Epidemic Spread and Network Connectivity

This paper presents selected historical mortality statistics and analyze their characteristics and trends. Statistics are collated particularly for the pandemics related with cholera, plague, Spanish flu and covid-19 between 1720 -2020. We introduce epidemic and epidemic spreading. Then the dissertation continues with brief introduction and discussion of Epidemic models such as SI, SIS and SIR Epidemic pass-through populations and persists over long time periods. Thus, efficient modeling of the understanding network plays a crucial role in understanding the spread and prevention of an epidemic. Further the mathematical modelling of infectious disease epidemics on network, starting from the simplest Erdos-Renyi random graphs, Percolation on graph and epidemics is studied. We also show empirical results of applying the models to calculate the spread of contagion and information connectivity on two complex networks suitable for the models. Based on the results, we calculate centrality metrics reflecting the outcome of the application, highlighting its important properties. We observed that the centrality values obtained by running the epidemic model and the connectivity model turn out to be mutually equivalent, as predicted by their similar fashions of calculation. Here we study about the network Modelling of Epidemics and its similarities with the probabilistic model.