Optimal control analysis to reduce the health complexity of co-infection with COVID-19 and kidney disease.

The COVID-19 pandemic remains a serious health risk, especially with diseases like kidney disease. There is no information in the literature on co-infection of kidney disease with COVID-19. Therefore, the current study introduces a deterministic mathematical model to explore the co-infection dynamics between COVID-19 and kidney disease, intending to offer insightful guidance for effective control strategies. The findings indicate that individuals with kidney disease are at an increased risk of severe complications from COVID-19, while COVID-19 can exacerbate kidney disease symptoms, creating a complex health scenario. To mitigate these risks, we propose and rigorously analyze three control measures using Pontryagin's Maximum Principle. Three controls [Formula: see text] and [Formula: see text], focus on public health education for COVID-19, promote a healthy lifestyle to prevent kidney disease, and provide specialized treatment for co-infected patients, respectively. The model's parameters are adjusted to align with collected epidemiological data using a hybrid approach that combines Bayesian and least square estimation methods. Our results are validated in the existing literature to identify the most effective control strategies. The study highlights the necessity of integrated healthcare strategies in managing the intricate relationship between COVID-19 and kidney disease. The results indicate that co-infection prevalence decreases when all three control measures are implemented simultaneously. Studying this potential control model is instrumental in optimizing control strategies, thereby significantly reducing the health complexities associated with co-infection of COVID-19 and kidney disease. This approach utilizes simulation to its maximum benefit, aiming to simplify the global health challenges posed by these conditions.