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

2020/03/16 by Kathakali Biswas, Abdul Khaleque, Biswas, Kathakali +3 · 4 citations
Biochemistry, Genetics and Molecular Biology · Mathematics · Medicine · Physics and Astronomy · Social Sciences · #COVID-19 diagnosis using AI #COVID-19 epidemiological studies #FOS: Biological sciences #FOS: Physical sciences #Misinformation and Its Impacts #Physics and Society (physics.soc-ph) #Populations and Evolution (q-bio.PE) #Statistical Mechanics (cond-mat.stat-mech) #cond-mat.stat-mech #physics.soc-ph #q-bio.PE

paper · pdf · doi:10.48550/arxiv.2003.07063

4 pages, 3 figures

arxiv created 2020/03/16 · openalex publication_date 2020/03/16 · arxiv updated 2020/03/17 · openalex created_date 2020/03/23 · openalex updated_date 2026/07/28

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 ∼ 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.

Citations

• A pneumonia outbreak associated with a new coronavirus of probable bat origin
• Scaling features in the spreading of COVID-19
• Visual Data Analysis and Simulation Prediction for COVID-19
• Effective containment explains sub-exponential growth in confirmed cases of recent COVID-19 outbreak in Mainland China

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