Reconstructing SARS-CoV-2 infection dynamics through the phylogenetic inference of unsampled sources of infection

Apollo: A comprehensive GPU-powered within-host simulator for viral evolution and infection dynamics across population, tissue, and cell

Modern sequencing instruments bring unprecedented opportunity to study within-host viral evolution in conjunction with viral transmissions between hosts. However, no computational simulators are available to assist the characterization of within-host dynamics. This limits our ability to interpret epidemiological predictions incorporating within-host evolution and to validate computational inference tools. To fill this need we developed Apollo, a GPU-accelerated, out-of-core tool for within-host simulation of viral evolution and infection dynamics across population, tissue, and cellular levels. Apollo is scalable to hundreds of millions of viral genomes and can handle complex demographic and population genetic models. Apollo can replicate real within-host viral evolution; accurately recapturing observed viral sequences from an HIV cohort derived from initial population-genetic configurations. For practical applications, using Apollo-simulated viral genomes and transmission networks, we validated and uncovered the limitations of a widely used viral transmission inference tool.

Genetic characteristics of SARS-CoV-2 virus variants observed upon three waves of the COVID-19 pandemic in Ukraine between February 2021-January 2022

The aim

of our study was to identify and characterize the SARS-CoV-2 variants in COVID-19 patients' samples collected from different regions of Ukraine to determine the relationship between SARS-CoV-2 phylogenetics and COVID-19 epidemiology.

Patients and methods

Samples were collected from COVID-19 patients during 2021 and the beginning of 2022 (401 patients). The SARS-CoV-2 genotyping was performed by parallel whole genome sequencing.

Results

The obtained SARS-CoV-2 genotypes showed that three waves of the COVID-19 pandemic in Ukraine were represented by three main variants of concern (VOC), named Alpha, Delta and Omicron; each VOC successfully replaced the earlier variant. The VOC Alpha strain was presented by one B.1.1.7 lineage, while VOC Delta showed a spectrum of 25 lineages that had different prevalence in 19 investigated regions of Ukraine. The VOC Omicron in the first half of the pandemic was represented by 13 lines that belonged to two different clades representing B.1 and B.2 Omicron strains. Each of the three epidemic waves (VOC Alpha, Delta, and Omicron) demonstrated their own course of disease, associated with genetic changes in the SARS-CoV-2 genome. The observed epidemiological features are associated with the genetic characteristics of the different VOCs, such as point mutations, deletions and insertions in the viral genome. A phylogenetic and transmission analysis showed the different mutation rates; there were multiple virus sources with a limited distribution between regions.

Conclusions

The evolution of SARS-CoV-2 virus and high levels of morbidity due to COVID-19 are still registered in the world. Observed multiple virus sourses with the limited distribution between regions indicates the high efficiency of the anti-epidemic policy pursued by the Ministry of Health of Ukraine to prevent the spread of the epidemic, despite the low level of vaccination of the Ukrainian population.

Changing genomic epidemiology of COVID-19 in long-term care facilities during the 2020-2022 pandemic, Washington State

BACKGROUND

Long-term care facilities (LTCFs) are vulnerable to disease outbreaks. Here, we jointly analyze SARS-CoV-2 genomic and paired epidemiologic data from LTCFs and surrounding communities in Washington state (WA) to assess transmission patterns during 2020-2022, in a setting of changing policy. We describe sequencing efforts and genomic epidemiologic findings across LTCFs and perform in-depth analysis in a single county.

METHODS

We assessed genomic data representativeness, built phylogenetic trees, and conducted discrete trait analysis to estimate introduction sizes over time, and explored selected outbreaks to further characterize transmission events.

RESULTS

We found that transmission dynamics among cases associated with LTCFs in WA changed over the course of the COVID-19 pandemic, with variable introduction rates into LTCFs, but decreasing amplification within LTCFs. SARS-CoV-2 lineages circulating in LTCFs were similar to those circulating in communities at the same time. Transmission between staff and residents was bi-directional.

CONCLUSIONS

Understanding transmission dynamics within and between LTCFs using genomic epidemiology on a broad scale can assist in targeting policies and prevention efforts. Tracking facility-level outbreaks can help differentiate intra-facility outbreaks from high community transmission with repeated introduction events. Based on our study findings, methods for routine tree building and overlay of epidemiologic data for hypothesis generation by public health practitioners are recommended. Discrete trait analysis added valuable insight and can be considered when representative sequencing is performed. Cluster detection tools, especially those that rely on distance thresholds, may be of more limited use given current data capture and timeliness. Importantly, we noted a decrease in data capture from LTCFs over time. Depending on goals for use of genomic data, sentinel surveillance should be increased or targeted surveillance implemented to ensure available data for analysis.

Retrospective, Observational Studies for Estimating Vaccine Effects on the Secondary Attack Rate of SARS-CoV-2

Coronavirus disease 2019 (COVID-19) vaccines are highly efficacious at preventing symptomatic infection, severe disease, and death. Most of the evidence that COVID-19 vaccines also reduce transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is based on retrospective, observational studies. Specifically, an increasing number of studies are evaluating vaccine effectiveness against the secondary attack rate of SARS-CoV-2 using data available in existing health-care databases or contact-tracing databases. Since these types of databases were designed for clinical diagnosis or management of COVID-19, they are limited in their ability to provide accurate information on infection, infection timing, and transmission events. We highlight challenges with using existing databases to identify transmission units and confirm potential SARS-CoV-2 transmission events. We discuss the impact of common diagnostic testing strategies, including event-prompted and infrequent testing, and illustrate their potential biases in estimating vaccine effectiveness against the secondary attack rate of SARS-CoV-2. We articulate the need for prospective observational studies of vaccine effectiveness against the SARS-CoV-2 secondary attack rate, and we provide design and reporting considerations for studies using retrospective databases.