Predicting measles elimination through surveillance-response and health system determinants: a village-level structural equation modeling study in Cirebon Regency, Indonesia

Background Measles elimination remains a major public health challenge despite the widespread availability of effective vaccines. The rising outbreaks in some countries suggest that high immunization coverage alone may not be enough to stop transmission. However, evidence remains limited regarding how surveillance-response, health-system capacity, nutritional status, and community vulnerability interact to influence measles elimination within a single epidemiological framework. Objective This study aimed to develop and validate an integrated epidemiological prediction model for measles elimination using Structural Equation Modeling–Partial Least Squares (SEM-PLS) at the village level in Cirebon Regency, Indonesia. Methods A cross-sectional study was conducted using secondary data from 424 village administrative units in Cirebon Regency during 2022–2024. Variables included measles immunization coverage, nutritional status, village vulnerability, health resources, surveillance-response performance, suspected measles cases, confirmed measles cases, and transmission incidences. Data were analyzed using Structural Equation Modeling–Partial Least Squares (SEM-PLS) to estimate the direct and indirect pathways linking surveillance-response, immunization, nutritional status, health-system capacity, community vulnerability, and transmission to measles elimination. Results The SEM-PLS model explained 56.9% of the variance in measles elimination. Surveillance-response performance emerged as the principal mediating mechanism linking health-system capacity with reductions in suspected cases, confirmed cases, and transmission. Measles elimination was significantly associated with confirmed cases (p<0.001), suspected cases (p<0.001), immunization coverage (p=0.002), nutritional status (p=0.003), village vulnerability (p=0.001), health resources (p=0.001), and surveillance-response performance (p<0.001). These findings indicate that surveillance-response contributes substantially to elimination through early case detection and interruption of transmission pathways. Conclusions Measles elimination should be viewed not solely as a vaccination outcome but as the product of an integrated epidemiological system in which surveillance-response, health-system capacity, nutritional status, and community vulnerability interact to interrupt transmission. The proposed predictive model provides a practical framework for evidence-based elimination planning and more adaptive, evidence-informed measles elimination policies in endemic settings.