Effects of Social Distancing on Spread of a Pandemic: Simulating Trends of COVID-19 in India

Most of the diseases spread from human-to-human which makes it dangerous for anyone who comes in contact with an infected being. The key point to note here is that such a spread gives rise to a network where nodes represent humans and edges show if two humans came in contact with each other or not. Studying and analysing pandemic networks help in managing the spread of disease efficiently. The recent coronavirus (COVID-19) outbreak that was first identified in December 2019 in Wuhan, Hubei province, China according to WHO reports, is an apt example of a deadly contagious disease. India has been fighting the virus since February 2020 relentlessly, with the scientists experimenting to find a cure for the disease, and healthcare personnel along with other essential workers ensuring that all necessary preventive and protective measures are being taken to reduce health risks. In our proposed study, we aim at forecasting the spread of COVID-19 in India with the help of SEIR-DH and linear regression model, by simulating the dynamics of disease spreading in a large population. We also aim to mathematically depict how increasing the severity of social distancing can affect the spread of the disease. The results of our study indicate that increasing the strictness of social distancing measures can help reduce the overall number of infected patients and also help flatten the epidemic curve of COVID-19 spread. The curve depicts the number of infected patients requiring healthcare for combating the disease over time.