Modelling funding-driven dynamics of measles transmission and control in Malawi

Abstract

Measles remains a persistent public health challenge in many low- and middle-income countries, where vaccination coverage is often limited by constrained and unpredictable funding. This study develops a novel compartmental model to examine the dynamic interaction between donor funding, vaccination coverage, and measles transmission in Malawi. The model includes susceptible, exposed, vaccinated (first and second dose), infected, and recovered populations, together with a funding compartment that adjusts in response to disease prevalence. Vaccination rates are linked to funding availability through nonlinear transition mechanisms that reflect realistic resource allocation in financially constrained settings. We establish the positivity and boundedness of solutions, characterise the disease-free and endemic equilibria, and derive the basic reproduction number \((R_0)\) using the next generation matrix method, with funding explicitly incorporated into the infected subsystem. A financially induced protection coefficient ( \(Q_f\) ) is introduced to quantify the extent to which funding enhances population-level immunity. Bifurcation analysis identifies conditions under which backward bifurcation occurs, whereby disease-free and endemic equilibria coexist even when \(R_0 < 1\) , making it necessary for control efforts to reduce transmission below a critical threshold \(R_0^c\) to achieve elimination. A global sensitivity analysis reveals that factors linked to funding exert strong negative influences on \(R_0\) , while transmission-related factors drive positive correlations. Numerical simulations calibrated to Malawian vaccination data from 2000 to 2024 show that stronger funding responsiveness accelerates disease decline and that the efficiency of second-dose vaccination plays a decisive role in successful elimination. This funding-driven framework provides a quantitative basis for optimising resource allocation under financial constraints and offers actionable insights for evidence-based policymaking in measles elimination programmes. The approach is broadly applicable to other vaccine-preventable diseases and represents a significant step forward in integrating economic realities into epidemiological modelling.