@article{fdi:010093167,
  title = {{R}eal-time forecasting of {COVID}-19-related hospital strain in {F}rance using a non-{M}arkovian mechanistic model},
  author = {{M}assey, {A}. and {B}oennec, {C}. and {R}estrepo {O}rtiz, {C}laudia and {B}lanchet, {C}. and {A}lizon, {S}. and {S}ofonea, {M}.{T}.},
  editor = {},
  language = {{ENG}},
  abstract = {{P}rojects such as the {E}uropean {C}ovid-19 {F}orecast {H}ub publish forecasts on the national level for new deaths, new cases, and hospital admissions, but not direct measurements of hospital strain like critical care bed occupancy at the sub-national level, which is of particular interest to health professionals for planning purposes. {W}e present a sub-national {F}rench framework for forecasting hospital strain based on a non-{M}arkovian compartmental model, its associated online visualisation tool and a retrospective evaluation of the real-time forecasts it provided from {J}anuary to {D}ecember 2021 by comparing to three baselines derived from standard statistical forecasting methods (a naive model, auto-regression, and an ensemble of exponential smoothing and {ARIMA}). {I}n terms of median absolute error for forecasting critical care unit occupancy at the two-week horizon, our model only outperformed the naive baseline for 4 out of 14 geographical units and underperformed compared to the ensemble baseline for 5 of them at the 90% confidence level (n = 38). {H}owever, for the same level at the 4 week horizon, our model was never statistically outperformed for any unit despite outperforming the baselines 10 times spanning 7 out of 14 geographical units. {T}his implies modest forecasting utility for longer horizons which may justify the application of non-{M}arkovian compartmental models in the context of hospital-strain surveillance for future pandemics.},
  keywords = {{FRANCE}},
  booktitle = {},
  journal = {{P}lo{S} {C}omputational {B}iology},
  volume = {20},
  numero = {5},
  pages = {e1012124 [22 ]},
  ISSN = {1553-7358},
  year = {2024},
  DOI = {10.1371/journal.pcbi.1012124},
  URL = {https://www.documentation.ird.fr/hor/fdi:010093167},
}