Carbon dioxide rises beyond acceptable safety levels in children under nose and mouth covering: Results of an experimental measurement study in healthy children.

Nose and mouth covering (NMC) has been made compulsory for children in many countries during the Covid-19 pandemic. We wanted to determine the average CO2 levels in inhaled air with NMC in children between age 6 and 17. We used short term measurements under surgical masks and FFP2 masks according to European norm EN 149, compared to baseline in an experimental, intra-individually controlled study over 25 min. CO2 content was measured every 15 s using an automated dual-wavelength infrared CO2 measurement device (G100, Geotech, Leamington Spa, UK) over 25 min in a short-term experimental setting, with children seated. After baseline measurement children were provided with two types of commonly worn NMC: surgical masks and FFP2--masks in randomized sequence for 3 min each. We kept ambient CO2-levels below 1000 parts per million (ppm) through frequent ventilation. We measured breathing frequency and pulse as potential physiological moderator variables. Forty-five children, 25 boys, 20 girls, with a mean age of 10.7 years (standard deviation 2.6) were measured. We measured 13,100 ppm (SD 380) under surgical mask and 13,900 ppm (SD 370) under FFP2 mask in inhaled air. A linear model with age as a covariate showed a highly significant effect of the condition (p < 1*10-9). We measured 2,700 ppm (SD 100) CO2 at pre-baseline and 2,800 ppm (SD 100) at post-baseline, a non-significant small difference. Appropriate contrasts revealed that the change was due to the masks only and the difference between the two types of masks was small and not significant. Wearing of NMC (surgical masks or FFP2- -masks) raises CO2 content in inhaled air quickly to a very high level in healthy children in a seated resting position that might be hazardous to children's health.

How bad data quality can turn a simulation into a dissimulation that shapes the future

During the spread of SARS-Cov-2, Germany imposed various restrictions, including the closure of schools on March 16 2020, and an extensive lockdown on March 23 2020. In this paper, we show how the influential simulation of the purported beneficial effects of this lockdown in Germany was based on wrong data, but nevertheless played a decisive role in shaping the future by allegedly producing evidence for the effectiveness of these measures, lending scientific credibility to policies. We point out that the evaluation of the success of such policies depends critically on data quality. Using publicly reported confirmed cases for the calculation of time series statistics is apt to produce misleading results because these data come with unknown variable time lags. Using data on incident cases, i.e., dates of the onset of symptoms, produces results that are much more reliable. Using this method demonstrates that previous analyses stating that the mitigation strategies of the German government were necessary and effective are indeed flawed. This in turn shows that model simulations and dissimulations are very close neighbors.