Calibrating COVID-19 susceptible-exposed-infected-removed models with time-varying effectivecontact rates.

We describe the population-based susceptible-exposed-infected-removed (SEIR) model developed by the Irish Epidemiological Modelling Advisory Group (IEMAG), which advises the Irish government on COVID-19 responses. The model assumes a time-varying effective contact rate (equivalently, a time-varying reproduction number) to model the effect of non-pharmaceutical interventions. A crucial technical challenge in applying such models is their accurate calibration to observed data, e.g. to the daily number of confirmed new cases, as the history of the disease strongly affects predictions of future scenarios. We demonstrate an approach based on inversion of the SEIR equations in conjunction with statistical modelling and spline-fitting of the data to produce a robust methodology for calibration of a wide class of models of this type. This article is part of the theme issue 'Data science approaches to infectious disease surveillance'.

Understanding the Challenges and Uncertainties of Seroprevalence Studies for SARS-CoV-2.

SARS-CoV-2 continues to widely circulate in populations globally. Underdetection is acknowledged and is problematic when attempting to capture the true prevalence. Seroprevalence studies, where blood samples from a population sample are tested for SARS-CoV-2 antibodies that react to the SARS-CoV-2 virus, are a common method for estimating the proportion of people previously infected with the virus in a given population. However, obtaining reliable estimates from seroprevalence studies is challenging for a number of reasons, and the uncertainty in the results is often overlooked by scientists, policy makers, and the media. This paper reviews the methodological issues that arise in designing these studies, and the main sources of uncertainty that affect the results. We discuss the choice of study population, recruitment of subjects, uncertainty surrounding the accuracy of antibody tests, and the relationship between antibodies and infection over time. Understanding these issues can help the reader to interpret and critically evaluate the results of seroprevalence studies.