Superspreading has become a key mechanism of COVID-19 transmission which creates chaos. The classical approach of compartmental models may not sufficiently reflect the epidemiological situation amid superspreading events (SSEs). We perform a data-driven approach and recognise the deterministic chaos of confirmed cases. The first derivative (≈difference of total confirmed cases) and the second derivative (≈difference of the first derivative) are used upon SSEs to showcase the chaos. Varying solution trajectories, sensitivity and numerical unpredictability are the chaotic characteristics discussed here.
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