A Hybrid Predictive Model For Mitigating Health And Economic Factors During A Pandemic

Many statistical and machine learning (ML) models have been developed to provide forecasts for the COVID-19 crisis. Acquiring qualitative data with a rather short timeframe is a challenge for anyone who wants to build a ML algorithm to support forecasts about the pandemic. We propose a hybrid approach that takes into consideration factors from human knowledge in order to reinforce or correct data-driven ML predictions.