Skip to main content
SearchLoginLogin or Signup

Review #2: "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 #2: "Modeling the Impact of the Omicron Infection Wave in Germany"
1 of 2
key-enterThis Pub is a Review of
Modeling the impact of the Omicron infection wave in Germany
Description

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:

  • Reliable. The main study claims are generally justified by its methods and data. The results and conclusions are likely to be similar to the hypothetical ideal study. There are some minor caveats or limitations, but they would/do not change the major claims of the study. The study provides sufficient strength of evidence on its own that its main claims should be considered actionable, with some room for future revision.

***************************************

Review:

This paper used a parsimonious infectious disease model to describe the spread of Omicron (BA.1) in Germany during the first quarter of 2022. The model projections were in line with the actual Omicron waves observed in Germany. This is an interesting and well written paper with important findings. Some comments about the model:

1. The authors assumed that there is no explicit distinction between vaccinated and unvaccinated individuals. However, even with the waning of vaccine immunity, vaccinated individuals still have high protection against disease (VEP). So, does this assumption affect the prediction of the ICU admission and occupancy? The authors could explore this assumption in a sensitivity analysis.

2. The daily new cases are used to calibrate the model, but these data could be affected due to variation in volume and strategies of testing. Data on the daily positivity rate in addition to the daily new cases could be better for model calibration. The authors could also explore this in a sensitivity analysis.

3. What is the ascertainment rate assumed in the model calibration? Is this rate based on serological studies in Germany?

4. The model equations do not include the effect of reinfection. I believe that this assumption might affect the model predictions. The effectiveness of previous infection in preventing reinfection with the delta variant was robust. This protection against reinfection with the omicron variant was lower but still considerable based on recent real-world studies (e.g. https://www.nejm.org/doi/full/10.1056/nejmc2200133). The authors could comment (without the need to change the modeling).

5. The authors could provide a Bland-Altman plots to show the agreement between the model projections and the actual Omicron waves observed in Germany.

Minor comments:

1. The authors should clarify in the method section that the age structure was not considered in the model.

2. It is easier to label the figures by order of appearance in the main text.

3. There is issue in citing references (e.g. line 379).

Comments
0
comment

No comments here

Why not start the discussion?