Risk of paediatric multisystem inflammatory syndrome (PIMS-TS) during the SARS-CoV-2 alpha and delta variant waves: National observational and modelling study, 2020-21, England.

Objectives: Paediatric Multisystem Inflammatory Syndrome (PIMS-TS) is a rare life-threatening complication that typically occurs several weeks after SARS-CoV-2 infection in children and young people (CYP). We used national and regional-level data from the COVID-19 pandemic waves in England to develop a model to predict PIMS-TS cases. Methods: SARS-CoV-2 infections in CYP aged 0-15 years in England were estimated using the PHE-Cambridge real-time model. PIMS-TS cases were identified through the British Paediatric Surveillance Unit during (March-June 2020) and through Secondary Uses Services (SUS) from November 2020. A predictive model was developed to estimate PIMS-TS risk and lag times after SARS-CoV-2 infections. Results: During the Alpha wave, the model accurately predicted PIMS-TS cases (506 vs. 502 observed cases), with a median estimated risk of 0.038% (IQR, 0.037-0.041%) of paediatric SARS-CoV-2 infections. For the Delta wave, the median risk of PIMS-TS was significantly lower at 0.026% (IQR, 0.025-0.029%), with 212 observed PIMS-TS cases compared to 450 predicted by the model. Conclusions: The model accurately predicted national and regional PIMS-TS cases in CYP during the Alpha wave. PIMS-TS cases were 53% lower than predicted during the Delta wave. Further studies are needed to understand the mechanisms of the observed lower risk with the Delta variant.

Methods for calculating credible intervals for ratios of beta distributions with application to relative risks of death during the second plague pandemic.

Employing historical records we are able to estimate the risk of premature death during the second plague pandemic, and identify the Black Death and pestis secunda epidemics. We show a novel method of calculating Bayesian credible intervals for a ratio of beta distributed random variables and use this to quantify uncertainty of relative risk estimates for these two epidemics which we consider in a 2 × 2 contingency table framework.

EpiJSON: A unified data-format for epidemiology.

Epidemiology relies on data but the divergent ways data are recorded and transferred, both within and between outbreaks, and the expanding range of data-types are creating an increasingly complex problem for the discipline. There is a need for a consistent, interpretable and precise way to transfer data while maintaining its fidelity. We introduce 'EpiJSON', a new, flexible, and standards-compliant format for the interchange of epidemiological data using JavaScript Object Notation. This format is designed to enable the widest range of epidemiological data to be unambiguously held and transferred between people, software and institutions. In this paper, we provide a full description of the format and a discussion of the design decisions made. We introduce a schema enabling automatic checks of the validity of data stored as EpiJSON, which can serve as a basis for the development of additional tools. In addition, we also present the R package 'repijson' which provides conversion tools between this format, line-list data and pre-existing analysis tools. An example is given to illustrate how EpiJSON can be used to store line list data. EpiJSON, designed around modern standards for interchange of information on the internet, is simple to implement, read and check. As such, it provides an ideal new standard for epidemiological, and other, data transfer to the fast-growing open-source platform for the analysis of disease outbreaks.