Intensity and lag-time of non-pharmaceutical interventions on COVID-19 dynamics in German hospitals

Abstract

IntroductionEvaluating the potential effects of non-pharmaceutical interventions on COVID-19 dynamics is challenging and controversially discussed in the literature. The reasons are manifold, and some of them are as follows. First, interventions are strongly correlated, making a specific contribution difficult to disentangle; second, time trends (including SARS-CoV-2 variants, vaccination coverage and seasonality) influence the potential effects; third, interventions influence the different populations and dynamics with a time delay.MethodsIn this article, we apply a distributed lag linear model on COVID-19 data from Germany from January 2020 to June 2022 to study intensity and lag time effects on the number of hospital patients and the number of prevalent intensive care patients diagnosed with polymerase chain reaction tests. We further discuss how the findings depend on the complexity of accounting for the seasonal trends.Results and discussionOur findings show that the first reducing effect of non-pharmaceutical interventions on the number of prevalent intensive care patients before vaccination can be expected not before a time lag of 5 days; the main effect is after a time lag of 10–15 days. In general, we denote that the number of hospital and prevalent intensive care patients decrease with an increase in the overall non-pharmaceutical interventions intensity with a time lag of 9 and 10 days. Finally, we emphasize a clear interpretation of the findings noting that a causal conclusion is challenging due to the lack of a suitable experimental study design.

Learning from the COVID-19 pandemic: a systematic review of mathematical vaccine prioritization models

“…Contrary to other review articles on this topic (Saadi et al, 2021;Liu and Lou, 2022;Noh et al, 2021;Thakkar and Spinardi, 2023), the focus of this review is on understanding the effect of modeling assumptions and parameters on policy recommendations. For example, while most studies agree that elderly and vulnerable should be vaccinated first due to their substantially higher infection fatality ratio, some studies suggest the opposite.…”

Learning from the COVID-19 pandemic: A systematic review of mathematical vaccine prioritization models

“…Contrary to other review articles on this topic ( Liu & Lou, 2022 ; Noh et al., 2021 ; Saadi et al., 2021 ; Thakkar and Spinardi, 2023 ), the focus of this review is on understanding the effect of modeling assumptions and parameters on policy recommendations. For example, while most studies agree that elderly and vulnerable should be vaccinated first due to their substantially higher infection fatality ratio, some studies suggest the opposite.…”

An Epidemic Model for Multi-Intervention Outbreaks

“…However, to our knowledge, these models do not allow for the implementation of multiple control efforts with differing effects on disease spread during the outbreak. Indeed, interventions can not only have differing effects on transmission, but also have varying time delays between implementation and impact, as seen with the COVID-19 pandemic (28)(29)(30). Further, not all interventions are sustainable and some may have diminished effects on pathogen transmission (31).…”