Estimation of SARS-CoV-2 fitness gains from genomic surveillance data without prior lineage classification (preprint)

The emergence of SARS-CoV-2 variants with increased fitness has had a strong impact on the epidemiology of COVID-19, with the higher effective reproduction number of the viral variants leading to new epidemic waves. Tracking such variants and their genetic signatures, using data collected through genomic surveillance, is therefore crucial for forecasting likely surges in incidence. Current methods of estimating fitness advantages of variants rely on tracking the changing proportion of a particular lineage over time, but describing successful lineages in a rapidly evolving viral population is a difficult task. We propose a new method of estimating fitness gains directly from nucleotide information generated by genomic surveillance, without a-priori assigning isolates to lineages from phylogenies, based solely on the abundance of Single Nucleotide Polymorphisms (SNPs). The method is based on mapping changes in the genetic population structure over time. Changes in the abundance of SNPs associated with periods of increasing fitness allow for the unbiased discovery of new variants, and thereby obviating a deliberate lineage assignment and phylogenetic inference. We conclude that the method provides a fast and reliable way to estimate fitness advantages of variants without the need for a-priori assigning isolates to lineages.

Forecasting local hospital bed demand for COVID-19 using on-request simulations (preprint)

For hospitals, realistic forecasting of bed demand during impending epidemics of infectious diseases is essential to avoid being overwhelmed by a potential sudden increase in the number of admitted patients. Short-term forecasting can aid hospitals in adjusting their planning and freeing up beds in time. We created an easy-to-use online on-request tool based on local data to forecast COVID-19 bed demand for individual hospitals. The tool is flexible and adaptable to different settings. It is based on a stochastic compartmental model for estimating the epidemic dynamics and coupled with an exponential smoothing model for forecasting. The models are written in R and Julia and implemented as an R-shiny dashboard. The model is parameterized using COVID-19 incidence, vaccination, and bed occupancy data at customizable geographical resolutions, loaded from official online sources or uploaded manually. Users can select their hospital's catchment area and adjust the number of COVID-19 occupied beds at the start of the simulation. The tool provides short-term forecasts of disease incidence and past and forecasted estimation of the epidemic reproductive number at the chosen geographical level. These quantities are then used to estimate the bed occupancy in both general wards and intensive care unit beds. The platform has proven efficient, providing results within seconds while coping with many concurrent users. By providing ad-hoc, local data informed forecasts, this platform allows decision-makers to evaluate realistic scenarios for allocating scarce resources, such as ICU beds, at various geographic levels.