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We formulate a compartmental model of the murine lymphatic system with the transfer rate parameters derived from the data on the geometric characteristics of the lymphatic system (LS) graph structure and the Hagen–Poiseuille-based values of the lymph flows through the system components, i.e., vertices and edges. It is supplemented by the physics-based model of lymph node draining-related function which considers a paradigmatic view of its geometry with one- and three-afferent lymphatic vessels and one efferent vessel, and the lymph flow described by the Darcy–Starling equations. We discuss further modelling work needed to gain a predictive understanding of the LS function in response to various perturbations including infections and therapeutic treatments. [ABSTRACT FROM AUTHOR]

Understanding how genetically encoded rules drive and guide complex neuronal growth processes is essential to comprehending the brain’s architecture, and agent-based models (ABMs) offer a powerful simulation approach to further develop this understanding. However, accurately calibrating these models remains a challenge. Here, we present a novel application of Approximate Bayesian Computation (ABC) to address this issue. ABMs are based on parametrized stochastic rules that describe the time evolution of small components–the so-called agents–discretizing the system, leading to stochastic simulations that require appropriate treatment. Mathematically, the calibration defines a stochastic inverse problem. We propose to address it in a Bayesian setting using ABC. We facilitate the repeated comparison between data and simulations by quantifying the morphological information of single neurons with so-called morphometrics and resort to statistical distances to measure discrepancies between populations thereof. We conduct experiments on synthetic as well as experimental data. We find that ABC utilizing Sequential Monte Carlo sampling and the Wasserstein distance finds accurate posterior parameter distributions for representative ABMs. We further demonstrate that these ABMs capture specific features of pyramidal cells of the hippocampus (CA1). Overall, this work establishes a robust framework for calibrating agent-based neuronal growth models and opens the door for future investigations using Bayesian techniques for model building, verification, and adequacy assessment. [ABSTRACT FROM AUTHOR]

The kinetics of intravenous (IV) fluid therapy and how it affects the movement of fluids within humans and animals is an ongoing research topic. Clinical researchers have in the past used a mathematical model adopted from pharmacokinetics that attempts to mimic these kinetics. This linear model is based on the ideas that the body tries to maintain fluid levels in various compartments at some baseline targets and that fluid movement between compartments is driven by differences between the actual volumes and the targets. Here a nonlinear pressure-based model is introduced, where the driving force of fluid movement out of the blood stream is the pressure differences, both hydrostatic and oncotic, between the capillaries and the interstitial space. This model is, like the linear model, a coarse representation of fluid movement on the whole body scale, but, unlike the linear model, it is based on some of the body’s biophysical processes. The abilities of both models to fit data from experiments on both awake and anesthetized cats was analyzed. The pressure-based model fit the data better than the linear model in all but one case, and was deemed statistically significantly better in a third of the cases. [ABSTRACT FROM AUTHOR]

Models with several levels of mixing (households, workplaces), as well as various corresponding formulations for R 0 , have been proposed in the literature. However, little attention has been paid to the impact of the distribution of the population size within social structures, effect that can help plan effective interventions. We focus on the influence on the model outcomes of teleworking strategies, consisting in reshaping the distribution of workplace sizes. We consider a stochastic SIR model with two levels of mixing, accounting for a uniformly mixing general population, each individual belonging also to a household and a workplace. The variance of the workplace size distribution appears to be a good proxy for the impact of this distribution on key outcomes of the epidemic, such as epidemic size and peak. In particular, our findings suggest that strategies where the proportion of individuals teleworking depends sublinearly on the size of the workplace outperform the strategy with linear dependence. Besides, one drawback of the model with multiple levels of mixing is its complexity, raising interest in a reduced model. We propose a homogeneously mixing SIR ODE-based model, whose infection rate is chosen as to observe the growth rate of the initial model. This reduced model yields a generally satisfying approximation of the epidemic. These results, robust to various changes in model structure, are very promising from the perspective of implementing effective strategies based on social distancing of specific contacts. Furthermore, they contribute to the effort of building relevant approximations of individual based models at intermediate scales. [ABSTRACT FROM AUTHOR]

In this paper we study existence and uniqueness of solution stochastic differential equations involving fractional integrals driven by Riemann-Liouville multifractional Brownian motion and a standard Brownian. Then, we obtain approximate numerical solution of our problem and colon cancer chemotherapy effect model are presented to confirm our results. We show that considering time dependent Hurst parameters play an important role to get more realistic results. [ABSTRACT FROM AUTHOR]

Hand, foot and mouth disease (HFMD) is a Class C infectious disease that carries particularly high risk for preschool children and is a leading cause of childhood death in some countries. We mimic the periodic outbreak of HFMD over a 2-year period—with differing amplitudes—and propose a dynamic HFMD model that differentiates transmission between mature and immature individuals and uses two possible optimal-control strategies to minimize case numbers, total costs and deaths. We parameterized the model by fitting it to HFMD data in mainland China from January 2011 to December 2018, and the basic reproduction number was estimated as 0.9599. Sensitivity analysis demonstrates that transmission between immature and mature individuals contributes substantially to new infections. Increasing the isolation rates of infectious individuals—particularly mature infectious individuals—could greatly reduce the outbreak risk and potentially eradicate the disease in a relatively short time period. It follows that we have a reasonable chance of controlling HFMD if we can reduce transmission in children under 7 and isolate older infectious individuals. [ABSTRACT FROM AUTHOR]

The dynamics of discrete-time two-species systems may not be simple even if they have no positive fixed points. To reveal the behaviour of such systems, we focus on a special class of discrete-time two-species systems whose degenerate cases have a line segment of positive fixed points. As a main result, we show that the line segment of positive fixed points persists as an invariant curve under a small perturbation of the degenerate case. The absence of a positive fixed point ensures that such an invariant curve consists of heteroclinic orbits connecting two axial fixed points. The general result is applied to a discrete-time competition model of Ricker type with reproductive delay. The application reveals the existence of heteroclinic orbits connecting two axial 2-cycles, not only heteroclinic orbits connecting two axial fixed points. [ABSTRACT FROM AUTHOR]

Foraging for resources is an essential process for the daily life of an ant colony. What makes this process so fascinating is the self-organization of ants into trails using chemical pheromone in the absence of direct communication. Here we present a stochastic lattice model that captures essential features of foraging ant dynamics inspired by recent agent-based models while forgoing more detailed interactions that may not be essential to trail formation. Nevertheless, our model’s results coincide with those presented in more sophisticated theoretical models and experiments. Furthermore, it captures the phenomenon of multiple trail formation in environments with multiple food sources. This latter phenomenon is not described well by other more detailed models. We complement the stochastic lattice model by describing a macroscopic PDE which captures the basic structure of lattice model. The PDE provides a continuum framework for the first-principle interactions described in the stochastic lattice model and is amenable to analysis. Linear stability analysis of this PDE facilitates a computational study of the impact various parameters impart on trail formation. We also highlight universal features of the modeling framework that may allow this simple formation to be used to study complex systems beyond ants. [ABSTRACT FROM AUTHOR]

In this paper, we consider the stochastic nutrient–phytoplankton–zooplankton model with nutrient cycle. In order to take stochastic fluctuations into account, we add the stochastic increments to the variations of biomass of nutrition, phytoplankton and zooplankton during time interval Δ t , thus we obtain the corresponding stochastic model. Subsequently, we explore the existence, uniqueness and stochastically ultimate boundness of global positive solution. By constructing suitable Lyapunov function, we also obtain V-geometric ergodicity of this model. In addition, the sufficient conditions of exponential extinction and persistence in the mean of plankton are established. At last, we present some numerical simulations to validate theoretical results and analyze the impacts of some important parameters. [ABSTRACT FROM AUTHOR]

This study explores a prey-predator model with a Holling type II functional response, focusing on how predation-induced fear affects prey dynamics. Assuming a decline in prey population growth rate attributed to predator-induced fear, the model incorporates a fear response delay representing prey detection time, along with gestation delay. The system's positivity, boundedness, and permanence are proved under certain parametric conditions. The local stability is discussed at trivial, semi-trivial, and positive equilibria. The system exhibits Hopf bifurcation with respect to both delays. Hopf bifurcation analysis is done for different combinations of delays. Furthermore, the properties of periodic solutions in the delayed system are also determined. An extensive numerical simulation has been performed to validate analytical findings. The occurrence of Hopf bifurcation is shown for different combinations of delays by plotting eigenvalues. [ABSTRACT FROM AUTHOR]

Repulsion systems under the discrete dynamical systems perspective are considered. The goal is to study the nonlinear dynamics of an arrangement of individual or cells under various types of repulsion laws. Repulsion is a natural mechanism, so we consider the following laws: global, open, cyclic and bounded repulsion. A first scenario under consideration is when particles are freely located on an Euclidean space and the second scenario where they are restricted to remain in a bounded set. We consider those two scenarios under the previous types of repulsion. The analysis culminates in a unification of two mechanisms: attraction and repulsion via a specific social behaviour problem. [ABSTRACT FROM AUTHOR]

In this paper, we analyze the stability and Hopf bifurcation of the fractional-order logistic equation with two different delays τ 1 , τ 2 > 0 : D α y (t) = ρ y (t - τ 1) 1 - y (t - τ 2) , t > 0 , ρ > 0 . We describe stability regions by using critical curves. We explore how the fractional order α , ρ , and time delays influence the stability and Hopf bifurcation of the model. Then, by choosing ρ , fractional order α , and time delays as bifurcation parameters, the existence of Hopf bifurcation is studied. An Adams-type predictor–corrector method is extended to solve fractional-order differential equations involving two different delays. Finally, numerical simulations are given to illustrate the effectiveness and feasibility of theoretical results. [ABSTRACT FROM AUTHOR]

Many flying and swimming creatures have morphing pectoral propulsors (wings or fins) for propulsion, typically with flapping, rowing, and pitching motions; flapping and rowing motions are responsible for the stroke plane angle that is important for a broader performance space of the propulsor, while the stroke plane angle has been less characterized and implemented by artificial propulsors of biomimetic vehicles and thus has lack of stroke plane angle control. In this paper, we consider robotic pectoral propulsors with combined flapping and rowing motions for a stroke plane angle that can be generally specified. We consider two possible rotation axes configurations (i.e., the dependence of the rotation axes for flapping and rowing). For each rotation axes configuration, we propose the kinematic relations between the flapping and rowing motions for a generally specified stroke plane angle and provide the general flapping (or rowing) kinematics as a function of the rowing (or flapping) kinematics, which have not been characterized previously. These results serve as the reference trajectories of the propulsor for specified stroke plane angles and have implications for stroke plane angle control and thus have implications to achieve a broader performance space for biomimetic propulsors. [ABSTRACT FROM AUTHOR]

Alzheimer’s disease is one of the most prevalent types of dementia worldwide. It is caused by the accumulation of amyloid-beta (Aβ) plaques in the brain, disrupting communication pathways and memory. Microglia and astrocytes act as the immune system of the brain, clearing Aβ plaque deposits. However, these cells can lose effectiveness when Aβ plaque accumulation exceeds normal limits, leading to inflammation induced by proinflammatory cytokines. One type of treatment involves anti-Aβ drug therapy. Anti-Aβ drugs are believed to have the ability to reduce Aβ plaque deposits effectively. The mechanism of Aβ plaque accumulation can be explained by ordinary differential equations describing the growth of Aβ monomers. In this study, we aimed to develop a new mathematical model to elucidate the role of the immune system and drug therapy in reducing Aβ plaque deposits. Based on the simulation results, we conclude that the use of anti-Aβ drug therapy can decrease the concentration of Aβ plaque deposits, and the effective treatment duration for Alzheimer’s patients is estimated to be approximately 4 months starting from the time the drug was first administered.

Although heart disease stands as a prominent contributor to worldwide deaths, not all individuals affected by it ultimately fall prey to its effects. Timely diagnosis and effective treatment can offer those with heart conditions a high-quality life in their later years. Consequently, early disease detection using accessible medical data has been a central goal for researchers in recent decades. Traditionally, researchers relied on statistical tools for this purpose. However, machine learning algorithms, especially classification models, have gained prominence with the growing accumulated data. These algorithms have shown promise in predicting heart disease based on individual data. Our study employed various classification algorithms to predict heart disease incidence using the available dataset. We prioritized model reliability by incorporating the conformal classifier. Our results have shown that boosting algorithms, such as XGBoost and CatBoost, demonstrated exceptional performance with promising metrics. These models identified chest pain type and ST segment slope as crucial indicators of heart disease. Boosting algorithms exhibited a compelling combination of broad coverage and a small prediction set size, making them well-suited for heart disease prediction. Furthermore, we employed explainable artificial intelligence-boosting algorithms to enhance the interpretability of our predictions.

We develop variations of a discrete-time predator–prey model to examine how intrinsic properties of the prey population may impact overall system dynamics. We focus on two properties of the prey species, namely developmental stage structure and undercompensatory (contest competition) versus overcompensatory (scramble competition) density dependence. Through analysis of these models, we examine how these different prey features affect system stability. Our results show that when prey growth is overcompensatory, the predator may have a stabilizing effect on the system dynamics with increasing predator density reversing the period doubling route to chaos observed with Ricker-type nonlinearities. Moreover, we find that stage structure in the prey does not have a destabilizing effect on the system dynamics unless the prey projection matrix is imprimitive or close to imprimitive, as may occur for semelparous species. In this case, a sufficiently large predator density may stabilize cycles that are otherwise unstable. [ABSTRACT FROM AUTHOR]

In this paper, we develop an integrodifference equation model that incorporates spatial memory and learning so that each year, a fraction of the population use the same dispersal kernel as the previous year, and the remaining individuals return to where they bred or were born. In temporally static environments, the equilibrium of the system corresponds to an ideal free dispersal strategy, which is evolutionarily stable. We prove local stability of this equilibrium in a special case, and we observe convergence towards this equilibrium in numerical computations. When there are periodic or stochastic temporal changes in the environment, the population is less able to match the environment, but is able to do so to some extent depending on the parameters. Overall, the mechanism proposed in this model shows a possible way for the dispersal kernel of a population to evolve towards an ideal free dispersal kernel. [ABSTRACT FROM AUTHOR]

Cancer, a disease intimately linked to cellular mutations, is commonly believed to exhibit a positive association with the cell count and lifespan of a species. Despite this assumption, the observed uniformity in cancer rates across species, referred to as the Peto’s paradox, presents a conundrum. Recognizing that tumour progression is not solely dependent on cancer cells but involves intricate interactions among various cell types, this study employed a Lotka-Volterra (LV) ordinary differential equation model to analyze the evolution of cancerous cells and the cancer incidence in an immune environment. As a result, this study uncovered the sufficient conditions underlying the absence of correlation in Peto’s paradox and provide insights into the reasons for the equitable distribution of cancer incidence across diverse species by applying nondimensionalization and drawing an analogy between the characteristic time interval for the variation of cell populations in the ODE model and that of cell cycles of a species. [ABSTRACT FROM AUTHOR]

The Ebola virus disease (EVD) has been endemic since 1976, and the case fatality rate is extremely high. EVD is spread by infected animals, symptomatic individuals, dead bodies, and contaminated environment. In this paper, we formulate an EVD model with four transmission modes and a time delay describing the incubation period. Through dynamical analysis, we verify the importance of blocking the infection source of infected animals. We get the basic reproduction number without considering the infection source of infected animals. And, it is proven that the model has a globally attractive disease-free equilibrium when the basic reproduction number is less than unity; the disease eventually becomes endemic when the basic reproduction number is greater than unity. Taking the EVD epidemic in Sierra Leone in 2014–2016 as an example, we complete the data fitting by combining the effect of the media to obtain the unknown parameters, the basic reproduction number and its time-varying reproduction number. It is shown by parameter sensitivity analysis that the contact rate and the removal rate of infected group have the greatest influence on the prevalence of the disease. And, the disease-controlling thresholds of these two parameters are obtained. In addition, according to the existing vaccination strategy, only the inoculation ratio in high-risk areas is greater than 0.4, the effective reproduction number can be less than unity. And, the earlier the vaccination time, the greater the inoculation ratio, and the faster the disease can be controlled. [ABSTRACT FROM AUTHOR]

To understand the dynamics of neurodegenerative disease, we consider a fractional-order yeast prion mathematical model. We characterize the system dynamics by locating the steady states and perform their local stability analysis both theoretically and numerically. We employ the homotopy perturbation method to determine approximate solutions for the proposed model. By varying the fractional orders, we generate plots of these solutions over time to examine the dynamics of the Sup35 monomer and Sup35 prion populations. Our simulations reveal that the dynamical behaviors of both populations can undergo alterations depending upon the chosen fractional orders and the value of the threshold parameter. [ABSTRACT FROM AUTHOR]

Our work is dedicated to the introduction and investigation of a new asymptotic correlation relation in the field of mean field models and limits. This new notion, order (as opposed to chaos), revolves around a tendency for self organisation in a given system and is expected to be observed in biological and societal models. Beyond the definition of this new notion, our work will show its applicability, and propagation, in the so-called choose the Leader model. [ABSTRACT FROM AUTHOR]

A system of partial differential equations is developed to study the spreading of tau pathology in the brain for Alzheimer’s and other neurodegenerative diseases. Two cases are considered with one assuming intracellular diffusion through synaptic activities or the nanotubes that connect the adjacent cells. The other, in addition to intracellular spreading, takes into account of the secretion of the tau species which are able to diffuse, move with the interstitial fluid flow and subsequently taken up by the surrounding cells providing an alternative pathway for disease spreading. Cross membrane transport of the tau species are considered enabling us to examine the role of extracellular clearance of tau protein on the disease status. Bifurcation analysis is carried out for the steady states of the spatially homogeneous system yielding the results that fast cross-membrane transport combined with effective extracellular clearance is key to maintain the brain’s healthy status. Numerical simulations of the first case exhibit solutions of travelling wave form describing the gradual outward spreading of the pathology; whereas the second case shows faster spreading with the buildup of neurofibrillary tangles quickly elevated throughout. Our investigation thus indicates that the gradual progression of the intracellular spreading case is more consistent with the clinical observations of the development of Alzheimer’s disease. [ABSTRACT FROM AUTHOR]

The Random Variable Transformation (RVT) technique has been applied in recent years to analyze a wide variety of dynamic models formulated via random differential equations. The applicability of this technique has usually been focused on problems where an explicit solution of the underlying deterministic problem is available. This fact limits the usefulness of the RVT method. This note aims to point out that the RVT technique can be successfully applied without this requirement by showing a wider range of potential applications including very general classes of single-species models. [ABSTRACT FROM AUTHOR]

Tumour growth is a complex process influenced by various factors, including cell proliferation, migration, and chemotaxis. In this study, a biphasic chemotaxis model for tumour growth is considered, and the effect of chemotherapy on the growth process is investigated. We use optimal control theory to derive the optimized treatment strategy that minimises the tumour size while minimising the toxicity associated with chemotherapy. Moreover, the existence, uniqueness, and strong solution estimates for the biphasic chemotaxis model subsystem in one dimension are derived. These results are achieved through semigroup theory and the truncation method. In addition, the research provides evidence of the existence of an optimal pair through the utilization of the minimising sequence technique. It also demonstrates the differentiability of the mapping from control variable to state variable and establishes the first-order necessary optimality condition. Lastly, a sequence of numerical simulations are presented to showcase the impact of chemotherapy and the influence of parameters in restraining tumour growth when applied in an optimized manner. Our results show that optimal control can provide a more effective and personalised treatment for cancer patients, and the approach can be extended to other tumour growth models. [ABSTRACT FROM AUTHOR]

In epidemics, waning immunity is common after infection or vaccination of individuals. Immunity levels are highly heterogeneous and dynamic. This work presents an immuno-epidemiological model that captures the fundamental dynamic features of immunity acquisition and wane after infection or vaccination and analyzes mathematically its dynamical properties. The model consists of a system of first order partial differential equations, involving nonlinear integral terms and different transfer velocities. Structurally, the equation may be interpreted as a Fokker-Planck equation for a piecewise deterministic process. However, unlike the usual models, our equation involves nonlocal effects, representing the infectivity of the whole environment. This, together with the presence of different transfer velocities, makes the proved existence of a solution novel and nontrivial. In addition, the asymptotic behavior of the model is analyzed based on the obtained qualitative properties of the solution. An optimal control problem with objective function including the total number of deaths and costs of vaccination is explored. Numerical results describe the dynamic relationship between contact rates and optimal solutions. The approach can contribute to the understanding of the dynamics of immune responses at population level and may guide public health policies. [ABSTRACT FROM AUTHOR]

We present a mathematical model of an experiment in which cells are cultured within a gel, which in turn floats freely within a liquid nutrient medium. Traction forces exerted by the cells on the gel cause it to contract over time, giving a measure of the strength of these forces. Building upon our previous work (Reoch et al. in J Math Biol 84(5):31, 2022), we exploit the fact that the gels used frequently have a thin geometry to obtain a reduced model for the behaviour of a thin, two-dimensional cell-seeded gel. We find that steady-state solutions of the reduced model require the cell density and volume fraction of polymer in the gel to be spatially uniform, while the gel height may vary spatially. If we further assume that all three of these variables are initially spatially uniform, this continues for all time and the thin film model can be further reduced to solving a single, non-linear ODE for gel height as a function of time. The thin film model is further investigated for both spatially-uniform and varying initial conditions, using a combination of analytical techniques and numerical simulations. We show that a number of qualitatively different behaviours are possible, depending on the composition of the gel (i.e., the chemical potentials) and the strength of the cell traction forces. However, unlike in the earlier one-dimensional model, we do not observe cases where the gel oscillates between swelling and contraction. For the case of initially uniform cell and gel density, our model predicts that the relative change in the gels’ height and length are equal, which justifies an assumption previously used in the work of Stevenson et al. (Biophys J 99(1):19–28, 2010). Conversely, however, even for non-uniform initial conditions, we do not observe cases where the length of the gel changes whilst its height remains constant, which have been reported in another model of osmotic swelling by Trinschek et al. (AIMS Mater Sci 3(3):1138–1159, 2016; Phys Rev Lett 119:078003, 2017). [ABSTRACT FROM AUTHOR]

The coexistence of multiple phytoplankton species despite their reliance on similar resources is often explained with mean-field models assuming mixed populations. In reality, observations of phytoplankton indicate spatial aggregation at all scales, including at the scale of a few individuals. Local spatial aggregation can hinder competitive exclusion since individuals then interact mostly with other individuals of their own species, rather than competitors from different species. To evaluate how microscale spatial aggregation might explain phytoplankton diversity maintenance, an individual-based, multispecies representation of cells in a hydrodynamic environment is required. We formulate a three-dimensional and multispecies individual-based model of phytoplankton population dynamics at the Kolmogorov scale. The model is studied through both simulations and the derivation of spatial moment equations, in connection with point process theory. The spatial moment equations show a good match between theory and simulations. We parameterized the model based on phytoplankters’ ecological and physical characteristics, for both large and small phytoplankton. Defining a zone of potential interactions as the overlap between nutrient depletion volumes, we show that local species composition—within the range of possible interactions—depends on the size class of phytoplankton. In small phytoplankton, individuals remain in mostly monospecific clusters. Spatial structure therefore favours intra- over inter-specific interactions for small phytoplankton, contributing to coexistence. Large phytoplankton cell neighbourhoods appear more mixed. Although some small-scale self-organizing spatial structure remains and could influence coexistence mechanisms, other factors may need to be explored to explain diversity maintenance in large phytoplankton. [ABSTRACT FROM AUTHOR]

One-dimensional discrete-time population models, such as those that involve Logistic or Ricker growth, can exhibit periodic and chaotic dynamics. Expanding the system by one dimension to incorporate epidemiological interactions causes an interesting complexity of new behaviors. Here, we examine a discrete-time two-dimensional susceptible-infectious (SI) model with Ricker growth and show that the introduction of infection can not only produce a distinctly different bifurcation structure than that of the underlying disease-free system but also lead to counter-intuitive increases in population size. We use numerical bifurcation analysis to determine the influence of infection on the location and types of bifurcations. In addition, we examine the appearance and extent of a phenomenon known as the 'hydra effect,' i.e., increases in total population size when factors, such as mortality, that act negatively on a population, are increased. Previous work, primarily focused on dynamics at fixed points, showed that the introduction of infection that reduces fecundity to the SI model can lead to a so-called 'infection-induced hydra effect.' Our work shows that even in such a simple two-dimensional SI model, the introduction of infection that alters fecundity or mortality can produce dynamics can lead to the appearance of a hydra effect, particularly when the disease-free population is at a cycle. [ABSTRACT FROM AUTHOR]

Globally, there is a high incidence of Syphilis among men who have sex with men (MSM). This is due to risk factors, such as having multiple partners, condomless sex, or substance abuse. In this paper, we present a mathematical model for Syphilis transmission dynamics among the MSM popion that incorporates high/low-risk transmission classes. The model equilibrium points and basic reproduction number ( R 0 ) are computed, and a bifurcation analysis is performed. Analytical results show that in the absence of infection-acquired immunity, the disease-free equilibrium is globally asymptotically stable when R 0 < 1. Global sensitivity analysis was performed using the Latin-hypercube sampling technique to determine sensitive parameters. The results indicate that the most sensitive parameters are the high-risk transmission rate, progression rate from primary infection to secondary infection, human recruitment, and mortality rates. Results from numerical simulations suggest that increasing the Syphilis treatment rates and reducing high/low-risk infection rates is essential for controlling Syphilis spread in the MSM population. We envisage that our findings can serve as a tool for Syphilis risk assessment to help alleviate public health concerns associated with Syphilis transmission within the MSM population. [ABSTRACT FROM AUTHOR]

This article addresses the pressing issue of pest outbreaks in India, which poses significant challenges for farmers and ecologists. A novel system is proposed for effective control that leverages natural enemies. Here, the pests are classified into juveniles and mature individuals, further categorized as susceptible or infected. The study introduces harvesting, incorporating external efforts and natural phenomena, in a pest-epidemic prey–predator system featuring a prey-stage structure. The model reveals three equilibria: trivial, boundary (indicating the absence of natural enemies), and interior equilibria. Notably, the trivial equilibrium is consistently unstable. As demonstrated by stability analysis, the survival or extinction of natural enemies hinges on control variables, including the harvesting rate, disease transmission rate, and natural death rate. Local stability is assessed using the Routh–Hurwitz criterion, while global stability is explored through the Lyapunov method. Furthermore, optimal control theory and Pontryagin’s maximum principle are applied for model optimization, unveiling crucial optimality conditions and determining the optimal harvesting rate for susceptible mature prey. Numerical computations validate theoretical insights, offering valuable guidance for formulating policies that optimize the control of susceptible adult pests within a disease-induced pest-natural enemy system, ensuring sustained cost-effectiveness.

The basic reproduction number [Formula: see text] is one of the main parameters determining the spreading of an epidemic in a population of susceptible individuals. Wallinga and Lipsitch proposed a method for estimating [Formula: see text] using the Euler–Lotka equation, which requires the Laplace transform of the generation interval distribution. The determination of the generation time distribution is challenging, as the generation time is not directly observable. We prove upper and lower bounds on [Formula: see text] using only the first few moments of the generation interval distributions and study the sensitivity of the bounds to these parameters. The bounds do not require the exact shape of the generation interval distribution and give robust estimates of the [Formula: see text] relationship.

The outbreak of COVID-19 causes a serious threat to human health and life around the world and puts enormous pressure on the healthcare system. The lockdown policy has effectively reduced the number of cases and suppressed the spread of the COVID-19 epidemic, but it also requires high social and economic costs. In this paper, we aim to use feedback control to help decision-makers establish lockdown policies, which can effectively constrain the spread of the COVID-19 pandemic with minimal financial loss. The time-dependent susceptible-infected-removed (SIR) model is used for the dynamics of the COVID-19 pandemic. The feedback control is based on a modified nonlinear active disturbance rejection controller (ADRC) that includes modified nonlinear extended state and nonlinear state error feedback with parameters tuned by particle swarm optimization, and the performances are compared with a well-known proportional-–integral–-derivative (PID) controller. The final simulation results show that the modified nonlinear ADRC has better performances and is more robust against uncertainties in the parameters of the epidemiological model.

In this paper, the existing Susceptible-Infected-Recovered-Deceased (SIRD) compartmental epidemiologic process model is modified for forecasting the coronavirus effect in India. The data from India was studied for weekly fatalities, weekly infected, weekly recovered, new cases, infected and recovered individuals, Reproductive Number [Formula: see text], recovery rate, death rate, and coefficient of transmission from 30 January 2020 to 31 July 2021. SARS Coronavirus 2 (SARS-CoV-2) is the Covid strain that causes Covid sickness (COVID-19), a respiratory ailment that triggered the outbreak of COVID-19 at the beginning of December 2019. We aim to provide a hybrid SIRD model for predicting the COVID-19 outbreak. In the proposed method, to improve the exploration ability of the Grey Wolf Optimizer (GWO) or to avoid stagnation in the swarm, a modified Grey Wolf Optimization Algorithm is used to optimize the initial value of Infected individuals. The modified SIRD model is further applied to get the predicted values. The data is examined on weekly basis to prevent noise. Depending on the fact, that the precise mode of transmission is highly dependent on how and when different precautions such as isolation, confinement, and other preventative measures were implemented, we put together our projections concerning satisfactory speculations based on genuine realities. The experimental results show the various trends observed in the pandemic in terms of number of peaks, increasing trend, decreasing trend, and continuous trend for infected individuals, weekly change in number of cases, weekly deaths, weekly infected, and weekly recoeverd cases of Covid-19. The proposed modified SIRD model could be a valuable tool for assessing the impact of government measures on COVID-19 outbreak.

ABSTRACTThroughout the last two centuries, vaccines have been helpful in mitigating numerous epidemic diseases. However, vaccine hesitancy has been identified as a substantial obstacle in healthcare management. We examined the epidemiological dynamics of an emerging infection under vaccination using an SVEIR model with differential morbidity. We mathematically analyzed the model, derived [Formula: see text], and provided a complete analysis of the bifurcation at [Formula: see text]. Sensitivity analysis and numerical simulations were used to quantify the tradeoffs between vaccine efficacy and vaccine hesitancy on reducing the disease burden. Our results indicated that if the percentage of the population hesitant about taking the vaccine is 10%, then a vaccine with 94% efficacy is required to reduce the peak of infections by 40%. If 60% of the population is reluctant about being vaccinated, then even a perfect vaccine will not be able to reduce the peak of infections by 40%.

The main four stages of wound healing are hemostasis, inflammation, proliferation, and remodelling. In the inflammation stage, the body rids of pathogens and debris, and prepares for tissue regrowth. An ordinary differential equation model of the inflammation is constructed that expands on previous models and incorporates dynamics reported in the updated literature. The interaction between debris and apoptotic neutrophils, along with classical and alternative macrophages are included. In all, the model incorporates pathogens, debris, apoptotic neutrophils, neutrophil, M1 macrophages, and M2 macrophages. Parameter values are analysed based on 1. experimental data and 2. reported ranges and expected peak times of white blood cells. Next, global sensitivity analyses which is able to test change in parameter values in tandem is conducted to interpret the overall influence each parameter has on white blood cell, debris, and pathogen output. [ABSTRACT FROM AUTHOR]

A mechanistic model that incorporates public health enlightenment, treatment, and snake developmental stages is developed and rigorously analyzed. The model is used to gain more insights into the dynamics and control of snakebite envenoming (SBE). Some interesting equilibrium points that demonstrate the presence and absence of snakes in a given population were computed. It has been shown that the existence or extinction of snakes in a given community depends on snake existence threshold (RS). Whenever the threshold is greater than one, snakes exist in the community, while if the threshold is exactly equal to one, then snakes seize to exist. Furthermore, a rigorous qualitative study of the model revealed that each of the snake-free, SBE-free, and SBE-endemic equilibrium points is globally asymptotically stable. In addition, a sensitivity analysis of the model parameters was conducted in order to identify the most influential parameters on SBE transmission dynamics. It is shown that an effective contact rate is the most important parameter. Therefore, preventive strategies that focus on reducing contact between humans and snakes, such as the use of snakebite repellent and protective gloves, are very important in controlling SBE. Finally, simulations have indicated that public health education on the use of snakebite preventive measures in combination with treatment of SBE victims is critical in reducing the number of new cases and deaths, respectively. [ABSTRACT FROM AUTHOR]

We consider a deterministic model of HIV infection that involves macrophages as a long-term active reservoir to describe all three stages of the disease process: the acute stage, chronic infection, and the transition to AIDS. The proposed model is shown to retain crucial properties, such as the positivity of solutions, regardless of variations in model parameters. A dynamical analysis is performed to identify the local stability properties of the viral clearance steady state. This analysis illustrates how chronically infected macrophages can explain the progression to AIDS and provoke viral explosion, while previous models do not. We further demonstrate that the infected T-cell population, even if not responsible for the majority of new infections that lead to viral explosion, may contribute significantly to the transition amongst the three stages of infection. Moreover, we explore the implications of the model for the administration of antiretroviral therapy (ART) and provide quantitative estimates that emphasize the time sensitive nature of treatment initiation and the level of drug efficacy. Finally, we study the effects of treatment interruption on the disease dynamics predicted by the model and elucidate the influence of both interruption time and duration. [ABSTRACT FROM AUTHOR]

Agent-based models (ABMs) are readily used to capture the stochasticity in tumour evolution; however, these models are often challenging to validate with experimental measurements due to model complexity. The Voronoi cell-based model (VCBM) is an off-lattice agent-based model that captures individual cell shapes using a Voronoi tessellation and mimics the evolution of cancer cell proliferation and movement. Evidence suggests tumours can exhibit biphasic growth in vivo. To account for this phenomena, we extend the VCBM to capture the existence of two distinct growth phases. Prior work primarily focused on point estimation for the parameters without consideration of estimating uncertainty. In this paper, approximate Bayesian computation is employed to calibrate the model to in vivo measurements of breast, ovarian and pancreatic cancer. Our approach involves estimating the distribution of parameters that govern cancer cell proliferation and recovering outputs that match the experimental data. Our results show that the VCBM, and its biphasic extension, provides insight into tumour growth and quantifies uncertainty in the switching time between the two phases of the biphasic growth model. We find this approach enables precise estimates for the time taken for a daughter cell to become a mature cell. This allows us to propose future refinements to the model to improve accuracy, whilst also making conclusions about the differences in cancer cell characteristics. [ABSTRACT FROM AUTHOR]

Reticulations in a phylogenetic network represent processes such as gene flow, admixture, recombination and hybrid speciation. Extending definitions from the tree setting, an anomalous network is one in which some unrooted tree topology displayed in the network appears in gene trees with a lower frequency than a tree not displayed in the network. We investigate anomalous networks under the Network Multispecies Coalescent Model with possible correlated inheritance at reticulations. Focusing on subsets of 4 taxa, we describe a new algorithm to calculate quartet concordance factors on networks of any level, faster than previous algorithms because of its focus on 4 taxa. We then study topological properties required for a 4-taxon network to be anomalous, uncovering the key role of 3 2 -cycles: cycles of 3 edges parent to a sister group of 2 taxa. Under the model of common inheritance, that is, when each gene tree coalesces within a species tree displayed in the network, we prove that 4-taxon networks are never anomalous. Under independent and various levels of correlated inheritance, we use simulations under realistic parameters to quantify the prevalence of anomalous 4-taxon networks, finding that truly anomalous networks are rare. At the same time, however, we find a significant fraction of networks close enough to the anomaly zone to appear anomalous, when considering the quartet concordance factors observed from a few hundred genes. These apparent anomalies may challenge network inference methods. [ABSTRACT FROM AUTHOR]

The historic total global human population dataset is available on Wikipedia and provides an opportunity for modelling with simple models such as the exponential and logistic differential equations for population. Using the per-capita population growth rate (PPGR) predicted by these two models and estimated PPGR from the data, we are able to estimate parameters for the exponential model and discover that we cannot estimate parameters for the logistic model. However, we are able to create a new differential equation that models this data using ideas from the derivation of the logistic model. This is the superexponential model that goes to infinity in finite time. While the initial exponential and superexponential models are good for approximating the population data, neither really matches the PPGRs estimated from the data. We switch to a hybrid model that better matches the estimated PPGRs. It is superexponential for the first portion of the data and logistic for the rest. This hybrid model greatly reduces the modelling error and can extrapolate from the data. We note that the model switches between superexponential and logistic around 1960, the year the FDA-approved oral contraceptives, a fascinating historical tie-in with our modelling. [ABSTRACT FROM AUTHOR]

When examining some factors that contribute to the growth or decline of a population or tumor, it is essential to consider a random hypothesis. By analyzing the effects of stress on a population (or volume of tumor growth) in a random environment, we develop stochastic models describing the dynamics of the population (or tumor growth) based on random adjustments to the population’s intrinsic growth rate, carrying capacity, and harvesting efforts (or tumor treatments). Apart from the models’ ability to capture fluctuations, the availability of a shape parameter in the models gives it the flexibility to describe a variety of population/tumor data with different shapes. The distribution of the stressed population size with or without harvesting (or treatments) is derived and used to calculate the maximum expected amount of harvests that can be taken from the population without depleting resources in the long run (or the minimum amount of chemotherapy needed to cause shrinkage or eradication of a tumor). The work done is applied to analyze tumor growth using published data comprising of the volume of breast tumor obtained by orthotopically implanting LM2- 4 L U C + cells into the right inguinal mammary fat pads of 6- to 8-week-old female Severe Combined Immuno-Deficient mice. [ABSTRACT FROM AUTHOR]

Convergent evolution is an important process in which independent species evolve similar features usually over a long period of time. It occurs with many different species across the tree of life, and is often caused by the fact that species have to adapt to similar environmental niches. In this paper, we introduce and study properties of a distance-based model for convergent evolution in which we assume that two ancestral species converge for a certain period of time within a collection of species that have otherwise evolved according to an evolutionary clock. Under these assumptions it follows that we obtain a distance on the collection that is a modification of an ultrametric distance arising from an equidistant phylogenetic tree. As well as characterising when this modified distance is a tree metric, we give conditions in terms of the model’s parameters for when it is still possible to recover the underlying tree and also its height, even in case the modified distance is not a tree metric. [ABSTRACT FROM AUTHOR]

This paper proposes and analyzes a five-dimensional tuberculosis model incorporating slow-fast progression, endogenous reactivation, exogenous reinfection, and the assumption that infectives pass the smear microscopy test. This study disseminates information regarding how the presence and infectiousness of smear-negative patients in a tuberculosis epidemic significantly affect the threshold epidemic quantity R 0 (basic reproduction number). Stability and bifurcation analysis is carried out, and we find that the exogenous reinfection causes backward bifurcation and multiple endemic steady states in the system. We analytically and numerically explored the case of the periodic oscillations in the population via Hopf bifurcation for R 0 < 1 as well as R 0 > 1. [ABSTRACT FROM AUTHOR]

Glioblastoma (GBM) is an aggressive primary brain cancer that currently has minimally effective treatments. Like other cancers, immunosuppression by the PD-L1-PD-1 immune checkpoint complex is a prominent axis by which glioma cells evade the immune system. Myeloid-derived suppressor cells (MDSCs), which are recruited to the glioma microenviroment, also contribute to the immunosuppressed GBM microenvironment by suppressing T cell functions. In this paper, we propose a GBM-specific tumor-immune ordinary differential equations model of glioma cells, T cells, and MDSCs to provide theoretical insights into the interactions between these cells. Equilibrium and stability analysis indicates that there are unique tumorous and tumor-free equilibria which are locally stable under certain conditions. Further, the tumor-free equilibrium is globally stable when T cell activation and the tumor kill rate by T cells overcome tumor growth, T cell inhibition by PD-L1-PD-1 and MDSCs, and the T cell death rate. Bifurcation analysis suggests that a treatment plan that includes surgical resection and therapeutics targeting immune suppression caused by the PD-L1-PD1 complex and MDSCs results in the system tending to the tumor-free equilibrium. Using a set of preclinical experimental data, we implement the approximate Bayesian computation (ABC) rejection method to construct probability density distributions that estimate model parameters. These distributions inform an appropriate search curve for global sensitivity analysis using the extended fourier amplitude sensitivity test. Sensitivity results combined with the ABC method suggest that parameter interaction is occurring between the drivers of tumor burden, which are the tumor growth rate and carrying capacity as well as the tumor kill rate by T cells, and the two modeled forms of immunosuppression, PD-L1-PD-1 immune checkpoint and MDSC suppression of T cells. Thus, treatment with an immune checkpoint inhibitor in combination with a therapeutic targeting the inhibitory mechanisms of MDSCs should be explored. [ABSTRACT FROM AUTHOR]

We investigate the existence, structure and stability of the nonconstant steady states for a predator–prey system with density-dependent motility under the Neumann boundary condition. By applying the Leray–Schauder degree theory, we show that under certain conditions, a small prey diffusion rate can ensure the existence of the nonconstant steady states, which is verified by numerical simulations. Over 1D domain, we treat prey diffusion rate as a bifurcation parameter and obtain the local and global structure of steady states near the homogeneous steady states with the aid of bifurcation theory and index theory. Moreover, a stability criterion of the bifurcating steady states is also presented. Finally, we give the existence and stability of time-periodic nontrivial solutions. [ABSTRACT FROM AUTHOR]

Ehrlichia chaffeensis is a tick-borne disease transmitted by ticks to dogs. Few studies have mathematical modelled such tick-borne disease in dogs, and none have developed models that incorporate different ticks' developmental stages (discrete variable) as well as the duration of infection (continuous variable). In this study, we develop and analyze a model that considers these two structural variables using integrated semigroups theory. We address the well-posedness of the model and investigate the existence of steady states. The model exhibits a disease-free equilibrium and an endemic equilibrium. We calculate the reproduction number ( $ \mathcal {T}_0 $ T 0 ). We establish a necessary and sufficient condition for the bifurcation of an endemic equilibrium. Specifically, we demonstrate that a bifurcation, either backward or forward, can occur at $ \mathcal {T}_0=1 $ T 0 = 1 , leading to the existence, or not, of an endemic equilibrium even when $ \mathcal {T}_0 \lt 1 $ T 0 < 1. Finally, numerical simulations are employed to illustrate these theoretical findings. [ABSTRACT FROM AUTHOR]

Populations are generally censused daily, weekly, monthly or annually. In this paper, we introduce a discrete-time nutrients-phytoplankton-oysters (NPO) model that describes the interactions of nutrients, phytoplankton and oysters in a bay ecosystem. We compute the threshold parameter $ \mathcal {R}_N $ R N for persistence of phytoplankton with or without oysters. When $ \mathcal {R}_{N} \lt 1 $ R N < 1 , then both phytoplankton and oysters populations go extinct. However, when $ \mathcal {R}_N \gt 1 $ R N > 1 , we show that the model may exhibit two scenarios: (1) a locally asymptotically stable equilibrium with positive values of nutrients and phytoplankton with oysters missing, and (2) a locally asymptotically stable interior equilibrium with positive values of nutrients, phytoplankton and oysters. We use sensitivity analysis to study the impact of human and environmental factors on the model. We use examples to illustrate that some human activities and environmental factors can force the interior equilibrium to undergo a Neimark–Sacker bifurcation which generates phytoplankton blooms with oscillations in oysters population and nutrients level. [ABSTRACT FROM AUTHOR]