Why are different estimates of the effective reproductive number so different? A case study on COVID-19 in Germany

The effective reproductive number R$_t$ has taken a central role in the scientific, political, and public discussion during the COVID-19 pandemic, with numerous real-time estimates of this quantity routinely published. Disagreement between estimates can be substantial and may lead to confusion among decision-makers and the general public. In this work, we compare different estimates of the national-level effective reproductive number of COVID-19 in Germany in 2020 and 2021. We consider the agreement between estimates from the same method but published at different time points (within-method agreement) as well as retrospective agreement across eight different approaches (between-method agreement). Concerning the former, estimates from some methods are very stable over time and hardly subject to revisions, while others display considerable fluctuations. To evaluate between-method agreement, we reproduce the estimates generated by different groups using a variety of statistical approaches, standardizing analytical choices to assess how they contribute to the observed disagreement. These analytical choices include the data source, data pre-processing, assumed generation time distribution, statistical tuning parameters, and various delay distributions. We find that in practice, these auxiliary choices in the estimation of R$_t$ may affect results at least as strongly as the selection of the statistical approach. They should thus be communicated transparently along with the estimates

Why are different estimates of the effective reproductive number so different? A case study on COVID-19 in Germany.

The effective reproductive number Rt has taken a central role in the scientific, political, and public discussion during the COVID-19 pandemic, with numerous real-time estimates of this quantity routinely published. Disagreement between estimates can be substantial and may lead to confusion among decision-makers and the general public. In this work, we compare different estimates of the national-level effective reproductive number of COVID-19 in Germany in 2020 and 2021. We consider the agreement between estimates from the same method but published at different time points (within-method agreement) as well as retrospective agreement across eight different approaches (between-method agreement). Concerning the former, estimates from some methods are very stable over time and hardly subject to revisions, while others display considerable fluctuations. To evaluate between-method agreement, we reproduce the estimates generated by different groups using a variety of statistical approaches, standardizing analytical choices to assess how they contribute to the observed disagreement. These analytical choices include the data source, data pre-processing, assumed generation time distribution, statistical tuning parameters, and various delay distributions. We find that in practice, these auxiliary choices in the estimation of Rt may affect results at least as strongly as the selection of the statistical approach. They should thus be communicated transparently along with the estimates

Comparison of uncertainty intervals after standardization of analytical choices.

The figure shows 95% uncertainty intervals corresponding to Fig 6, Step 4.</p

Temporal coherence of <i>R</i>_<i>t</i> estimates.

Panels: A Proportion of 95% uncertainty intervals issued in real-time which contained the consolidated estimate. B Mean width of 95%-uncertainty intervals (unavailable for HZI, who only published point estimates). C Mean absolute difference of the real-time and consolidated estimates. D Same as C, but signed rather than absolute differences. E Proportions of cases in which real-time and consolidated point estimates disagree on whether Rt > 1. F Same as D, but with a tolerance region [0.97, 1.03], i.e., only instances where real-time and consolidated estimates are on different sides of this interval are counted. All indicators are shown as a function of the time between the target date (as stated by the teams) and the publication date. Averages refer to the period October 1, 2020—July 22, 2021 (see Fig F in S1 Text for exact periods during which methods were operated). The consolidated estimate corresponds to the one published 70 days after the respective target date. For ETH two additional lines are included in the top row differentiating between intervals obtained from the old procedure before January 26, 2021 (n = 95), and from the new bootstrap approach afterward (n = 171; see model description in Section 2.2).</p

Supplementary text, figures and tables.

References to relevant code repositories and sources underlying Fig 2, additional remarks on the HZI approach, details on the handdling of temporal shifts, additional figures on temporal coherence, extension of the Cori method to a conditional negative binomial distribution. (PDF)</p

Overlay of different <i>R</i>_<i>t</i> estimates.

Estimates for the effective reproductive number of COVID-19 in Germany published by eight different research teams on July 10, 2021 (July 11, 2021, for HZI). Top: point estimates (only available for the last 15 weeks for epiforecasts); bottom: 95% uncertainty intervals (not available for HZI).</p

Scatter plot of mean generation time and corresponding standard deviation used by different research groups.

The red rhombus represents a “consensus value” chosen for further analysis, see Section 4.1. epiforecasts accounted for uncertainty in the generation time distribution by assuming independent normal priors for the mean and standard deviation; we illustrate the respective 95% uncertainty intervals by a cross. For context, we also show values used by public health agencies of other European countries. In the Netherlands (due to the transition to the Omicron variant) and Austria (due to a data update) the parameterization was revised. For details and references see Section B in S1 Text.</p

<i>R</i>_<i>t</i> estimates published between October 1, 2020, and December 10, 2020, and a consolidated estimate published 6 months later (epiforecasts: 15 weeks later).

Note that different time periods are used for Ilmenau and globalrt as these were not operated during the period shown for the other models. The consolidated ETH intervals are wider than those issued in real time due to a revision of methodology. The line type represents the label assigned to the estimate by the respective team: solid: “estimate”, dashed: “estimate based on partial data”, dotted: “forecast”. Shaded areas show 95% uncertainty intervals.</p