Assessing the dynamics of Lassa fever with impact of environmental sanitation: optimal control and cost-effectiveness analysis

Fractional-order modeling of human behavior in infections: analysis using real data from Liberia

This paper presents a fractional-order model using the Caputo differential operator to study Ebola Virus Disease (EVD) dynamics, calibrated with Liberian data. The model demonstrates improved accuracy over integer-order counterparts, particularly in capturing behavioral changes during outbreaks. Stability analysis, Lyapunov functions, and a validated numerical method strengthen its mathematical foundation. Simulations highlight its utility in accurately describing EVD evolution and guiding outbreak management. The study underscores the role of behavioral interventions in epidemic control, offering valuable insights for public health and policymaking. This research advances infectious disease models and enhances strategies for mitigating EVD outbreaks.

The dynamics of tuberculosis transmission model with different genders

The dynamics of tuberculosis transmission model with different genders are to be established and studied. The basic regeneration numbers R 0 = R F + R M are to be defined, where R F and R M to be the basic reproduction number of tuberculosis transmission in female and male populations, respectively. The existence and global stability of the disease-free equilibrium was discussed when R 0 < 1 . The global dynamic behaviours of the corresponding limit system under some conditions are to be provided, including the existence, uniqueness, and global stability of the disease-free equilibrium and endemic equilibrium. The numerical simulation shows that the endemic equilibrium may be unique and stable when R 0 > 1 , and the system will undergo Hopf bifurcation based on some parameter values. Finally, we applied this model to analyse the transmission of tuberculosis in China, estimated the incidence of tuberculosis in China in 2035, and gave the conclusion that controlling the incidence of tuberculosis in male populations could better reduce the incidence of tuberculosis in China.

A systematic review of mathematical models of Lassa fever

This systematic review, conducted following the PRISMA guidelines, scrutinizes mathematical models employed in the study of Lassa fever. The analysis revealed the inherent heterogeneity in both models and data, posing significant challenges to parameter estimation. While health and behavioral interventions exhibit promise in mitigating the disease's spread, their efficacy is contingent upon contextual factors. Identified through this review are critical gaps, limitations, and avenues for future research, necessitating increased harmonization and standardization in modeling approaches. The considerations of seasonal and spatial variations emerge as crucial elements demanding targeted investigation. The perpetual threat of emerging diseases, coupled with the enduring public health impact of Lassa fever, underscores the imperative for sustained research endeavors and investments in mathematical modeling. The conclusion underscored that while mathematical modeling remains an invaluable tool in the combat against Lassa fever, its optimal utilization mandates multidisciplinary collaboration, refined data collection methodologies, and an enriched understanding of the intricate disease dynamics. This comprehensive approach is essential for effectively reducing the burden of Lassa fever and safeguarding the health of vulnerable populations.

Cost-effectiveness analysis of COVID-19 intervention policies using a mathematical model: an optimal control approach

COVID-19 is an infectious disease that causes millions of deaths worldwide, and it is the principal leading cause of morbidity and mortality in all nations. Although the governments of developed and developing countries are enforcing their universal control strategies, more precise and cost-effective single or combination interventions are required to control COVID-19 outbreaks. Using proper optimal control strategies with appropriate cost-effectiveness analysis is important to simulate, examine, and forecast the COVID-19 transmission phase. In this study, we developed a COVID-19 mathematical model and considered two important features including direct link between vaccination and latently population, and practical healthcare cost by separation of infections into Mild and Critical cases. We derived basic reproduction numbers and performed mesh and contour plots to explore the impact of different parameters on COVID-19 dynamics. Our model fitted and calibrated with number of cases of the COVID-19 data in Bangladesh as a case study to determine the optimal combinations of interventions for particular scenarios. We evaluated the cost-effectiveness of varying single and combinations of three intervention strategies, including transmission control, treatment, and vaccination, all within the optimal control framework of the single-intervention policies; enhanced transmission control is the most cost-effective and prompt in declining the COVID-19 cases in Bangladesh. Our finding recommends that a three-intervention strategy that integrates transmission control, treatment, and vaccination is the most cost-effective compared to single and double intervention techniques and potentially reduce the overall infections. Other policies can be implemented to control COVID-19 depending on the accessibility of funds and policymakers' judgments.

A non-linear mathematical model for analysing the impact of COVID-19 disease on higher education in developing countries

This study proposes a non-linear mathematical model for analysing the effect of COVID-19 dynamics on the student population in higher education institutions. The theory of positivity and boundedness of solution is used to investigate the well-posedness of the model. The disease-free equilibrium solution is examined analytically. The next-generation operator method calculates the basic reproduction number ( R 0 ) . Sensitivity analyses are carried out to determine the relative importance of the model parameters in spreading COVID-19. In light of the sensitivity analysis results, the model is further extended to an optimal control problem by introducing four time-dependent control variables: personal protective measures, quarantine (or self-isolation), treatment, and management measures to mitigate the community spread of COVID-19 in the population. Simulations evaluate the effects of different combinations of the control variables in minimizing COVID-19 infection. Moreover, a cost-effectiveness analysis is conducted to ascertain the most effective and least expensive strategy for preventing and controlling the spread of COVID-19 with limited resources in the student population.