The Receptor Binding Domain of SARS-CoV-2 Lambda Variant Has a Better Chance Than the Delta Variant in Evading BNT162b2 COVID-19 mRNA Vaccine-Induced Humoral Immunity.

The SARS-CoV-2 Delta and Lambda variants had been named variants of concern (VOC) and variants of interest (VOI), respectively, by the World Health Organization (WHO). Both variants have two mutations in the spike receptor binding domain (RBD) region, with L452R and T478K mutations in the Delta variant, and L452Q and F490S mutations in the Lambda variant. We used surface plasmon resonance (SPR)-based technology to evaluate the effect of these mutations on human angiotensin-converting enzyme 2 (ACE2) and Bamlanivimab binding. The affinity for the RBD ligand, ACE2, of the Delta RBD is approximately twice as strong as that of the wild type RBD, an increase that accounts for the increased infectivity of the Delta variant. On the other hand, in spite of its amino acid changes, the Lambda RBD has similar affinity to ACE2 as the wild type RBD. The protective anti-wild type RBD antibody Bamlanivimab binds very poorly to the Delta RBD and not at all to the Lambda RBD. Nevertheless, serum antibodies from individuals immunized with the BNT162b2 vaccine were found to bind well to the Delta RBD, but less efficiently to the Lambda RBD in contrast. As a result, the blocking ability of ACE2 binding by serum antibodies was decreased more by the Lambda than the Delta RBD. Titers of sera from BNT162b2 mRNA vaccinated individuals dropped 3-fold within six months of vaccination regardless of whether the target RBD was wild type, Delta or Lambda. This may account partially for the fall off with time in the protective effect of vaccines against any variant.

Unsupervised PM2.5 anomalies in China induced by the COVID-19 epidemic.

To stop the spread of COVID-19 (2019 novel coronavirus), China placed lockdown on social activities across China since mid-January 2020. The government actions significantly affected emissions of atmospheric pollutants and unintentionally created a nationwide emission reduction scenario. In order to assess the impacts of COVID-19 on fine particular matter (PM2.5) levels, we developed a "conditional variational autoencoder" (CVAE) algorithm based on the deep learning to discern unsupervised PM2.5 anomalies in Chines cities during the COVID-19 epidemic. We show that the timeline of changes in number of cities with unsupervised PM2.5 anomalies is consistent with the timeline of WHO's response to COVID-19. Using unsupervised PM2.5 anomaly as a time node, we examine changes in PM2.5 before and after the time node to assess the response of PM2.5 to the COVID-19 lockdown. The rate of decrease of PM2.5 around the time node in northern China is 3.5 times faster than southern China, and decreasing PM2.5 levels in southern China is 3.5 times of that in northern China. Results were also compared with anomalous PM2.5 occurring in Chinese's Spring Festival from 2017 to 2019, PM2.5 anomalies during around Chinese New Year in 2020 differ significantly from 2017 to 2019. We demonstrate that this method could be used to detect the response of air quality to sudden changes in social activities.

Effect of Travel Restrictions of Wuhan City Against COVID-19: A Modified SEIR Model Analysis.

OBJECTIVE: Since December 2019, a new coronavirus viral was initially detected in Wuhan, China. Population migration increases the risk of epidemic transmission. Here, the objective of study is to estimate the output risk quantitatively and evaluate the effectiveness of travel restrictions of Wuhan city. METHODS: We proposed a modified susceptible-exposed-infectious-recovered (SEIR) dynamics model to predict the number of coronavirus disease 2019 (COVID-19) symptomatic and asymptomatic infections in Wuhan. And, subsequently, we estimated the export risk of COVID-19 epidemic from Wuhan to other provinces in China. Finally, we estimated the effectiveness of travel restrictions of Wuhan city quantitatively by the export risk on the assumption that the measure was postponed. RESULTS: The export risks of COVID-19 varied from Wuhan to other provinces of China. The peak of export risk was January 21-23, 2020. With the travel restrictions of Wuhan delayed by 3, 5, and 7 d, the export risk indexes will increase by 38.50%, 55.89%, and 65.63%, respectively. CONCLUSIONS: The results indicate that the travel restrictions of Wuhan reduced the export risk and delayed the overall epidemic progression of the COVID-19 epidemic in China. The travel restrictions of Wuhan city may provide a reference for the control of the COVID-19 epidemic all over the world.