Reconstructing the first COVID-19 pandemic wave with minimal data in the UK

Accurate measurement of exposure to SARS-CoV-2 in the population is crucial for understanding the dynamics of disease transmission and evaluating the impacts of interventions. However, it is particularly challenging to achieve this in the early phase of a pandemic because of the sparsity of epidemiological data. In our previous publication[1], we developed an early pandemic diagnostic tool that can link minimum datasets: seroprevalence, mortality and infection testing data to estimate the true exposure in different regions of England and found levels of SARS-CoV-2 population exposure are considerably higher than suggested by seroprevalence surveys. Here, we re-examined and evaluated the model in the context of reconstructing the first COVID-19 epidemic wave in England from three perspectives: validation from ONS Coronavirus Infection Survey, relationship between model performance and data abundance and time-varying case detection rate. We found that our model can recover the first but unobserved epidemic wave of COVID-19 in England from March 2020 to June 2020 as long as two or three serological measurements are given as model inputs additionally, with the second wave during winter of 2020 validated by the estimates from ONS Coronavirus Infection Survey. Moreover, the model estimated that by the end of October in 2020 the UK government’s official COVID-9 online dashboard reported COVID-19 cases only accounted for 9.1% (95%CrI (8.7%,9.8%)) of cumulative exposure, dramatically varying across two epidemic waves in England in 2020 (4.3% (95%CrI (4.1%, 4.6%)) vs 43.7% (95%CrI (40.7%, 47.3%))).