Spatio-temporal methods to handle missing data in syndromic surveillance with applications to health management information system data

Syndromic surveillance monitors infectious diseases, especially in situations where direct disease monitoring is unavailable. However, conventional syndromic surveillance methods face challenges in handling missing data, particularly when the missing completely at random (MCAR) assumption is violated. Additionally, these methods often do not leverage spatio-temporal techniques that can reduce bias and improve their performance. This study addresses both of these limitations by comparing a baseline syndromic surveillance model with a frequentist spatio-temporal model used in infectious diseases and a Bayesian spatio-temporal conditional autoregressive (CAR) model.

Drawing inspiration from COVID-19 symptom data collected via routine health systems in Liberia, we conduct simulations with various data generating processes, spatio-temporal correlation structures, and missing data mechanisms. Across the diverse simulations for outbreak detection, the baseline model and the Bayesian CAR model had high specificity, thus limiting outbreak false alarms. The findings underscore the importance of considering spatio-temporal models for syndromic surveillance.