RESUMO
Emerging SARS-CoV-2 variants have shaped the second year of the COVID-19 pandemic and the public health discourse around effective control measures. Evaluating the public health threat posed by a new variant is essential for appropriately adapting response efforts when community transmission is detected. However, this assessment requires that a true comparison can be made between the new variant and its predecessors because factors other than the virus genotype may influence spread and transmission. In this study, we develop a framework that integrates genomic surveillance data to estimate the relative effective reproduction number (Rt) of co-circulating lineages. We use Connecticut, a state in the northeastern United States in which the SARS-CoV-2 variants B.1.1.7 and B.1.526 co-circulated in early 2021, as a case study for implementing this framework. We find that the Rt of B.1.1.7 was 6-10% larger than that of B.1.526 in Connecticut in the midst of a COVID-19 vaccination campaign. To assess the generalizability of this framework, we apply it to genomic surveillance data from New York City and observe the same trend. Finally, we use discrete phylogeography to demonstrate that while both variants were introduced into Connecticut at comparable frequencies, clades that resulted from introductions of B.1.1.7 were larger than those resulting from B.1.526 introductions. Our framework, which uses open-source methods requiring minimal computational resources, may be used to monitor near real-time variant dynamics in a myriad of settings.
RESUMO
An impressive number of COVID-19 data catalogs exist. None, however, are optimized for data science applications, e.g., inconsistent naming and data conventions, uneven quality control, and lack of alignment between disease data and potential predictors pose barriers to robust modeling and analysis. To address this gap, we generated a unified dataset that integrates and implements quality checks of the data from numerous leading sources of COVID-19 epidemiological and environmental data. We use a globally consistent hierarchy of administrative units to facilitate analysis within and across countries. The dataset applies this unified hierarchy to align COVID-19 case data with a number of other data types relevant to understanding and predicting COVID-19 risk, including hydrometeorological data, air quality, information on COVID-19 control policies, and key demographic characteristics.
