Transmission dynamics of SARS-CoV-2 on the Diamond Princess uncovered using viral genome sequence analysis

On-demand, hospital-based, severe acute respiratory coronavirus virus 2 (SARS-CoV-2) genomic epidemiology to support nosocomial outbreak investigations: A prospective molecular epidemiology study

Objectives

We evaluated the added value of infection control-guided, on demand, and locally performed severe acute respiratory coronavirus virus 2 (SARS-CoV-2) genomic sequencing to support outbreak investigation and control in acute-care settings.

Design and setting

This 18-month prospective molecular epidemiology study was conducted at a tertiary-care hospital in Montreal, Canada. When nosocomial transmission was suspected by local infection control, viral genomic sequencing was performed locally for all putative outbreak cases. Molecular and conventional epidemiology data were correlated on a just-in-time basis to improve understanding of coronavirus disease 2019 (COVID-19) transmission and reinforce or adapt control measures.

Results

Between April 2020 and October 2021, 6 outbreaks including 59 nosocomial infections (per the epidemiological definition) were investigated. Genomic data supported 7 distinct transmission clusters involving 6 patients and 26 healthcare workers. We identified multiple distinct modes of transmission, which led to reinforcement and adaptation of infection control measures. Molecular epidemiology data also refuted (n = 14) suspected transmission events in favor of community acquired but institutionally clustered cases.

Conclusion

SARS-CoV-2 genomic sequencing can refute or strengthen transmission hypotheses from conventional nosocomial epidemiological investigations, and guide implementation of setting-specific control strategies. Our study represents a template for prospective, on site, outbreak-focused SARS-CoV-2 sequencing. This approach may become increasingly relevant in a COVID-19 endemic state where systematic sequencing within centralized surveillance programs is not available.

Trial registration

clinicaltrials.gov identifier: NCT05411562.

Transmission of SARS-CoV-2 Associated with Cruise Ship Travel: A Systematic Review

Background: Maritime and river travel may be associated with respiratory viral spread via infected passengers and/or crew and potentially through other transmission routes. The transmission models of SARS-CoV-2 associated with cruise ship travel are based on transmission dynamics of other respiratory viruses. We aimed to provide a summary and evaluation of relevant data on SARS-CoV-2 transmission aboard cruise ships, report policy implications, and highlight research gaps. Methods: We searched four electronic databases (up to 26 May 2022) and included studies on SARS-CoV-2 transmission aboard cruise ships. The quality of the studies was assessed based on five criteria, and relevant findings were reported. Results: We included 23 papers on onboard SARS-CoV-2 transmission (with 15 reports on different aspects of the outbreak on Diamond Princess and nine reports on other international cruises), 2 environmental studies, and 1 systematic review. Three articles presented data on both international cruises and the Diamond Princess. The quality of evidence from most studies was low to very low. Index case definitions were heterogeneous. The proportion of traced contacts ranged from 0.19 to 100%. Studies that followed up >80% of passengers and crew reported attack rates (AR) up to 59%. The presence of a distinct dose−response relationship was demonstrated by findings of increased ARs in multi-person cabins. Two studies performed viral cultures with eight positive results. Genomic sequencing and phylogenetic analyses were performed in individuals from three cruises. Two environmental studies reported PCR-positive samples (cycle threshold range 26.21−39.00). In one study, no infectious virus was isolated from any of the 76 environmental samples. Conclusion: Our review suggests that crowding and multiple persons per cabin were associated with an increased risk of transmission on cruise ships. Variations in design, methodology, and case ascertainment limit comparisons across studies and quantification of transmission risk. Standardized guidelines for conducting and reporting studies on cruise ships of acute respiratory infection transmission should be developed.

The Occurrence Birth-Death Process for combined-evidence analysis in macroevolution and epidemiology

Phylodynamic models generally aim at jointly inferring phylogenetic relationships, model parameters, and more recently, the number of lineages through time, based on molecular sequence data. In the fields of epidemiology and macroevolution, these models can be used to estimate, respectively, the past number of infected individuals (prevalence) or the past number of species (paleodiversity) through time. Recent years have seen the development of “total-evidence” analyses, which combine molecular and morphological data from extant and past sampled individuals in a unified Bayesian inference framework. Even sampled individuals characterized only by their sampling time, that is, lacking morphological and molecular data, which we call occurrences, provide invaluable information to estimate the past number of lineages. Here, we present new methodological developments around the fossilized birth–death process enabling us to (i) incorporate occurrence data in the likelihood function; (ii) consider piecewise-constant birth, death, and sampling rates; and (iii) estimate the past number of lineages, with or without knowledge of the underlying tree. We implement our method in the RevBayes software environment, enabling its use along with a large set of models of molecular and morphological evolution, and validate the inference workflow using simulations under a wide range of conditions. We finally illustrate our new implementation using two empirical data sets stemming from the fields of epidemiology and macroevolution. In epidemiology, we infer the prevalence of the coronavirus disease 2019 outbreak on the Diamond Princess ship, by taking into account jointly the case count record (occurrences) along with viral sequences for a fraction of infected individuals. In macroevolution, we infer the diversity trajectory of cetaceans using molecular and morphological data from extant taxa, morphological data from fossils, as well as numerous fossil occurrences. The joint modeling of occurrences and trees holds the promise to further bridge the gap between traditional epidemiology and pathogen genomics, as well as paleontology and molecular phylogenetics. [Birth–death model; epidemiology; fossils; macroevolution; occurrences; phylogenetics; skyline.]

NGS data vectorization, clustering, and finding key codons in SARS-CoV-2 variations

The rapid global spread and dissemination of SARS-CoV-2 has provided the virus with numerous opportunities to develop several variants. Thus, it is critical to determine the degree of the variations and in which part of the virus those variations occurred. Therefore, in this study, methods that could be used to vectorize the sequence data, perform clustering analysis, and visualize the results were proposed using machine learning methods. To conduct this study, a total of 224,073 cases of SARS-CoV-2 sequence data were collected through NCBI and GISAID, and the data were visualized using dimensionality reduction and clustering analysis models such as T-SNE and DBSCAN. The SARS-CoV-2 virus, which was first detected, was distinguished from different variations, including Omicron and Delta, in the cluster results. Furthermore, it was possible to examine which codon changes in the spike protein caused the variants to be distinguished using feature importance extraction models such as Random Forest or Shapely Value. The proposed method has the advantage of being able to analyse and visualize a large amount of data at once compared to the existing tree-based sequence data analysis. The proposed method was able to identify and visualize significant changes between the SARS-CoV-2 virus, which was first detected in Wuhan, China, in December 2019, and the newly formed mutant virus group. As a result of clustering analysis using sequence data, it was possible to confirm the formation of clusters among various variants in a two-dimensional graph, and by extracting the importance of variables, it was possible to confirm which codon changes played a major role in distinguishing variants. Furthermore, since the proposed method can handle a variety of data sequences, it can be used for all kinds of diseases, including influenza and SARS-CoV-2. Therefore, the proposed method has the potential to become widely used for the effective analysis of disease variations.