A Data‐Driven Mathematical Model of Measles Disease Transmission in Nigeria: Incorporating Context‐Dependent Nonlinear Functions

ABSTRACT This study develops a novel extension of the classical MSVIR (maternal immunity–susceptible–vaccinated–infected–recovered) compartmental framework to a more realistic and context‐sensitive MSVHIRD (maternal immunity–susceptible–vaccinated–hospitalized–infected–recovered–dead) model. Some of the epidemiological parameters are modeled as dynamic, context‐dependent functions such as maternal malnutrition levels, waning vaccine‐induced immunity, antibody decay rate and the general infection rate which incorporate seasonal oscillations and crowding indices to reflect environmental realities such as dry season stress and urban slum density. We employed standard theorems to analyze the model's boundedness, basic reproduction number and stability analysis. Using real data of measles incidence obtained from our world in data, https://ourworldindata.org/ , we use the solve_ivp function from the scipy.integrate module via the Python computational software to perform model fit for realistic measles outbreaks in Nigeria and for evaluating the effectiveness of interventions under varying levels of vaccine hesitancy, malnutrition, and crowding. By aligning the mathematical structure with data, the study presents policymakers a powerful tool for predicting outbreaks, tailoring and implementing effective control strategies to mitigate measles disease in Nigeria.