Speaker
Description
Spatial analyses in epidemiology often rely on accurate geolocation of individuals to estimate spatially structured health outcomes. However, routinely collected surveillance data frequently lack precise residential coordinates, introducing positional uncertainty that can bias spatial inference. This study examines the impact of uncertainty in patient location on the estimated spatial distribution of COVID-19 vaccination.
Because individual-level residential coordinates were unavailable, we implemented a probabilistic geolocation imputation strategy based on the hierarchical distribution of known administrative units (e.g., neighbourhoods). For each individual, a random spatial location was sampled from a density function defined by population-weighted spatial priors. To propagate uncertainty into the estimation process, the imputed coordinates were integrated within a Bayesian spatial modelling framework using the Stochastic Partial Differential Equation (SPDE) approach [1] implemented through the Integrated Nested Laplace Approximation (INLA) method [2].
We quantified the impact of positional uncertainty by comparing posterior estimates of vaccination odds surfaces under multiple imputation replicates and uncertainty-weighted spatial priors. The results indicate that ignoring location uncertainty leads to spatial over-smoothing and attenuated spatial gradients in vaccination odds, particularly in high-density urban areas. Incorporating imputation uncertainty within the SPDE-INLA framework yielded more conservative and stable posterior estimates, improving spatial risk characterisation while preserving credible interval coverage.
This work highlights the importance of formally accounting for spatial uncertainty in epidemiological modelling when exact geocoding is incomplete or unavailable. The proposed framework provides a generalizable Bayesian approach for integrating positional uncertainty into spatial health models, enhancing the robustness and interpretability of public health spatial analyses.
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