An empirical analysis of the Ebola outbreak in West Africa

Abstract

The data for the Ebola outbreak that occurred in 2014–2016 in three countries of West Africa are analysed within a common framework. The analysis is made using the results of an agent based Susceptible-Infected-Removed (SIR) model on a Euclidean network, where nodes at a distance l are connected with probability P(l) ∝ l−δ, δ determining the range of the interaction, in addition to nearest neighbors. The cumulative (total) density of infected population here has the form , where the parameters depend on δ and the infection probability q. This form is seen to fit well with the data. Using the best fitting parameters, the time at which the peak is reached is estimated and is shown to be consistent with the data. We also show that in the Euclidean model, one can choose δ and q values which reproduce the data for the three countries qualitatively. 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. Rescaling the real data a reasonably good quantitative agreement with the simulation results is obtained.

Modeling and forecasting the early evolution of the Covid-19 pandemic in Brazil

“…(2) Model: we use variations of the SIR model mentioned above. One of the advantages of the SIR model is the simplicity and researchers have used this model in several successful attempts to model the spread of infectious diseases 14 – 17 . (3) Estimation: our paper estimates all the parameters based on a clear hierarchical procedure based on squared error minimization.…”

Parallel Minority Game and it’s application in movement optimization during an epidemic

“…We take and each state is initially occupied with agents i.e., an occupation fraction of about 0.5, giving an average coordination number . This is close to the site percolation threshold [28] , which represents a reasonable restriction in interactions among the populations in terms of either the imposed social distancing or due to other factors seen in epidemic spreading [29] , [30] . An occupancy below this would make the population clusters fragmented, hence having very less chance of infection spreading.…”

Numerical Prediction for Spreading Novel Coronavirus Disease in India Using Logistic Growth and SIR Models

“…SIR model stands for Susceptible-Infected-Recovered which is a compartmental disease model to investigate the disease spread in a population of region or country [20][21][22][23]. In this present study the constant population belongs to any one of the three compartments: Susceptible, Infected or Recovered.…”