in Healthcare & Epidemiology
Constructing Statistical Characteristics of COVID-19 Infection Trees from Contact-Tracing Data
The global impact of the COVID-19 pandemic has highlighted the need for modern data-driven approaches to managing such epidemiological events with minimum socioeconomic impact. To this end, we analyze publicly available contact tracing data from Karnataka, India to rebuild infection trees based on the ancestry of infections revealed in the data. In an attempt to statistically analyze these trees, we find that both the number of infections originating from a person as well as the size of the tree created by a hierarchy of such infections show a remarkably similar characteristic: the tails of these distributions decay slowly and appear to conform to the power-law form. As a consequence of this discovery, mitigation strategies could be designed by identifying and containing super-spreaders along with milder general restrictions.
Heterogeneous Contact Networks in COVID-19 Spreading: The Role of Social Deprivation
We have two main aims. First, we use theories of disease spreading on networks to look at the COVID-19 epidemic on the basis of individual contacts -- these give rise to predictions that are often rather different from the homogeneous mixing approaches usually used. Our second aim is to look at the role of social deprivation, again using networks as our basis, in the spread of this epidemic. We choose the city of Kolkata as a case study, but assert that the insights so obtained are applicable to a wide variety of urban environments that are densely populated and where social inequalities are rampant. Our predictions of hotspots are found to be in good agreement with those currently being identified empirically as containment zones and provide a useful guide for identifying potential areas of concern.