Hierarchical latent class models for mortality surveillance using partially verified verbal autopsies

Monitoring cause-of-death data is an important part of understanding disease burdens and effects of public health interventions. Verbal autopsy (VA) is a well-established method for gathering information about deaths outside of hospitals by conducting an interview to caregivers of a deceased person. It is usually the only tool for cause-of-death surveillance in low-resource settings. A critical limitation with current practices of VA analysis is that all algorithms require either domain knowledge about symptom-cause relationships or large labelled datasets for model training. Therefore, they cannot be easily adopted during public health emergencies when new diseases emerge with rapidly evolving epidemiological patterns. In this paper, we consider estimating the fraction of deaths due to an emerging disease. We develop a novel Bayesian framework using hierarchical latent class models to account for the informative cause-of-death verification process. Our model flexibly captures the joint distribution of symptoms and how they change over time in different sub-populations. We also propose structured priors to improve the precision of the cause-specific mortality estimates for small sub-populations. Our model is motivated by mortality surveillance of COVID-19 related deaths in low-resource settings. We apply our method to a dataset that includes suspected COVID-19 related deaths in Brazil in 2021.