MAP3K19 regulatory variation in populations with African ancestry is associated with severe COVID-19 (preprint)

Ancestry impacts the likelihood of hospitalization due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV2). To identify ancestry-linked genetic risk variants associated with COVID-19 hospitalization, we performed an integrative analysis of two genome-wide association studies (GWASs) evaluating genetic variation among ancestrally diverse cohorts. We resolved four single nucleotide polymorphisms (SNPs) that are more frequent in COVID-19 hospitalized patients with non-European ancestry than the general population. Among them, the COVID-19 risk SNP rs16831827 shows the largest difference in allele frequency between African and European populations. The minor allele frequency of rs16831827*T was significantly higher in hospitalized COVID-19 patients with African ancestry. SNP rs16831827 also associates with COVID-19 hospitalization in an independent GWAS from the UK Biobank wherein both the hospitalized and control patients are entirely of African ancestry. rs16831827 is an expression quantitative trait locus (eQTL) of MAP3K19. Apart from rs16831827, two rare SNPs of MAP3K19, rs186150828 and rs192473276, were also significantly associated with COVID-19 hospitalization among African populations in the Regeneron COVID-19 hospitalization GWAS (p < 5x10-7). MAP3K19 expression is induced during ciliogenesis and most abundant in ciliated tissues including the lung and testis. Multiple COVID-19 related single-cell RNA sequencing data revealed that MAP3K19 is highly expressed in nasal multiciliated epithelial cells, nasopharyngeal ciliated cells, and airway ciliated cells. The COVID-19 hospitalization risk allele rs16831827*T is associated with reduced MAP3K19 expression. Hence, rs16831827 may increase the risk of severe COVID-19 by reducing baseline MAP3K19 expression.

The Impact of the COVID-19 Pandemic on Depressive Symptoms in China: A Longitudinal, Population-Based Study

Objectives: We aimed to examine how COVID-19 incidence is associated with depressive symptoms in China, whether the association is transient, and whether the association differs across groups. Methods: We used a longitudinal sample from 2018 to 2020 waves of the China Family Panel Study. We constructed COVID-19 incidence rates as the number of new cases per 100,000 population in respondents’ resident provinces in the past 7, 14, and 28 days when a respondent was surveyed. We performed linear or logistic regressions to examine the associations, and performed stratified analyses to explore the heterogeneity of the associations. Results: Our sample included 13,655 adults. The 7-day incidence rate was positively associated with the CES-D score (coef. = 2.551, 95% CI: 1.959–3.142), and likelihood of being more depressed (adjusted odds ratio = 6.916, 95% CI: 4.715–10.144). The associations were larger among those with less education, pre-existing depression, or chronic conditions. We did not find any significant association between the 14- or 28-day local incidence rates and depressive symptoms. Conclusion: The impact of COVID-19 incidence on mental health in China’s general population was statistically significant and moderate in magnitude and transient. Disadvantaged groups experienced higher increases in depressive symptoms.

Diffusion characteristics and risk assessment of respiratory pollutants in high-speed train carriages

Due to the density of people in the cabins of high-speed trains, and the development of the transportation network, respiratory diseases are easily transmitted and spread to various cities. In the context of the epidemic, studying the diffusion characteristics of respiratory pollutants in the cabin and the distribution of passengers is of great significance to the protection of the health of passengers. Based on the theory of computational fluid dynamics (CFD), a high-speed train cabin model with a complete air supply duct is established. For both summer and winter conditions, the characteristics of the flow field and temperature field in the cabin, under full load capacity, and the diffusion characteristics of respiratory pollutants under half load capacity are studied. Taking COVID-19 as an example, the probability of passengers being infected was evaluated. Furthermore, research on the layout of this type of cabin was carried out. The results show that it is not favorable to exhaust air at both ends, as this is likely to cause large-area diffusion of pollutants. The air barrier formed in the aisle can assist the ventilation system, which can prevent pollutants from spreading from one side to the other. Along the length of the train, the respiratory pollutants of passengers almost always spread only forward or backward. Moreover, when the distance between passengers and the infector exceeds one row, the probability of being infected does not decrease significantly. In order to reduce the probability of cross infection, and take into account the passenger efficiency of the railway, passengers in the same row should be separated from each other, and it is best to ride on both sides of the aisle. In the same column, passengers only need to be separated by one row, and it is not recommended to use the middle of the carriage. The number of passengers in the front and back half of the cabin should also be roughly the same.

Passenger-to-Car Assignment Optimization Model for High-Speed Railway with Risk of COVID-19 Transmission Consideration

Narrow and closed spaces like high-speed train cabins are at great risk for airborne infectious disease transmission. With the threat of COVID-19 as well as other potential contagious diseases, it is necessary to protect passengers from infection. Except for the traditional preventions such as increasing ventilation or wearing masks, this paper proposes a novel measurement that optimizes passenger-to-car assignment schemes to reduce the infection risk for high-speed railway passengers. First, we estimated the probability of an infected person boarding the train at any station. Once infectors occur, the non-steady-state Wells–Riley equation is used to model the airborne transmission intercar cabin. The expected number of susceptible passengers infected on the train can be calculated, which is the so-called overall infection risk. The model to minimize overall infection risk, as a pure integer quadratic programming problem, is solved by LINGO software and tested on several scenarios compared with the classical sequential and discrete assignment strategies used in China. The results show that the proposed model can reduce 67.6% and 56.8% of the infection risk in the base case compared to the sequential and discrete assignment, respectively. In other scenarios, the reduction lies mostly between 10% and 90%. The optimized assignment scheme suggests that the cotravel itinerary among passengers from high-risk and low-risk areas should be reduced, as well as passengers with long- and short-distance trips. Sensitivity analysis shows that our model works better when the incidence is higher at downstream or low-flow stations. Increasing the number of cars and car service capacity can also improve the optimization effect. Moreover, the model is applicable to other epidemics since it is insensitive to the Wells–Riley equation parameters. The results can provide a guideline for railway operators during the post-COVID-19 and other epidemic periods. [ FROM AUTHOR] Copyright of Mathematical Problems in Engineering is the property of Hindawi Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Evaluations of serological test in the diagnosis of 2019 novel coronavirus (SARS-CoV-2) infections during the COVID-19 outbreak (preprint)

The ongoing SARS-CoV-2 outbreak has killed over twenty-one thousand and sickened over four hundred thousand people worldwide, posing a great challenge to global public health. A sensitive and accurate diagnosis method will substantially help to control disease expansion. Here, we developed a chemiluminescence-immunoassay method based on the recombinant nucleocapsid antigen and the magnetic beads for diagnosis of SARS-CoV-2 infections and surveillance of antibody changing pattern. Serums from 29 healthy individuals, 51 tuberculosis patients, and 79 SARS-CoV-2 confirmed patients were employed to evaluate the performance of this approach. Compared to the IgM testing, the IgG testing was more reliable in which it identified 65 SARS-CoV-2 infections from the 79 confirmed patients and only two false-positive cases from the 80 control group with a sensitivity and specificity reaching 82.28% and 97.5%, respectively. However, only a slight difference (not statistically significant) in the detected cases of SARS-CoV-2 infections was observed between the IgM and IgG testing manner in patients at a different time of onset of disease. A performance comparison between an ELISA kit using the same nucleocapsid antigen and our chemiluminescence method was undertaken. The same false-positive cases were seen in both methods from the paired control group, while ELISA kit can only detect half of the SARS-CoV-2 infections from paired SARS-CoV-2 confirmed patients group than that of the chemiluminescence method, indicating a higher performance for the chemiluminescence-immunoassay approach. Together, our studies provide a useful and valuable serological testing tool for the diagnosis of SARS-CoV-2 infections in the community.