Micro-level social structures and the success of COVID-19 national policies

Similar policies in response to the COVID-19 pandemic have resulted different success rates. Although many factors are responsible for variances policy success, our study shows that micro-level structure of person-to-person interactions—measured by average household size and in-person social contact rate—can be an important explanatory factor. To create explainable model, we propose a network transformation algorithm simple computationally efficient scaled based on these parameters, as well incorporate national-level data dynamic SEIR simulations. The model was validated during early stages pandemic, which demonstrated it can reproduce ordinal ranking trend infected cases various European countries sufficiently similar terms some socio-cultural factors. We also performed several counterfactual analyses illustrate how policy-based scenario analysis rapidly easily with models. A using from ten indicates differences interactions explain substantial part variations national policies.