A physics-informed neural network that embeds a nine-compartment COVID-19 model produced competitive 1-4 week forecasts for California, outperforming naive and sequence deep learning baselines.
A semi-parametric, state-space compartmental model with time-dependent parameters for forecasting covid-19 cases, hospitalizations and deaths
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
dataset 1
citation-polarity summary
fields
cs.LG 1years
2025 1verdicts
CONDITIONAL 1roles
dataset 1polarities
use dataset 1representative citing papers
citing papers explorer
-
Physics-informed deep learning for infectious disease forecasting
A physics-informed neural network that embeds a nine-compartment COVID-19 model produced competitive 1-4 week forecasts for California, outperforming naive and sequence deep learning baselines.