Covid-19 spread: Reproduction of data and prediction using a SIR model on Euclidean network

Abstract

We study the datafor the cumulative as well as daily number of cases in the Covid-19 outbreak in China. The cumulative data can be fit to an empirical form obtained from a Susceptible-Infected-Removed (SIR) model studied on an Euclidean network previously. Plotting the number of cases against the distance from the epicenter for both China and Italy, we find an approximate power law variation with an exponent $\sim 1.85$ showing strongly that the spatial dependence plays a key role, a factor included in the model. We report here that the SIR model on the Eucledean network can reproduce with a high accuracy the data for China for given parameter values, and can also predict when the epidemic, at least locally, can be expected to be over.

4 pages, 3 figures

References in corpus (10)

• Effective containment explains sub-exponential growth in confirmed cases of recent COVID-19 outbreak in Mainland China
• Abnormal respiratory patterns classifier may contribute to large-scale screening of people infected with COVID-19 in an accurate and unobtrusive manner
• Trend and forecasting of the COVID-19 outbreak in China
• Artificial Intelligence Forecasting of Covid-19 in China
• Space-time dependence of corona virus (COVID-19) outbreak
• How many infections of COVID-19 there will be in the "Diamond Princess"-Predicted by a virus transmission model based on the simulation of crowd flow
• Scaling features in the spreading of COVID-19
• The Reconstruction and Prediction Algorithm of the Fractional TDD for the Local Outbreak of COVID-19
• Visual Data Analysis and Simulation Prediction for COVID-19
• Data analysis for the COVID-19 early dynamics in Northern Italy

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