Nowcasting the 2022 mpox outbreak in England

TL;DR

This paper introduces a flexible nowcasting model using generalized additive models to accurately estimate the real-time trajectory of the 2022 mpox outbreak in England, accounting for reporting delays and data backfill.

Contribution

The paper presents a novel, adaptable nowcasting approach that incorporates temporal reporting changes, improving real-time epidemic assessment for mpox in England.

Findings

Accurately estimated the epidemic curve despite reporting delays.

Provided real-time growth rate estimates of the mpox outbreak.

Enhanced epidemic monitoring through flexible modeling.

Abstract

In May 2022, a cluster of mpox cases were detected in the UK that could not be traced to recent travel history from an endemic region. Over the coming months, the outbreak grew, with over 3000 total cases reported in the UK, and similar outbreaks occurring worldwide. These outbreaks appeared linked to sexual contact networks between gay, bisexual and other men who have sex with men. Following the COVID-19 pandemic, local health systems were strained, and therefore effective surveillance for mpox was essential for managing public health policy. However, the mpox outbreak in the UK was characterised by substantial delays in the reporting of the symptom onset date and specimen collection date for confirmed positive cases. These delays led to substantial backfilling in the epidemic curve, making it challenging to interpret the epidemic trajectory in real-time. Many nowcasting models exist to tackle this challenge in epidemiological data, but these lacked sufficient flexibility. We have developed a novel nowcasting model using generalised additive models to correct the mpox epidemic curve in England, and provide real-time characteristics of the state of the epidemic, including the real-time growth rate. This model benefited from close collaboration with individuals involved in collecting and processing the data, enabling temporal changes in the reporting structure to be built into the model, which improved the robustness of the nowcasts generated.