Estimating severity from multiple data sources using Bayesian evidence synthesis

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As we prepare for a possible COVID-19 pandemic, understanding the severity of theepidemic, i.e. the proportion of infections that result in a severe event such ashospitalisation or death, is crucial to monitoring and predicting the burden of the epidemicon healthcare services. This burden is measured by the number of people infected whorequire primary care from general practitioners, hospital admission, respiratory supportand/or admission to intensive care. Severity is quantified by infection-severity and caseseverityrisks, namely the probability that an infection (whether with or without symptoms)or a symptomatic infection (clinical case) leads to a severe event. These quantities arechallenging to observe directly from a single dataset, as it is not possible to detect andfollow-up every case in a population, particularly early in the epidemic when case countsmay miss many asymptomatic or mild cases. The problem is compounded by the fact thatfor patients still ill in hospital, we have not yet had time to observe whether they willrecover or not. We propose combining information from multiple datasets, both onindividuals and aggregated counts, to estimate severity while accounting for thechallenges of missing cases and not yet observing outcomes.