Epidemic Spread Modelling with the SIR model

The recent national lockdown due to the Covid-19 global pandemic has afforded me all the free time in the world. So, I took this opportunity to learn more about how experts study epidemics to make inferences and devise policies. I inquired Google and YouTube on the topic and quickly discovered a wealth of relevant resources. Books, papers, articles and videos were aplenty. Herbert W. Hethcote (Three Basic Epidemiological Models), Aresh Dadlani et. al. (Deterministic Models in Epidemiology: from Modeling to Implementation), Fred Brauer and Carlos Castillo-Chávez (Epidemic Models) and Yiran Jing were the authors whose work I found really helpful in understanding how epidemic modelling works. YouTube videos from channels: Trefor Bazett, Tom Rocks Maths and VetenskapensHus were veritable gems on the subject owing to their platform-incentivized conciseness. Anyway, back to SIR - this model is arguably the simplest of all while still being able to yield useful insights into the workings of an epidemic.
There are two variants of the model - with and without vital dynamics (birth, death, migration). Here, I deal with the no vital dynamics variant which basically implies there's no significant change in the total population of the community under study, at least until the epidemic lasts. The idea is, the population is divided into three categories - Susceptible (normal, not yet infected individuals), Infective (infected individuals, who can infect others) and Recovered/Removed (individuals who have recovered from the disease and will not again go back to being infected, say because they've gained immunity). Populations of each compartment are constantly changing, moving from Susceptible(S) to Infective(I) to Recovered(R). The flux or rate of change of the population of each compartment (dS/dt, dI/dt, dR/dt) is assumed to be functions of one or multiple of such categorical populations. For example, the rate at which S changes into I is assumed to be proportional to the product of the Infective population and the Susceptible population. Such an ODE is written down for each of the compartments - giving a total of 3 ODEs that have to be solved simultaneously while satisfying the constant population assumption(S+I+R=N=constant). The system of ODEs happens to be impossible to solve explicitly owing to non-linearities in two of the three equations. So, only numeric solutions for I, S and R as functions of time are possible, via discretization as per the ole'faithful Finite Difference Method, which is what I've done here. The details of the model can be found on any of the resources listed above.
Here's how the graphs look:

Here's the code.
EDIT:
This is after I've realized what GitHub Pages is.
Live demo here
Corresponding repo here
Future updates, if any, will be made to the repo, not the Gist