Background: Incidence(t), derived from observational data published by the Robert Koch Institute, Berlin, Germany, is a commonly used parameter to illustrate the course of the COVID-19 pandemic in Germany. The parameter tα(t), equivalent to the doubling-time of the virus described by our research group, has also been useful in this regard.
Aims: To identify and compare parameters suitable for monitoring the course of the pandemic and to evaluate the extent to which these reflect qualitatively and quantitatively the effects of interventional measures introduced to control the pandemic.
Materials and methods: Parameters potentially useful for monitoring the course of the pandemic were obtained empirically or derived using the Bateman SIZ model and observational data for the daily increase in the number of new infections. The doubling-time in the number of infections, tα(t) was obtained by curve-fitting observational data for the previous 14-day interval and a fixed value for tbeta (half-life for rate of recovery = 6.24 days). The effects of the interventional measures on the course of the pandemic as reflected in the modulation of the identified parameters were evaluated.
Results: A total of 5 different parameters (Incidence(t), R9(t), tα_stable(S), Incidencecalc(t+14), tα(t)) of potential value in monitoring the course of the pandemic were identified. Lockdown measures altered Incidence(t), and these effects could be quantitated from alterations in the reproduction factor, R9(t), a parameter with prognostic value reflecting the consequences of non-intervention. The parameter tα_stable(S) is a function of the proportion of susceptible persons and therefore reflects the effects of vaccination. Although vaccination was in progress, together with lockdown and restrictions associated with the Recovered-Vaccinated-tested rule, R9(t) remained above 1.0 apparently due to the emergence of the more virulent Mδ variant (4th wave of the pandemic in Germany).
Conclusion: i) The newly identified parameters provide qualitative and quantitative information on the course of the COVID-19 pandemic in Germany. ii) R9(t) is a suitable parameter for assessing the effects of interventional measures and may have prognostic value. iii) The parameter tα_stable(S), a function of the proportion of susceptible persons, reflects the effects of vaccination. iv) Incidencecalc(t+14) provides prognostic information on the projected course of the pandemic with the assumption that the factors driving the pandemic do not change and is recommended as a suitable parameter for making decisions in real-time, with minimum delay, whether lockdown measures should be implemented.