ABSTRACT
The rapid emergence of new SARS-CoV-2 variants raises a number of public health questions including the capability of diagnostic tests to detect new strains, the efficacy of vaccines, and how to map the geographical distribution of variants to better understand patterns of transmission and possible load on healthcare resources. Next-Generation Sequencing (NGS) is the primary method for detecting and tracing the emergence of new variants, but it is expensive, and it can take weeks before sequence data is available in public repositories. Here, we describe a Polymerase Chain Reaction (PCR)-based genotyping approach that is significantly less expensive, accelerates reporting on SARS-CoV-2 variants, and can be implemented in any testing lab performing PCR. Specific Single Nucleotide Polymorphisms (SNPs) and indels are identified that have high positive percent agreement (PPA) and negative percent agreement (NPA) compared to NGS for the major genotypes that circulated in 2021. Using a 48-marker panel, testing on 1,128 retrospective samples yielded a PPA and NPA in the 96.3 to 100% and 99.2 to 100% range, respectively, for the top 10 most prevalent lineages. The effect on PPA and NPA of reducing the number of panel markers was also explored. In addition, with the emergence of Omicron, we also developed an Omicron genotyping panel that distinguishes the Delta and Omicron variants using four (4) highly specific SNPs. Data from testing demonstrates the capability to use the panel to rapidly track the growing prevalence of the Omicron variant in the United States in December 2021.
ABSTRACT
The COVID-19 pandemic has caused a massive economic shock across the world due to business interruptions and shutdowns from social-distancing measures. To evaluate the socio-economic impact of COVID-19 on individuals, a micro-economic model is developed to estimate the direct impact of distancing on household income, savings, consumption, and poverty. The model assumes two periods: a crisis period during which some individuals experience a drop in income and can use their precautionary savings to maintain consumption; and a recovery period, when households save to replenish their depleted savings to pre-crisis level. The San Francisco Bay Area is used as a case study, and the impacts of a lockdown are quantified, accounting for the effects of unemployment insurance (UI) and the CARES Act federal stimulus. Assuming a shelter-in-place period of three months, the poverty rate would temporarily increase from 17.1% to 25.9% in the Bay Area in the absence of social protection, and the lowest income earners would suffer the most in relative terms. If fully implemented, the combination of UI and CARES could keep the increase in poverty close to zero, and reduce the average recovery time, for individuals who suffer an income loss, from 11.8 to 6.7 months. However, the severity of the economic impact is spatially heterogeneous, and certain communities are more affected than the average and could take more than a year to recover. Overall, this model is a first step in quantifying the household-level impacts of COVID-19 at a regional scale. This study can be extended to explore the impact of indirect macroeconomic effects, the role of uncertainty in households' decision-making and the potential effect of simultaneous exogenous shocks (e.g., natural disasters).