Performance comparison of Python, MATLAB and R for numerical solutions of SI and SIR epidemiological models

TL;DR

This paper compares the computational efficiency and accuracy of Python, MATLAB, and R in solving SI and SIR epidemiological models using various numerical methods, providing practical guidance for epidemic modeling.

Contribution

It offers a comprehensive comparison of software tools and numerical methods for epidemiological models, highlighting their performance and accuracy in practical applications.

Findings

Python, MATLAB, and R show different run-time efficiencies.

RK4 method provides high accuracy across software.

MATLAB's ODE45 offers a high-accuracy reference solution.

Abstract

Mathematical modeling plays a vital role in epidemiology, offering insights into the spread and control of infectious diseases. The compartmental models developed by Kermack and McKendrick, particularly the SI (Susceptible-Infected) and SIR (Susceptible-Infected-Recovered) models, form the basis of many epidemic studies. While some simple cases permit analytical solutions, most real-world models require numerical methods such as Euler's method, the fourth-order Runge-Kutta (RK4) method, and Predictor-Corrector (P-C) methods. These methods are typically implemented in scientific computing software like Python, MATLAB, and R. However, the computational efficiency and run-time performance of these software tools in solving epidemiological models have not been comprehensively compared in the literature. This study addresses this gap by solving the SI and SIR models using Euler's method, RK4, and P-C methods in Python, MATLAB, and R. Execution times are recorded for each implementation to evaluate computational efficiency. Additionally, for the SI model, where an exact analytical solution exists, R2 values are computed to assess numerical accuracy. For the SIR model, a high-accuracy reference solution is obtained by solving the system using MATLAB's ODE45 solver, and the SIR solutions computed via the RK4 method in MATLAB are compared against this reference. The results provide a comparative perspective on the accuracy and run-time performance across different software and numerical methods, offering practical guidance for researchers and practitioners in selecting suitable tools for epidemic modeling.