Environmental factors and mobility predict COVID-19 seasonality (preprint)

BackgroundWe recently showed that seasonal patterns of COVID-19 incidence and Influenza-Like Illnesses incidence are highly similar, in a country in the temperate climate zone, such as the Netherlands (latitude: 52{degrees}N). We hypothesize that in The Netherlands the same environmental factors and mobility trends that are associated with the seasonality of flu-like illnesses are predictors of COVID-19 seasonality as well. MethodsWe used meteorological, pollen/hay fever and mobility data from the Netherlands with its 17.4 million inhabitants. For the reproduction number of COVID-19 (Rt), we used data from the Dutch State Institute for Public Health. This Rt metric is a daily estimate that is based on positive COVID-19 tests in the Netherlands in hospitals and municipalities. For all datasets we selected the overlapping period of COVID-19 and the first allergy season: from February 17, 2020 till September 21, 2020 (total number of measurements: n = 218), the end of pollen season. Backward stepwise multiple linear regression was used to develop an environmental prediction model of the Rt of COVID-19. Next, we studied whether adding mobility trends to an environmental model improved the predictive power. ResultsBy means of stepwise backward multiple linear regression four highly significant (p value < 0.01) predictive factors are selected in our combined model: temperature, solar radiation, hay fever incidence, and mobility to indoor recreation locations. Our combined model explains 87.5% of the variance of Rt of COVID-19 and has a good and highly significant fit: F(4, 213) = 374.2, p-value < 0.00001. The combined model had a better overall predictive performance compared to a solely environmental model, which still explains 77.3% of the variance of Rt, and a good and highly significant fit: F(4, 213) = 181.3, p < 0.00001. ConclusionsWe conclude that the combined mobility and environmental model can adequately predict the seasonality of COVID-19 in a country with a temperate climate like the Netherlands. In this model higher solar radiation, higher temperature and hay fever are related to lower COVID-19 reproduction, and mobility to indoor recreation locations with increased COVID-19 spread. HighlightsO_LIThe seasonality of COVID-19 can be well-explained by environmental factors and mobility. C_LIO_LIA combined model explains 87.5% of the variance of the reproduction number of COVID-19 C_LIO_LIInhibitors of the reproduction number of COVID-19 are higher solar radiation, and seasonal allergens/allergies. C_LIO_LIMobility, especially to indoor recreation locations, increases the reproduction number of COVID-19. C_LIO_LITemperature has no direct effect on the reproduction number of COVID-19, but affects mobility and seasonal allergens. C_LIO_LIAdding mobility trends to an environmental model improves the predictive value regarding the reproduction number of COVID-19. C_LI

Absence of rapid T cell control corresponds with delayed viral clearance in hospitalised COVID-19 patients (preprint)

SARS-CoV-2 viral load is associated with disease severity. A better understanding of immunological mechanisms involved in viral clearance is crucial to guide new therapeutic strategies. Here, we studied the timing of viral clearance in relation to 122 immune parameters in 150 hospitalized COVID-19 patients. Delayed viral clearance was associated with more severe disease, which occurred after the virus had been cleared in most cases. Paradoxically, delayed viral clearance was associated with over time higher maximum levels of SARS-CoV-2 specific IgG, IgA, and neutralizing antibodies, increased numbers of eosinophils, monocytes, and pro-inflammatory cyto-/chemokines. In contrast, early viral clearance and less critical illness correlated with higher levels of CD4 + and CD8 + T cells. Collectively, our data show that absence of rapid T cell control corresponds with delayed clearance and aberrant antibody and cytokine profiles. Viral clearance often precedes critical illness, which suggests immunopathology as underlying mechanism. These data can guide treatment strategies.