Understanding the leading indicators of hospital admissions from COVID-19 across successive waves in the UK

Drivers of epidemic dynamics in real time from daily digital COVID-19 measurements

Understanding the drivers of respiratory pathogen spread is challenging, particularly in a timely manner during an ongoing epidemic. In this work, we present insights that we obtained using daily data from the National Health Service COVID-19 app for England and Wales and that we shared with health authorities in almost real time. Our indicator of the reproduction number R(t) was available days earlier than other estimates, with an innovative capability to decompose R(t) into contact rates and probabilities of infection. When Omicron arrived, the main epidemic driver switched from contacts to transmissibility. We separated contacts and transmissions by day of exposure and setting and found pronounced variability over days of the week and during Christmas holidays and events. For example, during the Euro football tournament in 2021, days with England matches showed sharp spikes in exposures and transmissibility. Digital contact-tracing technologies can help control epidemics not only by directly preventing transmissions but also by enabling rapid analysis at scale and with unprecedented resolution.

The role of viral interference in shaping RSV epidemics following the 2009 H1N1 influenza pandemic

Respiratory syncytial virus (RSV) primarily affects infants, young children, and older adults, with seasonal outbreaks in the United States (US) peaking around December or January. Despite the limited implementation of non-pharmaceutical interventions, disrupted RSV activity was observed in different countries following the 2009 influenza pandemic, suggesting possible viral interference from influenza. Although interactions between the influenza A/H1N1 pandemic virus and RSV have been demonstrated at an individual level, it remains unclear whether the disruption of RSV activity at the population level can be attributed to viral interference. In this work, we first evaluated changes in the timing and intensity of RSV activity across 10 regions of the US in the years following the 2009 influenza pandemic using dynamic time warping. We observed a reduction in RSV activity following the pandemic, which was associated with intensity of influenza activity in the region. We then developed an age-stratified, two-pathogen model to examine various hypotheses regarding viral interference mechanisms. Based on our model estimates, we identified three mechanisms through which influenza infections could interfere with RSV: 1) reducing susceptibility to RSV coinfection; 2) shortening the RSV infectious period in coinfected individuals; and 3) reducing RSV infectivity in coinfection. Our study offers statistical support for the occurrence of atypical RSV seasons following the 2009 influenza pandemic. Our work also offers new insights into the mechanisms of viral interference that contribute to disruptions in RSV epidemics and provides a model-fitting framework that enables the analysis of new surveillance data for studying viral interference at the population level.

Forecasting influenza hospital admissions within English sub-regions using hierarchical generalised additive models

BACKGROUND

Seasonal influenza places a substantial burden annually on healthcare services. Policies during the COVID-19 pandemic limited the transmission of seasonal influenza, making the timing and magnitude of a potential resurgence difficult to ascertain and its impact important to forecast.

METHODS

We have developed a hierarchical generalised additive model (GAM) for the short-term forecasting of hospital admissions with a positive test for the influenza virus sub-regionally across England. The model incorporates a multi-level structure of spatio-temporal splines, weekly cycles in admissions, and spatial correlation. Using multiple performance metrics including interval score, coverage, bias, and median absolute error, the predictive performance is evaluated for the 2022-2023 seasonal wave. Performance is measured against autoregressive integrated moving average (ARIMA) and Prophet time series models.

RESULTS

Across the epidemic phases the hierarchical GAM shows improved performance, at all geographic scales relative to the ARIMA and Prophet models. Temporally, the hierarchical GAM has overall an improved performance at 7 and 14 day time horizons. The performance of the GAM is most sensitive to the flexibility of the smoothing function that measures the national epidemic trend.

CONCLUSIONS

This study introduces an approach to short-term forecasting of hospital admissions for the influenza virus using hierarchical, spatial, and temporal components. The methodology was designed for the real time forecasting of epidemics. This modelling framework was used across the 2022-2023 winter for healthcare operational planning by the UK Health Security Agency and the National Health Service in England.