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Measles is a significant source of global disease burden and child mortality.Measles vaccination through routine immunization (RI) programs in high-burdensettings remains a challenge due to poor health care infrastructure and access.Supplementary immunization activities (SIA) in the form of vaccinationcampaigns are therefore implemented to prevent measles outbreaks by reducingthe size of the susceptible population. The SIA efficacy, defined as thefraction of susceptible population immunized by an SIA, is a critical metricfor assessing campaign effectiveness. We propose a discrete-time hidden Markovmodel for estimating SIA efficacy and forecasting future incidence trends usingreported measles incidence data. Our approach extends the time-seriessusceptible-infected-recovered (TSIR) framework by adding a model component tocapture the impact of SIAs on the susceptible population. It also accounts forunder-reporting and its associated uncertainty via a two-stage estimationprocedure with uncertainty propagation. The proposed model can be used toestimate the underlying susceptible population dynamics, assess how manysusceptible people were immunized by past SIAs, and forecast incidence trendsin the future under various hypothetical SIA scenarios. We examine modelperformance via simulations under various levels of under-reporting, and applythe model to analyze monthly reported measles incidence in Benin from 2012 to2018.

Estimating efficacy of measles supplementary immunization activities via discrete-time modeling of disease incidence time series