Browsing by Author "Fatoyinbo HO"

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    Modelling Lassa virus dynamics in West African Mastomys natalensis and the impact of human activities.
    (The Royal Society, 2024-07-24) John RS; Fatoyinbo HO; Hayman DTS
    Lassa fever is a West African rodent-borne viral haemorrhagic fever that kills thousands of people a year, with 100 000 to 300 000 people a year probably infected by Lassa virus (LASV). The main reservoir of LASV is the Natal multimammate mouse, Mastomys natalensis. There is reported asynchrony between peak infection in the rodent population and peak Lassa fever risk among people, probably owing to differing seasonal contact rates. Here, we developed a susceptible-infected-recovered ([Formula: see text])-based model of LASV dynamics in its rodent host, M. natalensis, with a persistently infected class and seasonal birthing to test the impact of changes to seasonal birthing in the future owing to climate and land use change. Our simulations suggest shifting rodent birthing timing and synchrony will alter the peak of viral prevalence, changing risk to people, with viral dynamics mainly stable in adults and varying in the young, but with more infected individuals. We calculate the time-average basic reproductive number, [Formula: see text], for this infectious disease system with periodic changes to population sizes owing to birthing using a time-average method and with a sensitivity analysis show four key parameters: carrying capacity, adult mortality, the transmission parameter among adults and additional disease-induced mortality impact the maintenance of LASV in M. natalensis most, with carrying capacity and adult mortality potentially changeable owing to human activities and interventions.
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    On the analysis of a heterogeneous coupled network of memristive Chialvo neurons
    (Springer Nature, 2023-09-01) Ghosh I; Muni SS; Fatoyinbo HO
    We perform a numerical study on the application of electromagnetic flux on a heterogeneous network of Chialvo neurons represented by a ring-star topology. Heterogeneities are realized by introducing additive noise modulations on both the central–peripheral and the peripheral–peripheral coupling links in the topology not only varying in space but also in time. The variation in time is understood by two coupling probabilities, one for the central–peripheral connections and the other for the peripheral–peripheral connections, respectively, that update the network topology with each iteration in time. We have further reported various rich spatiotemporal patterns like two-cluster states, chimera states, coherent, and asynchronized states that arise throughout the network dynamics. We have also investigated the appearance of a special kind of asynchronization behavior called “solitary nodes” that have a wide range of applications pertaining to real-world nervous systems. In order to characterize the behavior of the nodes under the influence of these heterogeneities, we have studied two different metrics called the “cross-correlation coefficient” and the “synchronization error.” Additionally, to capture the statistical property of the network, for example, how complex the system behaves, we have also studied a measure called “sample entropy.” Various two-dimensional color-coded plots are presented in the study to exhibit how these metrics/measures behave with the variation of parameters.
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    On the higher-order smallest ring-star network of Chialvo neurons under diffusive couplings
    (American Institute of Physics, 2024-07-18) Nair AS; Ghosh I; Fatoyinbo HO; Muni SS
    Network dynamical systems with higher-order interactions are a current trending topic, pervasive in many applied fields. However, our focus in this work is neurodynamics. We numerically study the dynamics of the smallest higher-order network of neurons arranged in a ring-star topology. The dynamics of each node in this network is governed by the Chialvo neuron map, and they interact via linear diffusive couplings. This model is perceived to imitate the nonlinear dynamical properties exhibited by a realistic nervous system where the neurons transfer information through multi-body interactions. We deploy the higher-order coupling strength as the primary bifurcation parameter. We start by analyzing our model using standard tools from dynamical systems theory: fixed point analysis, Jacobian matrix, and bifurcation patterns. We observe the coexistence of disparate chaotic attractors. We also observe an interesting route to chaos from a fixed point via period-doubling and the appearance of cyclic quasiperiodic closed invariant curves. Furthermore, we numerically observe the existence of codimension-1 bifurcation points: saddle-node, period-doubling, and Neimark-Sacker. We also qualitatively study the typical phase portraits of the system, and numerically quantify chaos and complexity using the 0-1 test and sample entropy measure, respectively. Finally, we study the synchronization behavior among the neurons using the cross correlation coefficient and the Kuramoto order parameter. We conjecture that unfolding these patterns and behaviors of the network model will help us identify different states of the nervous system, further aiding us in dealing with various neural diseases and nervous disorders.
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    Pattern Formation in a Spatially Extended Model of Pacemaker Dynamics in Smooth Muscle Cells.
    (Springer Nature Switzerland AG on behalf of the Society for Mathematical Biology, 2022-07-08) Fatoyinbo HO; Brown RG; Simpson DJW; van Brunt B
    Spatiotemporal patterns are common in biological systems. For electrically coupled cells, previous studies of pattern formation have mainly used applied current as the primary bifurcation parameter. The purpose of this paper is to show that applied current is not needed to generate spatiotemporal patterns for smooth muscle cells. The patterns can be generated solely by external mechanical stimulation (transmural pressure). To do this we study a reaction-diffusion system involving the Morris-Lecar equations and observe a wide range of spatiotemporal patterns for different values of the model parameters. Some aspects of these patterns are explained via a bifurcation analysis of the system without coupling - in particular Type I and Type II excitability both occur. We show the patterns are not due to a Turing instability and that the spatially extended model exhibits spatiotemporal chaos. We also use travelling wave coordinates to analyse travelling waves.
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