We present a simple analytical description of the first wave of the COVID-19 pandemic based on a phenomenologically motivated ansatz. The proposed expression describes the time evolution of the number of currently infected individuals (active cases), $N\left(t\right)$, with an effective exponent $\delta$ determined empirically from the epidemic data. For $\delta =1$, the expression reduces to a Gaussian time dependence. The model is validated against epidemiological data from Israel, Austria, New Zealand, and Germany and shows good agreement with the observations. The analytical framework enables estimates of the epidemic peak, its duration, and the temporal evolution of the number of infected individuals. Furthermore, the ratio $N_{\mathrm{r},\mathrm{d}}/N$ of the cumulative number of recovered and deceased patients to the number of active infected individuals is shown to exhibit an approximately S-shaped time dependence when plotted on a log-log scale, also in good agreement with the data.

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• Epidemic
• Phase transitions in biological systems