ABSTRACT
Omicron and its subvariants have steadily gained greater capability of immune escape compared to other variants of concern, resulting in an increased incidence of reinfections even among vaccinated individuals. We evaluated the antibody response to Omicron BA.1, BA.2, and BA.4/5 in US military members vaccinated with the primary 2-dose series of Moderna mRNA-1273 in a cross-sectional study. While nearly all vaccinated participants had sustained spike (S) IgG and neutralizing antibodies (ND50) to the ancestral strain, only 7.7% participants had detectable ND50 to Omicron BA.1 at 8 months postvaccination. The neutralizing antibody response to BA.2 and BA.5 was similarly reduced. The reduced antibody neutralization of Omicron correlated with the decreased antibody binding to the receptor-binding domain. The participants' seropositivity to the nuclear protein positively correlated with ND50. Our data emphasizes the need for continuous vigilance in monitoring for emerging variants and the need to identify potential alternative targets for vaccine design.
Subject(s)
COVID-19 , Military Personnel , Humans , 2019-nCoV Vaccine mRNA-1273 , Antibody Formation , Cross-Sectional Studies , SARS-CoV-2/genetics , Antibodies, Neutralizing , Antibodies, ViralABSTRACT
The outbreak of coronavirus disease (COVID-19) has swept across more than 180 countries and territories since late January 2020. As a worldwide emergency response, governments have implemented various measures and policies, such as self-quarantine, travel restrictions, work from home, and regional lockdown, to control the spread of the epidemic. These countermeasures seek to restrict human mobility because COVID-19 is a highly contagious disease that is spread by human-to-human transmission. Medical experts and policymakers have expressed the urgency to effectively evaluate the outcome of human restriction policies with the aid of big data and information technology. Thus, based on big human mobility data and city POI data, an interactive visual analytics system called Epidemic Mobility (EpiMob) was designed in this study. The system interactively simulates the changes in human mobility and infection status in response to the implementation of a certain restriction policy or a combination of policies (e.g., regional lockdown, telecommuting, screening). Users can conveniently designate the spatial and temporal ranges for different mobility restriction policies. Then, the results reflecting the infection situation under different policies are dynamically displayed and can be flexibly compared and analyzed in depth. Multiple case studies consisting of interviews with domain experts were conducted in the largest metropolitan area of Japan (i.e., Greater Tokyo Area) to demonstrate that the system can provide insight into the effects of different human mobility restriction policies for epidemic control, through measurements and comparisons.
ABSTRACT
Lockdown measures have been a "panacea" for pandemic control but also a violent "poison" for economies. Lockdown policies strongly restrict human mobility but mobility reduce does harm to economics. Governments meet a thorny problem in balancing the pros and cons of lockdown policies, but lack comprehensive and quantified guides. Based on millions of financial transaction records, and billions of mobility data, we tracked spatio-temporal business networks and human daily mobility, then proposed a high-resolution two-sided framework to assess the epidemiological performance and economic damage of different lockdown policies. We found that the pandemic duration under the strictest lockdown is less about two months than that under the lightest lockdown, which makes the strictest lockdown characterize both epidemiologically and economically efficient. Moreover, based on the two-sided model, we explored the spatial lockdown strategy. We argue that cutting off intercity commuting is significant in both epidemiological and economical aspects, and finally helped governments figure out the Pareto optimal solution set of lockdown strategy.