Winter forecasting of respiratory viruses in Victoria Australia
Henderson, A. S.; Moss, R.; Adekunle, A. I.; Ye, H.; O'Hara-Wild, M.; Eales, O.; Senior, K. L.; Tobin, R.; Windecker, S. M.; golding, N.; Robinson, E.; Strachan, J.; Hyndman, R. J.; Dawson, P.; McCaw, J.; McBryde, E.; Shearer, F. M.
Show abstract
Temperate regions of the world, such as southern Australia, often experience increased health burden from respiratory pathogens during winter. The ability to forecast short-term trends in cases of these pathogens is of significant interest to public health. Across the 2024 southern hemisphere winter period, the Australia--Aotearoa Consortium for Epidemic Forecasting and Analytics (ACEFA) ran a pilot respiratory virus forecasting initiative in collaboration with the Victorian Department of Health. Each week from the 9th of May 2024 through to 12th September 2024, the consortium solicited 28-day forecasts of daily case incidence for influenza, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and respiratory syncytial virus (RSV) from multiple research groups. Four component model forecasts were contributed by three different research groups, with a fourth group utilising the component forecasts to generate ensemble forecasts (making a total of six models, four component models and two ensembles). Here we statistically evaluated the performance of each forecast and a baseline model against the observed case data. The two ensemble models were found to be frequently the top performing models. All models performed worse than the baseline model around the epidemic peaks for each pathogen.
Matching journals
The top 2 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- An ensemble n -sub-epidemic modeling framework for short-term forecasting epidemic trajectories: Application to the COVID-19 pandemic in the USA 96%
- Fast and Accurate Influenza Forecasting in the United States with Inferno 96%
- Improving Probabilistic Infectious Disease Forecasting Through Coherence 95%
Similar papers in this journal
- A prospective real-time transfer learning approach to estimate Influenza hospitalizations with limited data 97%
- Assessing the utility of COVID-19 case reports as a leading indicator for hospitalization forecasting in the United States 96%
- Foundation time series models for forecasting and policy evaluation in infectious disease epidemics 95%
Similar papers in this journal
- A Stacked ensemble method for forecasting influenza-like illness visit volumes at emergency departments 97%
- On the use of growth models for forecasting epidemic outbreaks with application to COVID-19 data 94%
- A Bayesian Susceptible-Infectious-Hospitalized-Ventilated-Recovered Model to Predict Demand for COVID-19 Inpatient Care in a Large Healthcare System 94%
Similar papers in this journal
- A semi-parametric, state-space compartmental model with time-dependent parameters for forecasting COVID-19 cases, hospitalizations, and deaths 96%
- Border quarantine, vaccination and public health measures to mitigate the impact of COVID-19 importations: a modelling study 93%
- Introducing a framework for within-host dynamics and mutations modelling of H5N1 influenza infection in humans 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.