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

Introduction: Evaluating 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. Methods: In 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 discussion: Our 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.

Thrombin generation test in patients with Covid-19

Background: TGA is a sensitive and specific method for diagnosing thrombin formation and one of the best methods correlating with bleeding or thrombotic events. TGA provides information about the total clotting potential, because thrombin generation does not stop after the formation of fibrin clots. Aim(s): To evaluate the parameters of the thrombin generation test in patients with Covid-19. Method(s): A retrospective study on the basis of the regional center for antithrombotic therapy of SBHI CCH 1 City Hospital named after E.E.Volosevich . Patients with confirmed Covid-19 (n = 100). Blood sampling to determine the parameters of the thrombin generation (kinetics) test (TGA) was performed on the 1st day of the study. The automatic formulation of the TGA method is carried out on an open-type coagulometer Ceveron alpha TGA. Genetic methods -Real- time PCR, Litech Company (Russia): Hemostasis system genes: FII G20210A, FGB G455A, PAI-1675 5G/4G. Result(s): It was found that Tlag (min) and tPeak (min) significantly decreased in patients on the 1st day of hospitalization. Correlation analysis of Tlag (min) with the genotype of F I (fibrinogen) rS = - 0.3 (p = 0.02), confirming that heterozygous polymorphism of the FI gene is associated with a decrease in Tlag (min). The presence of polymorphism in the PAI-1 gene is associated with a decrease in tPeak (min), i.e. in patients with polymorphism in PAI-1, the peak of thrombin is achieved faster (rS = 0.5;p = 0.03). The presence of polymorphism in the F I gene is associated with an increase in Peak (nmol/l), which is significantly higher in contrast to patients without polymorphism. The presence of polymorphisms in the PAI-1 and F I genes is associated with an increase in EPT (nmol/min: RS = 0.3;p = 0.04 and rS = 0.6;p = 0.02). Conclusion(s): The results of TGA in our study reflect data on an increase in the procoagulant potential of blood in patients with COVID-19.

COVID-19 mortality and exposure to airborne PM2.5: A lag time correlation.

COVID-19 has escalated into one of the most serious crises in the 21st Century. Given the rapid spread of SARS-CoV-2 and its high mortality rate, here we investigate the impact and relationship of airborne PM2.5 to COVID-19 mortality. Previous studies have indicated that PM2.5 has a positive relationship with the spread of COVID-19. To gain insights into the delayed effect of PM2.5 concentration (µgm-3) on mortality, we focused on the role of PM2.5 in Wuhan City in China and COVID-19 during the period December 27, 2019 to April 7, 2020. We also considered the possible impact of various meteorological factors such as temperature, precipitation, wind speed, atmospheric pressure and precipitation on pollutant levels. The results from the Pearson's correlation coefficient analyses reveal that the population exposed to higher levels of PM2.5 pollution are susceptible to COVID-19 mortality with a lag time of >18 days. By establishing a generalized additive model, the delayed effect of PM2.5 on the death toll of COVID-19 was verified. A negative correction was identified between temperature and number of COVID-19 deaths, whereas atmospheric pressure exhibits a positive correlation with deaths, both with a significant lag effect. The results from our study suggest that these epidemiological relationships may contribute to the understanding of the COVID-19 pandemic and provide insights for public health strategies.