Clustering of cantons in Costa Rica based on interest variables during the beta variant of Covid-19

Abstract

The study of case behavior and the analysis of bioindicators are relevant and important for
decision-making by health authorities worldwide, related to the Covid-19 pandemic. Thus,
numerous investigations have been carried out around the world to understand this phenomenon,
its variants, and its primary impacts on population health. In this study, a cluster analysis was
conducted based on the variables of mortality rate, morbidity rate, and fatality rate, along with
cantonal geographical density, for the period of the beta variant in Costa Rica, corresponding
to the months from February to June 2021. Therefore, a total of three methods were chosen
to obtain groups: k-means, k-medoids, and fuzzy methods; as well as two types of distances:
Euclidean and Manhattan. Additionally, the sum of squares within groups and the Dunn index
were used to validate the formation of the clusters. It was identified that the method and distance
that formed the most compact cantonal clusters with lower intragroup variability were k-medoids
and Manhattan, respectively, due to their greater robustness against extreme values. Among
the formed groups, cluster 1 has a moderate impact of the pandemic during the specified
variant, while groups 2 and 3 have low and high impacts, respectively. Moreover, groups 1 and
2 are predominantly composed of cantons outside the Greater Metropolitan Area, in contrast to
the third group. This analysis provides valuable insights for health authorities in understanding
the impacts of the Covid-19 pandemic in Costa Rican regions and aids in the development of
targeted strategies for effective management.