Online Program

Regional COVID-19 Dynamics: Surrogate Synchrony in Case Infection Rates (309927)

Keywords: COVID19, dyadic processes, dynamic systems, synchrony, disease surveillance, disease prevention, regional dynamics

As many US states begin to reopen more fully, lifting mask mandates, travel restrictions and distancing guidelines that were initially implemented to reduce the spread of COVID-19, this study explores the spatiality of COVID-19 case infection rate synchrony among regions of neighboring US states. Synchrony is roughly defined as the way two interacting processes or systems are patterned or synchronized in timing and in form in a nonrandom way and can occur with a dynamic lag such that there is a direction of entrainment whereby one system entrains the other. Tschacher and Haken (2019) proposed the use of the surrogate synchrony (SUSY) algorithm to estimate the dyadic coupling between two simultaneous processes based upon time-lagged cross-correlations, with significance assessed utilizing permutation. Using Fisher’s Z transformation to allow for aggregation, the SUSY algorithm is extended in the current work to provide an estimate of in-phase synchrony between each US state and its contiguous neighbors. The spatially-varying estimates are then mapped to allow for visualization of regional COVID-19 case infection rate synchrony, including directionality, which can assist policy makers in the proposal of targeted, systematic interventions related to in-flowing and cross-border virus spread.