Educational Browser-Native SIR Simulation: Analytical Benchmarks Showing Numerical Accuracy for Lightweight Epidemic Modeling
Ben-Joseph, J.
Show abstract
Lightweight epidemic calculators are widely used for teaching and rapid scenario exploration, yet many omit the methodological detail needed for scientific reuse. We present a browser-native SIR calculator that exposes forward Euler and classical fourth-order Runge-Kutta (RK4) integration alongside epidemiologically interpretable outputs and a population-conservation diagnostic. The implementation is anchored to analytical properties of the deterministic SIR system, including the epidemic threshold, the peak condition, and the final-size relation. Bench-mark experiments show that RK4 is essentially step-size invariant over practical discretizations, whereas Euler at a coarse one-day step overestimates peak prevalence by 3.97% and final size by 0.66% relative to a fine-step RK4 reference. These results demonstrate that browser-based tools can support publication-quality computational narratives when solver choice, diagnostics, and assumptions are treated as first-class outputs.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Fluid-Derived Lattices for Unbiased Modeling of Bacterial Colony Growth 95%
- Prediction of Covid-19 spreading and optimal coordination of counter-measures: From microscopic to macroscopic models to Pareto fronts 95%
- Phenotype switching in a global method for agent-based models of biological tissue 94%
Similar papers in this journal
- Verifying Infectious Disease Scenario Planning for Geographically Diverse Populations 95%
- An R t - based model for predicting multiple epidemic waves in a heterogeneous population 94%
- Demonstrating multi-country calibration of a tuberculosis model using new history matching and emulation package - hmer 94%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.