Spatial individual-level models (ILMs) provide a flexible framework for modeling infectious disease transmission across populations with known locations. Bayesian inference for these models relies on Markov chain Monte Carlo (MCMC), which requires repeated likelihood evaluation and, when parts of the epidemic trajectory are unobserved, data-augmented sampling over high-dimensional latent variables. This computational cost limits the applicability of MCMC to large populations and to settings requiring inference across multiple outbreaks. We propose using neural posterior estimation (NPE) for amortized Bayesian inference in spatial ILMs. NPE trains a conditional normalizing flow on simulated data to approximate the posterior directly, bypassing likelihood evaluation at inference time. We compare two embedding architectures: a convolutional neural network (CNN) operating on the population-level incidence curve and a graph neural network (GNN) operating on individual-level infection and location data. In a simulation study under full observation, stochastic removals, and partial observation, both variants achieve empirical coverage close to the nominal level, with the GNN embedding yielding lower error and narrower credible intervals for the spatial transmission parameters. Data-augmented MCMC is the most accurate when its chains converge, and GNN-NPE is closer to MCMC than CNN-NPE at a small fraction of the per-epidemic cost of MCMC. We apply the framework to the observed 2001 UK foot-and-mouth disease outbreak on 1,177 farms, where MCMC with the exact likelihood provides a reference posterior. Both NPE variants recover the reproduction number estimated by MCMC. GNN-NPE gives a narrower posterior for the spatial decay of transmission than CNN-NPE but is shifted relative to the MCMC reference.

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