Estimating severity from multiple data sources using Bayesian evidence synthesis

This COVID-19 Rapid Response award is jointly funded (50:50) between the Medical Research Council and the National Institute for Health Research. The figure displayed is the total award amount of the two funders combined, with each partner contributing equally towards the project. In preparing for a possible COVID-19 pandemic, estimates of severity, in particular of both the numbers of infections occurring at different levels of severity and the infection- and case-severity risks, are crucial to understand and predict the burden and impact of the epidemic on healthcare services. Such estimates are most importantly needed by age and risk group (e.g. defined by co-morbidities) strata, although in the early stages of an epidemic, strata-specific information is rarely available. No single dataset can provide enough information on its own to estimate severity, but estimation is feasible by synthesising multiple datasets, such as: line-listing data from first few hundred type studies; surveillance data including case counts, numbers accessing healthcare, and numbers of deaths; cohort studies; and household studies. Such a synthesis needs to account for biases inherent in each data source, including differential ascertainment by severity level; and to account for the incomplete nature of the data, which, collected in real time, are typically affected by censoring of final outcomes (recovery/hospital discharge or death). We propose to make the best use of both individual- and aggregate-level data that will become available, by using a combination of survival analysis techniques (e.g. curerate mixture or competing risks models) and Bayesian evidence synthesis in a single analysis to estimate severity in real time, as data accumulate over the course of the epidemic, and once the epidemic is over.