and the pandemic multi-wave temporal pattern. The above allows us to introduce the epidemiology of variants that we described via the Mutation epidemiological Renormalisation Group (MeRG) framework.

The Standard Model (SM) is the most successful theory describing the particle interactions. Its predictions, incredibly precise, agree very well with the experimental data. The last proof being the discovery of the Higgs boson in 2012, which is at the origin of the mass of the other particles, thus essential to the theory. However, the SM cannot explain everything: - Experiments have confirmed a mass for the neutrinos, which the SM does not provide. -The gravitational interactions are not a part of the SM. -The dark matter, hypothetical particle to explain the astrophysics observations, is not taken into account in the SM - The Higgs Mechanism that generates a mass to the boson is ad-hoc via the introduction of the Higgs potential. The Higgs mass also suffers from the quadratic divergences of new physics. Those reasons push us to believe that the SM is just the low energy manifestation of a more fundamental theoy. The main subject of the thesis are the Composite Higgs Models, which principaly try to solve the last issue of the above list. In this class of models, the higgs Boson, and all its ineractions, are replace by a new fermionic sector very similar to Quantum ChromoDynamic (QCD). For that purpose, new fermions called technifermions are introduced and charged under a new color dubbed technicolor. Like the color in QCD, the technicolor theory confines at low energy and a condensate breaks its global symmetries. This generates Goldstone bosons which can play the role of the SM Higgs boson. By its bound state nature, the new Higgs boson has no more issues with the quantum correction of its mass. In this scenario the new strong interaction gives a dynamical origin to the Higgs potential. Nontheless, challenges appear if we want to generate a mass for the SM fermions. The paradigm of Partial Compositness try to overcome this issue and postulate a linear mixing between the SM fermions and technibaryons, fermionic bound state of three technifermions, like the quarks forming the proton.

Never before such a vast amount of data, including genome sequencing, has been collected for any viral pandemic than for the current case of COVID-19. This offers the possibility to trace the virus evolution and to assess the role mutations play in its spread within the population, in real time. To this end, we focused on the Spike protein for its central role in mediating viral outbreak and replication in host cells. Employing the Levenshtein distance on the Spike protein sequences, we designed a machine learning algorithm yielding a temporal clustering of the available dataset. From this, we were able to identify and define emerging persistent variants that are in agreement with known evidences. Our novel algorithm allowed us to define persistent variants as chains that remain stable over time and to highlight emerging variants of epidemiological interest as branching events that occur over time. Hence, we determined the relationship and temporal connection between variants of interest and the ensuing passage to dominance of the current variants of concern. Remarkably, the analysis and the relevant tools introduced in our work serve as an early warning for the emergence of new persistent variants once the associated cluster reaches 1% of the time-binned sequence data. We validated our approach and its effectiveness on the onset of the Alpha variant of concern. We further predict that the recently identified lineage AY.4.2 (‘Delta plus’) is causing a new emerging variant. Comparing our findings with the epidemiological data we demonstrated that each new wave is dominated by a new emerging variant, thus confirming the hypothesis of the existence of a strong correlation between the birth of variants and the pandemic multi-wave temporal pattern. The above allows us to introduce the epidemiology of variants that we described via the Mutation epidemiological Renormalisation Group (MeRG) framework. Highlights Objectives To study the relation among Spike protein mutations, the emergence of relevant variants and the multi-wave pattern of the COVID-19 pandemic. Setting Genomic sequencing of the SARS-CoV-2 Spike proteins in the UK nations (England, Scotland, Wales). Epi-demiological data for the number of infections in the UK nations, South Africa, California and India. Methodology We design a machine learning algorithm, based on the Levenshtein distance on the Spike protein sequences, that leads to a temporal clustering of the available dataset, from which we define emerging persistent variants. The above allows us to introduce the epidemiology of variants that we described via the Mutation epidemiological Renormalisation Group (MeRG) framework. Results We show that: Our approach, based only on the Spike protein sequence, allows to efficiently identify the variants of concern (VoCs) and of interest (VoIs), as well as other emerging variants occurring during the diffusion of the virus. Within our time-ordered chain analysis, a branching relation emerges, thus permitting to reconstruct the evolutionary diversification of Spike variants and the establishment of the epidemiologically relevant ones. Our analysis provides an early warning for the emergence of new persistent variants once its associated dominant Spike sequence reaches 1% of the time-binned sequence data. Validation on the onset of the Alpha VoC shows that our early warning is triggered 6 weeks before the WHO classification decision. Comparison with the epidemiological data demonstrates that each new wave is dominated by a new emerging variant, thus confirming the hypothesis that there is a strong correlation between the emergence of variants and the multi-wave temporal pattern depicting the viral spread. A theory of variant epidemiology is established, which describes the temporal evolution of the number of infected by different emerging variants via the MeRG approach. This is corroborated by empirical data. Conclusions Applying a ML approach to the temporal variability of the Spike protein sequence enables us to identify, classify and track emerging virus variants. Our analysis is unbiased, in the sense that it does not require any prior knowledge of the variant characteristics, and our results are validated by other informed methods that define variants based on the complete genome. Furthermore, correlating persistent variants of our approach to epidemiological data, we discover that each new wave of the COVID-19 pandemic is driven and dominated by a new emerging variant. Our results are therefore indispensable for further studies on the evolution of SARS-CoV-2 and the prediction of evolutionary patterns that determine current and future mutations of the Spike proteins, as well as their diversification and persistence during the viral spread. Moreover, our ML algorithm works as an efficient early warning system for the emergence of new persistent variants that may pose a threat of triggering a new wave of COVID-19. Capable of a timely identification of potential new epidemiological threats when the variant only represents 1% of the new sequences, our ML strategy is a crucial tool for decision makers to define short and long term strategies to curb future outbreaks. The same methodology can be applied to other viral diseases, influenza included, if sufficient sequencing data is available.

We propose a physical theory underlying the temporal evolution of competing virus variants that relies on the existence of (quasi) fixed points capturing the large time scale invariance of the dynamics. To motivate our result we first modify the time-honoured compartmental models of the SIR type to account for the existence of competing variants and then show how their evolution can be naturally re-phrased in terms of flow equations ending at quasi fixed points. As the natural next step we employ (near) scale invariance to organise the time evolution of the competing variants within the effective description of the epidemic Renormalization Group framework. We test the resulting theory against the time evolution of COVID-19 virus variants that validate the theory empirically.

Abstract We present an extension of the large- $N_\mathrm{f}$ N f formalism that allows one to study cases with multiple fermion representations. The pole structure in the beta function is traced back to the intrinsic non-abelian nature of the gauge group, independently from the fermion representation. This result validates the conjectured existence of an interactive UV fixed point for non-abelian gauge theories with large fermion multiplicity. Finally, we apply our results to chiral gauge theories.