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The time required to identify and confirm risk factors for new diseases and to design an appropriate treatment strategy is one of the most significant obstacles medical professionals face. Traditionally, this approach entails several clinical studies that may last several years, during which time strict preventative measures must be in place to contain the epidemic and limit the number of fatalities. Analytical tools may be used to direct and accelerate this process. This study introduces a six-state compartmental model to explain and assess the impact of age demographics by designing a dynamic, explainable analytics model of the SARS-CoV-2 coronavirus. An age-stratified mathematical model taking the form of a deterministic system of ordinary differential equations divides the population into different age groups to better understand and assess the impact of age on mortality. It also provides a more accurate and effective interpretation of the disease evolution, specifically in terms of the cumulative numbers of infected cases and deaths. The proposed Kermack-Mckendrick model is incorporated into a non-linear least-squares optimization curve-fitting problem whose optimized parameters are numerically obtained using the Levenberg-Marquard algorithm. The curve-fitting model's efficiency is proved by testing the age-stratified model's performance on three U.S. states: Connecticut, North Dakota, and South Dakota. Our results confirm that splitting the population into different age groups leads to better fitting and forecasting results overall as compared to those achieved by the traditional method, i.e., without age groups. By using comprehensive models that account for age, gender, and ethnicity, regional public health authorities may be able to avoid future epidemics from inflicting more fatalities and establish a public health policy that reduces the burden on the elderly population. [ABSTRACT FROM AUTHOR]

This note presents a novel chemotherapy protocol for physicians to treat cancer tumors. Mathematical modeling, analysis, and simulations are used to describe the detailed dynamics of tumor, effector-immune cells, lymphocyte population, and chemotherapy drug, inside the patient body. An optimized scheduling alternating between treatment and relaxation sessions is determined to minimize the tumor size at the end of therapy period and overcome the toxicity level of patient's organs. To this end, we propose and allot relaxation sessions between two consecutive treatment sessions so that the body can partially recover. For each treatment period, we determine an optimal control strategy to minimize the tumor size and drug consumption without negatively affecting the natural cells. Finally, a particle swarm optimization-based approach is developed in order to ascertain the duration of each therapy session. The obtained results show that the proposed solution presents significant advantages in drug dosage, tumor reduction, and chemotherapy scheduling sessions compared to mathematical-based state-of-art approaches. [ABSTRACT FROM AUTHOR]