Observation-masked neural posterior estimation for heterogeneous epidemiological surveillance data

Abstract

Disease surveillance data are often sparse, irregularly timed and heterogeneous across observational units, creating challenges for inference in mechanistic epidemiological models. We present observation-masked neural posterior estimation (OM-NPE), a simulation-based Bayesian inference framework for such settings. The approach represents observations on a common temporal grid and records observation availability through a binary mask, enabling heterogeneous surveillance records to be analysed using a single amortised neural posterior estimator. We demonstrate OM-NPE using two contrasting epidemiological systems. First, we fit an effective SEIR model of Zika virus transmission to weekly sentinel surveillance data from six French Polynesian archipelagos during the 2013-14 outbreak. Second, we fit a stochastic compartmental model of Xylella fastidiosa spread to annual disease-severity observations from 17 olive groves in Apulia, Italy, each surveyed only two or three times over seven years. In both applications, OM-NPE recovered epidemiological parameters consistent with the assumed epidemiological and observation models, appropriately represented uncertainty, and generated posterior distributions in well under one second without retraining or Markov chain Monte Carlo sampling. Observation masking extends amortised Bayesian inference to heterogeneous longitudinal surveillance systems and supports rapid evaluation of alternative monitoring schedules.