There has been a lot of talk and work done on the data set of COVID-19 infections which shows that the growth of infection is exponential since the growth does not seem to be linear. However, that does not seem to be the case here. There was an exponential growth pattern in the early stages of the virus, however it was followed by the power law distribution.
The possibility of exponential growth exist in random network since there is an equal probability of two people coming together. However, the real world network of this infectious disease is not like random network where people come across each other randomly. The structure of this network shows that majority of the population have scattered connections with their neighbours and that the connectedness of people is less than exponential, which is similar to a small world network.This can help to show that the spread of COVID-19 happens in small world interaction network such as neighbours or people travelling together with the infected ones. A small world network is a distribution which comes between a random network and a well connected network.
This analysis can also be seen practically as the disease is talking longer to double the deaths as shown in the image below. This is because the susceptible individuals around the infected are decreasing because of they might have higher immunity or mild symptoms which are not be worth getting tested.
This topic is very essential to be talked about because this performs a more detailed analysis on the data of COVID-19 infections and predicts a more accurate infections rate which is necessary to correctly educate the government and general public to make help them make the right decisions for themselves and the community.
Link: https://www.zdnet.com/article/graph-theory-suggests-covid-19-might-be-a-small-world-after-all/