arXiv subjects

C. Brandon Ogbunugafor

Publications and source records attributed to C. Brandon Ogbunugafor.

8 recordsLinked to original sources

arXiv2026

Three Centuries of the Laws of Cricket Reveal Core Principles of the Evolution of Regulatory Mechanisms

Rules, regulations, and regulatory systems are central to societies, institutions, and organisms, yet surprisingly little is known about their evolution over long timescales. The Laws of Cricket, the world's second most popular sport, offer a unique insight into this fundamental question. Their 268-year history constitutes the longest continuous rule-set record yet assembled. Our quantitative analysis reveals generic features including rule-book size growing exponentially in time but scaling sublinearly with matches played; new situations stimulate new rules, but at a decelerating rate; regulatory structures exhibit abrupt phase transitions, increasing rule specificity, interconnectivity and complexity with central rules shifting from gameplay to officiating. These provide a framework for understanding how governance evolves from simple collections of rules to complex regulatory architectures across social, legal, and biological domains.

physics.soc-ph↗

arXiv2026

Learning virulence-transmission relationships using causal inference

The relationship between traits that influence pathogen virulence and transmission is part of the central canon of the evolution and ecology of infectious disease. However, identifying directional and mechanistic relationships among traits remains a key challenge in various subfields of biology, as models often assume static, fixed links between characteristics. Here, we introduce learning evolutionary trait relationships (LETR), a data-driven framework that applies Granger-causality principles to determine which traits drive others and how these relationships change over time. LETR integrates causal discovery with generative mapping and transfer-operator analysis to link short-term predictability with long-term trait distributions. Using a synthetic myxomatosis virus-host data set, we show that LETR reliably recovers known directional influences, such as virulence driving transmission. Applying the framework to global pandemic (SARS-CoV-2) data, we find that past virulence improves future transmission prediction, while the reverse effect is weak. Invariant-density estimates reveal a long-term trend toward low virulence and transmission, with bimodality in virulence suggesting ecological influences or host heterogeneity. In summary, this study provides a blueprint for learning the relationship between how harmful a pathogen is and how well it spreads, which is highly idiosyncratic and context-dependent. This finding undermines simplistic models and encourages the development of new theory for the constraints underlying pathogen evolution. Further, by uniting causal inference with dynamical modeling, the LETR framework offers a general approach for uncovering mechanistic trait linkages in complex biological systems of various kinds.

q-bio.PE↗

arXiv2025

Structural causal influence (SCI) captures the forces of social inequality in models of disease dynamics

Mathematical modeling has played a central role in understanding how infectious disease transmission manifests in populations. These models have demonstrated the importance of key community-level factors in structuring epidemic risk, and are now routinely used in public health for decision support. One barrier to their broader utility is that the existing canon does not often accommodate social inequalities as distinct formal drivers of variability in transmission dynamics. Given decades of evidence supporting the organizational effects of inequalities in structuring society more generally, and infectious disease risk more specifically, addressing this modeling gap is of critical importance. In this study, we build on previous efforts to integrate social forces into computational epidemiology by introducing a metric, the structural causal influence (SCI). The SCI uses causal analysis to provide a measure of the relative vulnerability of sub-communities within a susceptible population, shaped by differences in characteristics such as access to therapy, exposure to disease, and other determinants driven by social forces. We develop our metric in a simple case and apply it to a context of public health importance: Hepatitis C virus in a population of persons who inject drugs. In addition, we demonstrate the flexibility of the SCI using an agent-based model of an infectious disease. Our use of the SCI reveals that, under specific parameters in a multi-community model, the "less vulnerable" community may achieve a basic reproduction number below one, ensuring disease extinction. However, even minimal transmission between communities can increase this number, leading to sustained epidemics within both communities.

q-bio.QM↗

arXiv2025

Optimal virulence strategies in epidemiological models with asymptomatic transmission

Asymptomatic infection has gained notoriety as an important feature of infectious disease dynamics. Despite increasing attention, there have been few rigorous examinations of how asymptomatic transmission influences pathogen evolution. In this study, we apply evolutionary invasion analysis to compute optimal strategies for viruses evolving in a system with a distinct asymptomatic transmission stage. We ask how pathogens would evolve under three conditions: with an increase in the mean infectious period in the symptomatic state, with an increase in the mean infectious period in the asymptomatic stage, and an increase in proportion proceeding through the ``mild recovery route" (where the symptomatic state was bypassed entirely). We find that an increased proportion of cases moving through a ``mild recovery route" -- which can occur with different host susceptibility or increased public health intervention -- leads to a model structure in which mutant pathogens are transmitted largely through the asymptomatic route, with slightly increased evolved virulence levels. In addition, we find that an increase in the mean infectious period of the symptomatic state has a small overall influence on the fitness of the pathogen, when effective transmission can occur via the asymptomatic route. Further, we find that virulence levels change very slightly for both the asymptomatic and symptomatic populations. In sum, our results highlight the evolutionary implications of variation in host susceptibility and public health interventions in the context of asymptomatic transmission. More generally, the findings speak to the need for more nuanced interrogations of subtle routes of transmission, as they can have profound implications in disease evolution, ecology, and epidemiology.

q-bio.PE↗

arXiv2023

The context-specificity of virulence evolution revealed through evolutionary invasion analysis

Models are often employed to integrate knowledge about epidemics across scales and simulate disease dynamics. While these approaches have played a central role in studying the mechanics underlying epidemics, we lack ways to reliably predict how the relationship between virulence (the harm to hosts caused by an infection) and transmission will evolve in certain virus-host contexts. In this study, we invoke evolutionary invasion analysis -- a method used to identify the evolution of uninvadable strategies in dynamical systems -- to examine how the virulence-transmission dichotomy can evolve in models of virus infections defined by different natural histories. We reveal that peculiar ecologies drive different evolved relationships between virulence and transmission. Specifically, we discover patterns of virulence evolution between epidemics of various kinds (SARS-CoV-2 and hepatitis C virus) and that varying definitions of virulence alter our predictions for how viruses will evolve. We discuss the findings in light of contemporary conversations in the public health sector around the possibility of predicting virus evolution and in more extensive theoretical discussions involving virulence evolution in emerging infectious diseases.

q-bio.PE↗

arXiv2023

Evaluating evolution as a learning algorithm

We interpret the Moran model of natural selection and drift as an algorithm for learning features of a simplified fitness landscape, specifically genotype superiority. This algorithm's efficiency in extracting these characteristics is evaluated by comparing it to a novel Bayesian learning algorithm developed using information-theoretic tools. This algorithm makes use of a communication channel analogy between an environment and an evolving population. We use the associated channel-rate to determine an informative population-sampling procedure. We find that the algorithm can identify genotype superiority faster than the Moran model but at the cost of larger fluctuations in uncertainty.

q-bio.PE↗

arXiv2021

Immunity-induced criticality of the genotype network of influenza A (H3N2) hemagglutinin

Seasonal influenza kills hundreds of thousands every year, with multiple constantly-changing strains in circulation at any given time. A high mutation rate enables the influenza virus to evade recognition by the human immune system, including immunity acquired through past infection and vaccination. Here, we capture the genetic similarity of influenza strains and their evolutionary dynamics with genotype networks. We show that the genotype networks of influenza A (H3N2) hemagglutinin are characterized by heavy-tailed distributions of module sizes and connectivity, suggesting critical-like behavior. We argue that: (i) genotype networks are driven by mutation and host immunity to explore a subspace of networks predictable in structure, and (ii) genotype networks provide an underlying structure necessary to capture the rich dynamics of multistrain epidemic models. In particular, inclusion of strain-transcending immunity in epidemic models is dependent upon the structure of an underlying genotype network. This interplay suggests a self-organized criticality where the epidemic dynamics of influenza locates critical-like regions of its genotype network. We conclude that this interplay between disease dynamics and network structure might be key for future network analysis of pathogen evolution and realistic multistrain epidemic models.

q-bio.PE↗

arXiv2016

OFFl models: novel schema for dynamical modeling of biological systems

Flow diagrams are a common tool used to help build and interpret models of dynamical systems, often in biological contexts such as consumer-resource models and similar compartmental models. Typically, their usage is intuitive and informal. Here, we present a formalized version of flow diagrams as a kind of weighted directed graph which follow a strict grammar, which translate into a system of ordinary differential equations (ODEs) by a single unambiguous rule, and which have an equivalent representation as a relational database. (We abbreviate this schema of "ODEs and formalized flow diagrams" as OFFl.) Drawing a diagram within this strict grammar encourages a mental discipline on the part of the modeler in which all dynamical processes of a system are thought of as interactions between dynamical species that draw parcels from one or more source species and deposit them into target species according to a set of transformation rules. From these rules, the net rate of change for each species can be derived. The modeling schema can therefore be understood as both an epistemic and practical heuristic for modeling, serving both as an organizational framework for the model building process and as a mechanism for deriving ODEs. All steps of the schema beyond the initial scientific (intuitive, creative) abstraction of natural observations into model variables are algorithmic and easily carried out by a computer, thus enabling the future development of a dedicated software implementation. Such tools would empower the modeler to consider significantly more complex models than practical limitations might have otherwise proscribed, since the modeling framework itself manages that complexity on the modeler's behalf. In this report, we describe the chief motivations for OFFl, outline its implementation, and utilize a range of classic examples from ecology and epidemiology to showcase its features.

q-bio.QM↗