A Neural SIR Model for Global Forecasting
Philip Nadler, Rossella Arcucci, Yike Guo · Rare & Special e-Zone (The Hong Kong University of Science and Technology) · 2020
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.