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

By Kathakali Biswas, Abdul Khaleque, P. Sen. 2020

89 citations recorded by the source corpus.

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.

Subfields: Modeling and Simulation, Radiology, Nuclear Medicine and Imaging, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/1360b6c29928bc88e40f1820b6da4bd0efaa6e05

Citations in the corpus

• Deep learning methods for forecasting COVID-19 time-Series data: A Comparative study (2020, unknown): “In [12], the SIR model is extended on the euclidean network to enhance 37 37 the prediction quality of confirmed cases and illustrate the key role of the spatial factor in the epidemic propagation.”
• To mask or not to mask: Modeling the potential for face mask use by the general public to curtail the COVID-19 pandemic (2020, unknown): “Most mathematical models of the COVID-19 pandemic can broadly be divided into either population-based, SIR (Kermack-McKendrick)type models, driven by (potentially stochastic) differential equations [38, 20, 34, 22, 21, 23, 31, 26, 32, 24, 33] , or agent-based models [39, 28, 25, 27, 30] , in which i”