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.

The rapid spread of highly pathogenic avian influenza (HPAI) A (H5N1) viruses in Southeast Asia in 2004 prompted the New Zealand Ministry for Primary Industries to expand its avian influenza surveillance in wild birds. A total of 18,693 birds were sampled between 2004 and 2020, including migratory shorebirds (in 2004–2009), other coastal species (in 2009–2010), and resident waterfowl (in 2004–2020). No avian influenza viruses (AIVs) were isolated from cloacal or oropharyngeal samples from migratory shorebirds or resident coastal species. Two samples from red knots (Calidris canutus) tested positive by influenza A RT-qPCR, but virus could not be isolated and no further characterization could be undertaken. In contrast, 6179 samples from 15,740 mallards (Anas platyrhynchos) tested positive by influenza A RT-qPCR. Of these, 344 were positive for H5 and 51 for H7. All H5 and H7 viruses detected were of low pathogenicity confirmed by a lack of multiple basic amino acids at the hemagglutinin (HA) cleavage site. Twenty H5 viruses (six different neuraminidase [NA] subtypes) and 10 H7 viruses (two different NA subtypes) were propagated and characterized genetically. From H5- or H7-negative samples that tested positive by influenza A RT-qPCR, 326 AIVs were isolated, representing 41 HA/NA combinations. The most frequently isolated subtypes were H4N6, H3N8, H3N2, and H10N3. Multivariable logistic regression analysis of the relations between the location and year of sampling, and presence of AIV in individual waterfowl showed that the AIV risk at a given location varied from year to year. The H5 and H7 isolates both formed monophyletic HA groups. The H5 viruses were most closely related to North American lineages, whereas the H7 viruses formed a sister cluster relationship with wild bird viruses of the Eurasian and Australian lineages. Bayesian analysis indicates that the H5 and H7 viruses have circulated in resident mallards in New Zealand for some time. Correspondingly, we found limited evidence of influenza viruses in the major migratory bird populations visiting New Zealand. Findings suggest a low probability of introduction of HPAI viruses via long-distance bird migration and a unique epidemiology of AIV in New Zealand.

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.

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 ( S I R )-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, R ~ p , 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.