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Modelling the spread of epidemics on complex networks has led to a number of interesting results in the recent past. Starting with a brief overview, we discuss the characteristic features of the Susceptible-Infected-Removed (SIR) model considered on a network embedded in Euclidean space with random long range interactions chosen following a certain prescription. Numerical simulations suggest a simple form of the cumulative density of infected agents at time t, R(t), depending on the network parameters and the infection probability q. The epidemic dynamics shows the effect of the phase transition occurring in the network. The data for the Ebola outbreak that occurred in 2014-2016 in three countries of West Africa are analysed within a common framework using this model. The predicted form of R(t) is seen to fit well with the data. We also show that in the Euclidean model, appropriate choices of the parameters reproduce the data for the three countries with considerable accuracy. These choices are correlated with population density, control schemes and other factors. Comparing the real data and the results from the model one can also estimate the size of the actual population susceptible to the disease.

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Parongama Sen, University of Calcutta
TP conference room 0.02
Contact: Joachim Krug