Lag-time effects of vaccination on SARS-CoV-2 dynamics in German hospitals and intensive-care units

Bruno Enagnon Lokonon; Yvette Montcho; Paul Klingler; Chénangnon Frédéric Tovissodé; Romain Glèlè Kakaï; Martin Wolkewitz

doi:10.3389/fpubh.2023.1085991

Lag-time effects of vaccination on SARS-CoV-2 dynamics in German hospitals and intensive-care units

Front Public Health. 2023 Apr 11:11:1085991. doi: 10.3389/fpubh.2023.1085991. eCollection 2023.

Authors

Bruno Enagnon Lokonon^{1

2}, Yvette Montcho^{1

2}, Paul Klingler², Chénangnon Frédéric Tovissodé¹, Romain Glèlè Kakaï¹, Martin Wolkewitz²

Affiliations

¹ Laboratoire de Biomathématiques et d'Estimations Forestières, Université d'Abomey-Calavi, Cotonou, Benin.
² Faculty of Medicine and Medical Center, Institute of Medical Biometry and Statistics, University of Freiburg, Freiburg im Breisgau, Germany.

Abstract

Background: The Efficacy and effectiveness of vaccination against SARS-CoV-2 have clearly been shown by randomized trials and observational studies. Despite these successes on the individual level, vaccination of the population is essential to relieving hospitals and intensive care units. In this context, understanding the effects of vaccination and its lag-time on the population-level dynamics becomes necessary to adapt the vaccination campaigns and prepare for future pandemics.

Methods: This work applied a quasi-Poisson regression with a distributed lag linear model on German data from a scientific data platform to quantify the effects of vaccination and its lag times on the number of hospital and intensive care patients, adjusting for the influences of non-pharmaceutical interventions and their time trends. We separately evaluated the effects of the first, second and third doses administered in Germany.

Results: The results revealed a decrease in the number of hospital and intensive care patients for high vaccine coverage. The vaccination provides a significant protective effect when at least approximately 40% of people are vaccinated, whatever the dose considered. We also found a time-delayed effect of the vaccination. Indeed, the effect on the number of hospital patients is immediate for the first and second doses while for the third dose about 15 days are necessary to have a strong protective effect. Concerning the effect on the number of intensive care patients, a significant protective response was obtained after a lag time of about 15-20 days for the three doses. However, complex time trends, e.g. due to new variants, which are independent of vaccination make the detection of these findings challenging.

Conclusion: Our results provide additional information about the protective effects of vaccines against SARS-CoV-2; they are in line with previous findings and complement the individual-level evidence of clinical trials. Findings from this work could help public health authorities efficiently direct their actions against SARS-CoV-2 and be well-prepared for future pandemics.

Keywords: COVID-19; delayed effects; linear lag models; non-pharmaceutical interventions (NPIs); policy decisions; vaccination.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

COVID-19 Vaccines
COVID-19* / epidemiology
COVID-19* / prevention & control
Hospitals
Humans
Intensive Care Units
SARS-CoV-2*
Vaccination

Substances

COVID-19 Vaccines

Grants and funding

The authors acknowledge the support from the Humboldt Research Hub SEMCA funded by the German Federal Foreign Office with the support of the Alexander von Humboldt Foundation (AvH).