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Review #1: "Modeling the Impact of the Omicron Infection Wave in Germany"

Reviewers found that this paper detailed the specifications of the model well and could be informative, though there was concern over the accuracy of various parameters used.

Published onSep 07, 2022
Review #1: "Modeling the Impact of the Omicron Infection Wave in Germany"
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key-enterThis Pub is a Review of
Modeling the impact of the Omicron infection wave in Germany

BACKGROUNDIn November 2021, the first case of SARS-CoV-2 “variant of concern” (VOC) B.1.1.529 (“Omicron”) was reported in Germany, alongside global reports of reduced vaccine efficacy against infections with this variant. The potential threat posed by the rapid spread of this variant in Germany remained, at the time, elusive.METHODSWe developed a variant-dependent population-averaged susceptible-exposed-infected-recovered (SEIR) infectious disease model. The model was calibrated on the observed fixation dynamics of the Omicron variant in December 2021, and allowed us to estimate potential courses of upcoming infection waves in Germany, focusing on the corresponding burden on intensive care units (ICUs) and the efficacy of contact reduction strategies.RESULTSA maximum median incidence of approximately 300 000 (50% PI in 1000: [181,454], 95% PI in 1000: [55,804]) reported cases per day was expected with the median peak occurring in the mid of February 2022, reaching a cumulative Omicron case count of 16.5 million (50% PI in mio: [11.4, 21.3], 95% PI in mio: [4.1, 27.9]) until Apr 1, 2022. These figures were in line with the actual Omicron waves that were subsequently observed in Germany with respective peaks occurring in mid February (peak: 191k daily new cases) and mid March (peak: 230k daily new cases), cumulatively infecting 14.8 million individuals during the study period. The model peak incidence was observed to be highly sensitive to variations in the assumed generation time and decreased with shorter generation time. Low contact reductions were expected to lead to containment. Early, strict, and short contact reductions could have led to a strong “rebound” effect with high incidences after the end of the respective non-pharmaceutical interventions. Higher vaccine uptake would have led to a lower outbreak size. To ensure that ICU occupancy remained below maximum capacity, a relative risk of requiring ICU care of 10%–20% was necessary (after infection with Omicron vs. infection with Delta).CONCLUSIONSWe expected a large cumulative number of infections with the VOC Omicron in Germany with ICU occupancy likely remaining below capacity nevertheless, even without additional non-pharmaceutical interventions. Our estimates were in line with the retrospectively observed waves. The results presented here informed legislation in Germany. The methodology developed in this study might be used to estimate the impact of future waves of COVID-19 or other infectious diseases.

RR:C19 Evidence Scale rating by reviewer:

  • Potentially informative. The main claims made are not strongly justified by the methods and data, but may yield some insight. The results and conclusions of the study may resemble those from the hypothetical ideal study, but there is substantial room for doubt. Decision-makers should consider this evidence only with a thorough understanding of its weaknesses, alongside other evidence and theory. Decision-makers should not consider this actionable, unless the weaknesses are clearly understood and there is other theory and evidence to further support it.



The manuscript proposes a variant-dependent population-averaged susceptible exposed infected recovered epidemiological model to model the Omicron wave in Germany in early 2022. The authors perform various permutations of the assumptions that they make on the latency and contagion period, vaccine efficacy and uptake, along with reductions in the contact rate in the population. The authors provide detailed explanations of their proposed model along with numerical results from their studies. Overall, the results seems potentially informative with some modifications to the study.

At various points throughout the manuscript, the authors make assumptions that are not necessarily supported by the data. Primarily, they chose mean latency and mean infectious period for the Delta and Omicron that are not supported in the literature. While some of the results look promising, infeasible choices for these values casts suspicion on the results. Moreover, it does not appear that the values were varied between simulations with the same model settings. This can lead to poorly fit models to the available data, as is the case here for most of the numerical results. An alternative course is to sample latency and infectious times from a distribution, learned as part of a previous study, and examine how the interesting parts of the present work interact with the disease outbreak.

Furthermore the authors mention at several point throughout the paper that their assumptions can bias their results to be overly pessimistic, i.e., that the predicted infectious wave will be smaller than predicted by the model. However, their numerical results were not terribly far off from the reality of the Omicron wave in Germany. As the authors note, “model peak height strongly depended on variations in the assumed generation time”. The authors also assume a constant 50% rate of reporting of active infections. This assumption has correlations with the model parameters as well as a downward bias on the predicted number of active cases. Reporting of one in two active cases seems pessimistic, it would be interesting to see how varying this parameter impacted the resulting infection curves.

I think the most interesting part of the work examines different settings of vaccine efficacy and the result on the peak and duration of Omicron waves. To strengthen the work, I suggest replacing constant latency and infectious periods with distributional forms, and investigate the Omicron wave by looking at the contact reduction and vaccine aspects of the pandemic. These considerations have great implications for public health agencies (not to lessen the impact of modeling infections), in controlling the spread of a disease and lessening its impact on a community.

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