A Bayesian Spatiotemporal Model to Estimate Disease Burden Using Hospital-Based Active Surveillance
Passive surveillance systems, in which data routinely collected by medical facilities are used to monitor the caseload of infectious diseases, are relatively straightforward to implement but often result in underestimation of the burden of disease due to under-diagnosis and imperfect testing. Targeted active surveillance can be used to correct these case counts to better reflect the true burden of disease. However, when the active surveillance effort is performed at a subset of hospitals and passive surveillance data is reported at an aggregated regional level, the resulting spatial misalignment must be reconciled to estimate the true rate of hospital-presenting disease at the spatial region level. Motivated by a recent active surveillance project for leptospirosis in four Puerto Rican hospitals, we address this challenge and develop a novel Bayesian spatio-temporal framework to better reflect the true number of hospital-presenting individuals with the disease. In particular, our method extends the Poisson-logistic framework to incorporate spatial heterogeneity in the probability of presenting to the hospitals across the study region. Our framework also accounts for imperfect diagnostic testing within the active surveillance data, addressing a common challenge for infectious diseases, particularly for neglected ones like leptospirosis. The model is assessed via simulation under various scenarios and then applied to the motivating leptospirosis data. Our approach offers a comprehensive framework for integrating spatially misaligned passive and active surveillance data, enabling better estimation of true disease burden.