Fast and Accurate Forecasting of COVID-19 Deaths Using the SIkJ$α$ Model
This work addresses the need for fast and accurate COVID-19 death forecasting to inform policy decisions, though it is incremental as it builds on an existing model.
The authors tackled the problem of forecasting COVID-19 deaths by extending the SIkJα model to simplify parameter learning with fast linear regressions, achieving better root mean squared error than seven CDC approaches in most evaluations and generating forecasts for all US states in 3.18 seconds.
Forecasting the effect of COVID-19 is essential to design policies that may prepare us to handle the pandemic. Many methods have already been proposed, particularly, to forecast reported cases and deaths at country-level and state-level. Many of these methods are based on traditional epidemiological model which rely on simulations or Bayesian inference to simultaneously learn many parameters at a time. This makes them prone to over-fitting and slow execution. We propose an extension to our model SIkJ$α$ to forecast deaths and show that it can consider the effect of many complexities of the epidemic process and yet be simplified to a few parameters that are learned using fast linear regressions. We also present an evaluation of our method against seven approaches currently being used by the CDC, based on their two weeks forecast at various times during the pandemic. We demonstrate that our method achieves better root mean squared error compared to these seven approaches during majority of the evaluation period. Further, on a 2 core desktop machine, our approach takes only 3.18s to tune hyper-parameters, learn parameters and generate 100 days of forecasts of reported cases and deaths for all the states in the US. The total execution time for 184 countries is 11.83s and for all the US counties ($>$ 3000) is 101.03s.