Estimating Asymptomatic and Symptomatic Transmission of the COVID-19 First Few Cases in Selenge Province, Mongolia.

BACKGROUND: Following the first locally transmitted case in Sukhbaatar soum, Selenge Province, we aimed to investigate the ultimate scale of the epidemic in the scenario of uninterrupted transmission. METHODS: This was a prospective case study following the locally modified WHO FFX cases generic protocol. A rapid response team collected data from November 14 to 29, 2020. We created a stochastic process to draw many transmission chains from this greater distribution to better understand and make inferences regarding the outbreak under investigation. RESULTS: The majority of the cases involved household transmissions (35, 52.2%), work transmissions (20, 29.9%), index (5, 7.5%), same apartment transmissions (2, 3.0%), school transmissions (2, 3.0%), and random contacts between individuals transmissions (1, 1.5%). The posterior means of the basic reproduction number of both the asymptomatic cases R 0 Asy $$ {R}_0^{Asy} $$ and the presymptomatic cases R 0 Pre $$ {R}_0^{Pre} $$ (1.35 [95% CrI 0.88-1.86] and 1.29 [95% CrI 0.67-2.10], respectively) were lower than that of the symptomatic cases (2.00 [95% Crl 1.38-2.76]). CONCLUSION: Our study highlights the heterogeneity of COVID-19 transmission across different symptom statuses and underscores the importance of early identification and isolation of symptomatic cases in disease control. Our approach, which combines detailed contact tracing data with advanced statistical methods, can be applied to other infectious diseases, facilitating a more nuanced understanding of disease transmission dynamics.

Analyses of publicly available Hungatella hathewayi genomes revealed genetic distances indicating they belong to more than one species.

Hungatella hathewayi has been observed to be a member of the gut microbiome. Unfortunately, little is known about this organism in spite of being associated with human fatalities; it is important to understand virulence mechanisms and epidemiological prospective to cause disease. In this study, a patient with chronic neurologic symptoms presented to the clinic with subsequent isolation of a strain with phenotypic characteristics suggestive of Clostridium difficile. However, whole-genome sequence found the organism to be H. hathewayi. Analysis including publicly available Hungatella genomes found substantial genomic differences as compared to the type strain, indicating this isolate was not C. difficile. We examined the whole-genome of Hungatella species and related genera, using comparative genomics to fully examine species identification and toxin production. Orthogonal phylogenetic using the 16S rRNA gene and entire genome analyses that included genome distance analyses using Genome-to-Genome Distance (GGDC), Average Nucleotide Identity (ANI), and a pan-genome analysis with inclusion of available public genomes determined the speciation to be Hungatella. Two clearly differentiated groups were identified, one including a reference H. hathewayi genome (strain DSM-13,479) and a second group that was determined to be H. effluvii, which included our clinical isolate. Also, some genomes reported as H. hathewayi were found to belong to other genera, including Clostridium and Faecalicatena. We show that the Hungatella species have an open pan-genome reflecting high genomic diversity. This study highlights the importance of correctly assigning taxonomic identification, particularly in disease-associated strains, to better understand virulence and therapeutic options.

Biological Machine Learning Combined with Campylobacter Population Genomics Reveals Virulence Gene Allelic Variants Cause Disease.

Highly dimensional data generated from bacterial whole-genome sequencing is providing an unprecedented scale of information that requires an appropriate statistical analysis framework to infer biological function from populations of genomes. The application of genome-wide association study (GWAS) methods is an appropriate framework for bacterial population genome analysis that yields a list of candidate genes associated with a phenotype, but it provides an unranked measure of importance. Here, we validated a novel framework to define infection mechanism using the combination of GWAS, machine learning, and bacterial population genomics that ranked allelic variants that accurately identified disease. This approach parsed a dataset of 1.2 million single nucleotide polymorphisms (SNPs) and indels that resulted in an importance ranked list of associated alleles of porA in Campylobacter jejuni using spatiotemporal analysis over 30 years. We validated this approach using previously proven laboratory experimental alleles from an in vivo guinea pig abortion model. This framework, termed µPathML, defined intestinal and extraintestinal groups that have differential allelic porA variants that cause abortion. Divergent variants containing indels that defeated automated annotation were rescued using biological context and knowledge that resulted in defining rare, divergent variants that were maintained in the population over two continents and 30 years. This study defines the capability of machine learning coupled with GWAS and population genomics to simultaneously identify and rank alleles to define their role in infectious disease mechanisms.