This paper proposes an immunosuppressive infection model with time delay and stochastic perturbation. A stochastic threshold R 0 s is constructed, and the sufficient conditions for virus extinction and weak persistence are given. Subsequently, we respectively fit the SDDE and ODE models to the real data, and conduct a sensitivity analysis of the equilibrium. The greater the noise intensity, the more obvious the oscillation amplitude of the solution curve around the immune-free equilibrium, the larger noise intensity can cause the originally persistent virus in ODE to go extinct in the SDDE model. The greater the time delay, the longer it takes for the virus and immune cells to reach their first peaks. The viral replication rate significantly affects the virus-immune system, and the reproduction of HIV-1 can be inhibited by modulating it. A relatively high viral inhibition rate will lead to the extinction of immune cells while the virus persists.

The global dynamic behavior of the respiration process in bacterial culture at different concentrations was comprehensively described using the qualitative theory of differential equations and symbolic calculation software.

In this paper, we study the dynamics of a delayed predator–prey model with harvesting and prey refuge. For the model without time delays, we show the stability of boundary and positive equilibria, given the existence of bionomic equilibrium, and provide the optimal harvesting policy. For the model with time delays, we show the local stability of the positive equilibrium for four different time delay cases, give the existence of Hopf bifurcation near the positive equilibrium, and prove the direction and stability of bifurcating periodic solutions. Ecologically, via numerical simulations, we find that the synergistic effects of time delay, refuge and harvesting can have complex effects on population dynamics. One of the most important results indicates that the increase of prey refuge or prey harvesting rate can eliminate the periodic solutions induced by time delay, while the increase in predator harvesting rate can maintain this periodic phenomenon.

Measles is a highly contagious and potentially fatal disease, despite the availability of effective immunizations. This study formulates a deterministic mathematical model to investigate the transmission dynamics of measles, with eight compartments representing different epidemiological states such as susceptible, vaccinated, exposed, infected, early-treated, delayed-treated, hospitalized, and recovered individuals. We use the Next Generation Matrix (NGN) approach to obtain the basic reproduction number ( R 0 ) and examine local stability at the disease-free equilibrium (DFE). Sensitivity analysis with Partial Rank Correlation Coefficients (PRCC) identifies significant parameters influencing disease dynamics, such as vaccination rates, transmission rate, treatment timings, and disease-induced mortality rates. Simulation results show that delayed therapy has a limited effect on lowering the infected population, emphasizing the importance of immediate intervention. Early treatment considerably reduces the number of infected individuals, whereas improved recovery rates in hospitalized cases result in fewer hospitalizations. Vaccination is extremely successful, with increased rates significantly lowering the susceptible population while boosting the vaccinated population. Higher disease-related mortality rates reduce the afflicted population, stressing the importance of strong control methods. The transmission rate has a substantial impact on infection rates and hospitalizations, emphasizing the need for effective public health policies and healthcare capacity. The combined effect of immunization and early treatment provides useful information for optimizing control measures. This study emphasizes the need of quick and effective measures in managing measles outbreaks and serves as a platform for future research into improved public health methods.

In this paper, we develop a non-autonomous delay differential equation model for mosquito population suppression. After establishing the positiveness and boundedness of the solutions, we study the dynamical behaviours of the model with or without Wolbachia-infected male mosquitoes. More specifically, for the model without infected male mosquitoes, we analyse the asymptotic stability of the equilibria and demonstrate that the model undergo Hopf bifurcations under certain conditions. For the model incorporating infected male mosquitoes, we derive sufficient conditions for the global asymptotic stability of the origin. Numerical examples are provided to illustrate and support our theoretical findings.

ABSTRACT Throughout 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 R 0 , and provided a complete analysis of the bifurcation at R 0 = 1 . 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%.

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 (T0). 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 T0=1, leading to the existence, or not, of an endemic equilibrium even when T0<1. Finally, numerical simulations are employed to illustrate these theoretical findings.

The work of Fred Brauer (1932–2021) broke new ground in several areas of mathematical population biology, especially mathematical epidemiology and population management. This special issue reflects his legacy: the lines of inquiry he opened, the impact of his research and his books, and his mentoring of generations of young researchers. This dedication highlights milestones in his career and connects his work to the contributions in this issue.

In volume transmission (or neuromodulation) neurons do not make one-to-one connections to other neurons, but instead simply release neurotransmitter into the extracellular space from numerous varicosities. Many well-known neurotransmitters including serotonin (5HT), dopamine (DA), histamine (HA), Gamma-Aminobutyric Acid (GABA) and acetylcholine (ACh) participate in volume transmission. Typically, the cell bodies are in one volume and the axons project to a distant volume in the brain releasing the neurotransmitter there. We introduce volume transmission and describe mathematically two natural homeostatic mechanisms. In some brain regions several neurotransmitters in the extracellular space affect each other's release. We investigate the dynamics created by this comodulation in two different cases: serotonin and histamine; and the comodulation of 4 neurotransmitters in the striatum and we compare to experimental data. This kind of comodulation poses new dynamical questions as well as the question of how these biochemical networks influence the electrophysiological networks in the brain.

This paper aims to apply an optimal control theory for the autonomous model of the leptospirosis epidemic to examine the effect of four time-dependent control measures on the model dynamics with cost-effectiveness. Pontryagin's Maximum Principle was used to derive the optimality system associated with the optimal control problem. Numerical simulations of the optimality system were performed for different control strategies and the results were presented graphically with and without controls. The optimality system was simulated using the Forward–Backward Sweep method in the Matlab programme. The numerical results revealed that the combination of all optimal control measures is the most effective strategy for minimizing the spread and impact of disease in the community. Furthermore, a cost-effectiveness analysis was performed to determine the most cost-effective strategy using the incremental cost-effectiveness ratio approach and we observed that the rodenticide control-only strategy is most effective to combat the spread of disease when available resources are limited.