Multi-scale causality analysis between COVID-19 cases and mobility level using ensemble empirical mode decomposition and causal decomposition

Abstract

The global spread of the coronavirus disease 2019 (COVID-19) pandemic has affected the world in many ways. Due to the communicable nature of the disease, it is difficult to investigate the causal reason for the epidemic's spread sufficiently. This study comprehensively investigates the causal relationship between the spread of COVID-19 and mobility level on a multi time-scale and its influencing factors, by using ensemble empirical mode decomposition (EEMD) and the causal decomposition approach. Linear regression analysis investigates the significance and importance of the influential factors on the intrastate and interstate causal strength. The results of an EEMD analysis indicate that the mid-term and long-term domain portrays the macroscopic component of the states' mobility level and COVID-19 cases, which represents overall intrinsic characteristics. In particular, the mobility level is highly associated with the long-term variations of COVID-19 cases rather than short-term variations. Intrastate causality analysis identifies the significant effects of median age and political orientation on the causal strength at a specific time-scale, and some of them cannot be identified from the existing method. Interstate causality results show a negative association with the interstate distance and the positive one with the airline traffic in the long-term domain. Clustering analysis confirms that the states with the higher the gross domestic product and the more politically democratic tend to more adhere to social distancing. The findings of this study can provide practical implications to the policymakers that whether the social distancing policies are effectively working or not should be monitored by long-term trends of COVID-19 cases rather than short-term.

Keywords: COVID-19; Causal decomposition; Ensemble empirical mode decomposition; Mobility; Multi-scale causality analysis.

Fig. 1

Research process for investigating intrastate and interstate causal strength of COVID-19 cases and the number of trips.

Fig. 2

Ensemble empirical mode decomposition (EEMD) results for COVID-19 cases and daily trips in California and New York states: Energy, period, and cross-correlation coefficients of each IMF are indicated.

Fig. 3

Energy strength and corresponding periods for each IMF gathered from the number of COVID-19 cases and the number of daily trips by states.

Fig. 4

Interstate causal relationship between COVID-19 cases in Nevada (red line) and California (blue line) based on Ensemble empirical mode decomposition (EEMD) and causal decomposition analysis.

Fig. 5

Scatter plot of clustering results of K-means clustering results (IMFs energy strength of COVID-19 cases) and hierarchical clustering.

Fig. 6

K-means clustering results (IMFs energy strength of COVID-19 cases, k=2).