Clustering of countries based on the associated social contact patterns in epidemiological modelling
This work addresses the need for better country comparisons in epidemic modeling, particularly for non-pharmaceutical interventions, but it is incremental as it applies existing clustering methods to contact data.
The paper tackles the problem of comparing and grouping countries based on their social contact patterns for epidemiological modeling, presenting a framework that clusters countries using contact matrices and demonstrates its application in a COVID-19 model to identify comparable countries during pandemics.
Mathematical models have been used to understand the spread patterns of infectious diseases such as Coronavirus Disease 2019 (COVID-19). The transmission component of the models can be modelled in an age-dependent manner via introducing contact matrix for the population, which describes the contact rates between the age groups. Since social contact patterns vary from country to country, we can compare and group the countries using the corresponding contact matrices. In this paper, we present a framework for clustering countries based on their contact matrices with respect to an underlying epidemic model. Since the pipeline is generic and modular, we demonstrate its application in a COVID-19 model from Röst et. al. which gives a hint about which countries can be compared in a pandemic situation, when only non-pharmaceutical interventions are available.