Effect of Sociodemographic Factors on COVID-19 Incidence of 342 Cities in China: An Analysis Using Geographically Weighted Regression Model (preprint)

Background: The coronavirus disease 2019 (COVID-19) has spread quickly among the crowd and brought serious global impact since December 2019. However, there were considerable geographical disparities in the distribution of the COVID-19 incidence among different cities. In this study, we aimed to explore the effect of sociodemographic factors on COVID-19 incidence of 342 cities in China from the geographic perspective.Methods: The official surveillance data about the COVID-19 and sociodemographic information in the 342 cities of China were collected. Local GWPR model and global GLM Poisson regression model were compared to find the optimal one for analysis. Results: A significantly lower AICc in the GWPR model was shown compared with the GLM Poisson regression model (43218.9 in GWPR vs. 61953.0 in GLM, respectively). Any spatial auto-correlations of residuals were not found in the GWPR model (global Moran’s I = -0.005, p = 0.468), representing the spatial auto-correlation had been captured by the GWPR model. These cities with higher GDP, limited health resources, and shorter distance to Wuhan, were at higher risk for COVID-19. As population density increased, the incidence of COVID-19 decreased for most of the cities, except parts of the southeastern cities. Conclusions: There are potential effects of the sociodemographic factors on the COVID-19 incidence. Furthermore, the findings and methodology in our study could be used as a guide to other countries to help understand the local transmission of COVID-19 and tailor site-specific intervention strategies.

Engineering mesenchymal stem cells with neutralizing and anti- inflammation dual-capability against SARS-CoV-2 infection (preprint)

The emergence of the novel human coronavirus SARS-CoV-2 has caused a worldwide epidemic of coronavirus disease 2019 (COVID-19), which markedly affected the global health and economy. Both uncontrolled viral replication and proinflammatory cytokine storm can cause severe tissue damage in COVID-19 patients. SARS-CoV-2 utilizes angiotensin-converting enzyme 2 (ACE2) as its entry receptor. In this study, we generated ACE2 extracellular domain-Fc and scFv-IL6R-Fc fusion protein to differentially neutralize virus and temper cytokine storm. The hACE21-740-Fc fusion protein showed potent inhibitory effect on pseudotyped SARS-CoV-2 entry and good safety profile in mice. The scFv-IL6R-Fc showed strong blockade effect on IL-6 signal activation. In addition, we established a mesenchymal stem cells (MSCs)-based hACE21-740-Fc and scFv-IL6R-Fc delivery strategy, which provided a potential rapid option for urgent clinic therapeutic need of COVID-19 patients.