BibTex format
@inproceedings{Nadler:2020,
author = {Nadler, P and Arcucci, R and Guo, Y},
pages = {254--266},
title = {A Neural SIR Model for Global Forecasting},
year = {2020}
}
Publications
Citation
RIS format (EndNote, RefMan)
TY - CPAPER
AB - Being able to understand and forecast epidemic developments is crucial for policymakers. We develop a predictive model combining epidemiological dynamics of compartmental models with highly non-linear interactions learned by a LSTM Network. A novel dynamic SIR model is fit to variables related to the population transmission of Covid-19. This is embedded in a Bayesian recursive updating framework which is then coupled with a LSTM network to forecast cases of Covid-19. The model significantly improves forecasts over simple univariate LSTM or SIR models. We apply the model to developed and developing countries and forecast confirmed infections and analyze future trajectories.
AU - Nadler,P
AU - Arcucci,R
AU - Guo,Y
EP - 266
PY - 2020///
SP - 254
TI - A Neural SIR Model for Global Forecasting
ER -