Towards Quantification of Covid-19 Intervention Policies from Machine Learning-based Time Series Forecasting Approaches

COVID-19 has become the most devastating infectious disease of the 21st century. Governments worldwide devised a range of policies to control the pandemic and reduce losses in multiple aspects. To make timely and wise decisions, a thorough analysis of policies with reliable statistical evidence is crucial. Obtaining these statistical references to policies would help decision-makers optimize intervention policies to the maximum. In this work, we designed a policy-aware time series forecasting model based on the transformer architecture and successfully estimated COVID-19 epidemic trends. Through incorporating temporal information from 16 policy indicators, we developed a policy-aware time series model that demonstrated high forecasting performance. We further quantify the causal effect of indicators by employing a counterfactual approach and propose two static metrics lag period and average effect. The empirical results demonstrate that our model causally verifies the effectiveness of all 16 policy indicators in controlling COVID-19 virus transmission in the US. From qualitative analysis, we conclude that frequent adjustments to the extent of policy actions may intensify epidemic spread. Our study provides insight into modeling government policies from a statistical angle, and it has practical applications when confronting potential influenza-like diseases in the future.