  A system to model the spread of COVID-19 cases after lockdown has been
proposed, to define new preventive measures based on hotspots, using the graph
clustering algorithm. This method allows for more lenient measures in areas
less prone to the virus spread. There exist methods to model the spread of the
virus, by predicting the number of confirmed cases. But the proposed system
focuses more on the preventive side of the solution from a geographical point
of view, by predicting the areas or regions that may become hotspots for the
virus in the near future. The fact that the virus can only be transmitted by
being in close proximity to an already infected person, suggests that, the
regions that can easily be reached from an existing hotspot, have a higher
chance of becoming a new hotspot. Moreover, in smaller regions, even after
strict provisions, positive cases have been found. To consider this fact, the
geographic distance between the nearest hotspots can be used as a measure of
likelihood of the region also becoming a hotspot. In this paper, a weighted
graph of regions with the regions themselves as weighted nodes with weight of
the nodes as the number of active cases and the distance as edge weights. The
graph can be completely connected or connected based on a distance threshold.
The nodes are the administrative, and the distance measure tells the possible
transmission between separate communities. Using this data, the potential
regions that can become hotspots can be predicted, and preventive measures can
be devised.
