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Modelled COVID-19 pandemic with a system of 9 first order differential equations. The system was fitted to the values of the pandemic in Italy, UK, India, Brazil and Sweden, and numerically solved using MCMC statistical methods in python’s lmfit module. Estimates of the real number of infected people and predictions for the future were then made.

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Epidemiological_model

Modelled COVID-19 pandemic with a system of 9 first order differential equations. The system was fitted to the values of the pandemic in Italy, UK, India, Brazil and Sweden, up to June, and numerically solved using MCMC statistical methods in python’s lmfit module. It was also fitted to Spain, although the code for Spain is unavaiable. Estimates of the real number of infected people and predictions for the future were then made.

The countries dataset was used to update and store the epidemiological values of the anlysed countries. The other codes were used to analyse the data.

By Stefano Veroni and José Antonio Guzmàn Funck. Supervisor: Kilian D Stenning

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Modelled COVID-19 pandemic with a system of 9 first order differential equations. The system was fitted to the values of the pandemic in Italy, UK, India, Brazil and Sweden, and numerically solved using MCMC statistical methods in python’s lmfit module. Estimates of the real number of infected people and predictions for the future were then made.

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