Micro-level Social Structures and the Success of COVID-19 National Policies

Abstract

Similar policies in response to the COVID-19 pandemic have resulted in different success rates. Although many factors are responsible for the variances in policy success, our study shows that the micro-level structure of person-to-person interactions, measured by the average household size and in-person social contact rate, can be a significant explanatory factor for policy success. To create an explainable model, we propose a novel network transformation algorithm to create a simple and computationally efficient scaled network based on these micro-level parameters, and to incorporate national-level policy data in the network dynamic, all without requiring any parameter calibration. The model was further validated during the early stages of the COVID-19 pandemic, showing that it is capable of reproducing the dynamic ordinal ranking and trend of infected cases of various countries where they are sufficiently similar in terms of other socio-cultural factors (six European countries). We then perform several counterfactual analyses to illustrate how policy-based scenario analysis can be performed rapidly and easily with these explainable models.

Note: Funding: This work was partially supported by the NSF Grant [grant number 1554560]; and in part by the internal funding by Northeastern University.

Declaration of Interests: None to declare.

Keywords: COVID-19, Contagion on Networks, Social Distancing Policies, Explainable Model, Counterfactual Analysis, Complex Networks, Non-pharmaceutical Interventions, Policy Analysis