SIR final size that we can use to demonstrate the fate of S

版本 1.0.1 (2.8 KB) 作者: Ayesha Sohail
This code utilises optimisation twice, for parametric approximation and for disease fate by optimising S_{\infty}. I hope it will help!
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更新时间 2024/9/13

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STAGE 1
We use optimization techniques to fit the parameters α and β by
minimizing the error function:
min
α,β
nX
i=1
(yactual,i − ypredicted,i (α, β))2
▶ Various optimization algorithms can be used, such as gradient
descent or least squares.
▶ MATLAB functions like fminsearch or lsqcurvefit are
commonly used.
STAGE 2
We present graphical results from the SIR model with fitted
parameters:
▶ Infected Data and Model Predictions:
▶ The plot shows actual infection data alongside predictions
generated by the SIR model.
▶ The model was used to estimate susceptible populations S(t)
based on given infected data.
▶ Susceptible Population (S(t)):
▶ The plot of S(t) was derived from the model, given the
constraints and conditions applied.
▶ The green star represents the optimized final size S∞, obtained
through constrained optimization.
▶ Note that S∞ is shown within the context of the model’s
predictions and the given infected data.

引用格式

Ayesha Sohail (2025). SIR final size that we can use to demonstrate the fate of S (https://ww2.mathworks.cn/matlabcentral/fileexchange/172665-sir-final-size-that-we-can-use-to-demonstrate-the-fate-of-s), MATLAB Central File Exchange. 检索时间: .

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版本 已发布 发行说明
1.0.1

Slight change in N
1.0.0