Genomic investigations of unexplained acute hepatitis in children

Since its first identification in Scotland, over 1,000 cases of unexplained paediatric hepatitis in children have been reported worldwide, including 278 cases in the UK1. Here we report an investigation of 38 cases, 66 age-matched immunocompetent controls and 21 immunocompromised comparator participants, using a combination of genomic, transcriptomic, proteomic and immunohistochemical methods. We detected high levels of adeno-associated virus 2 (AAV2) DNA in the liver, blood, plasma or stool from 27 of 28 cases. We found low levels of adenovirus (HAdV) and human herpesvirus 6B (HHV-6B) in 23 of 31 and 16 of 23, respectively, of the cases tested. By contrast, AAV2 was infrequently detected and at low titre in the blood or the liver from control children with HAdV, even when profoundly immunosuppressed. AAV2, HAdV and HHV-6 phylogeny excluded the emergence of novel strains in cases. Histological analyses of explanted livers showed enrichment for T cells and B lineage cells. Proteomic comparison of liver tissue from cases and healthy controls identified increased expression of HLA class 2, immunoglobulin variable regions and complement proteins. HAdV and AAV2 proteins were not detected in the livers. Instead, we identified AAV2 DNA complexes reflecting both HAdV-mediated and HHV-6B-mediated replication. We hypothesize that high levels of abnormal AAV2 replication products aided by HAdV and, in severe cases, HHV-6B may have triggered immune-mediated hepatic disease in genetically and immunologically predisposed children

Investigation of hospital discharge cases and SARS-CoV-2 introduction into Lothian care homes

Background The first epidemic wave of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) in Scotland resulted in high case numbers and mortality in care homes. In Lothian, over one-third of care homes reported an outbreak, while there was limited testing of hospital patients discharged to care homes. Aim To investigate patients discharged from hospitals as a source of SARS-CoV-2 introduction into care homes during the first epidemic wave. Methods A clinical review was performed for all patients discharges from hospitals to care homes from 1st March 2020 to 31st May 2020. Episodes were ruled out based on coronavirus disease 2019 (COVID-19) test history, clinical assessment at discharge, whole-genome sequencing (WGS) data and an infectious period of 14 days. Clinical samples were processed for WGS, and consensus genomes generated were used for analysis using Cluster Investigation and Virus Epidemiological Tool software. Patient timelines were obtained using electronic hospital records. Findings In total, 787 patients discharged from hospitals to care homes were identified. Of these, 776 (99%) were ruled out for subsequent introduction of SARS-CoV-2 into care homes. However, for 10 episodes, the results were inconclusive as there was low genomic diversity in consensus genomes or no sequencing data were available. Only one discharge episode had a genomic, time and location link to positive cases during hospital admission, leading to 10 positive cases in their care home. Conclusion The majority of patients discharged from hospitals were ruled out for introduction of SARS-CoV-2 into care homes, highlighting the importance of screening all new admissions when faced with a novel emerging virus and no available vaccine

SARS-CoV-2 Omicron is an immune escape variant with an altered cell entry pathway

Vaccines based on the spike protein of SARS-CoV-2 are a cornerstone of the public health response to COVID-19. The emergence of hypermutated, increasingly transmissible variants of concern (VOCs) threaten this strategy. Omicron (B.1.1.529), the fifth VOC to be described, harbours multiple amino acid mutations in spike, half of which lie within the receptor-binding domain. Here we demonstrate substantial evasion of neutralization by Omicron BA.1 and BA.2 variants in vitro using sera from individuals vaccinated with ChAdOx1, BNT162b2 and mRNA-1273. These data were mirrored by a substantial reduction in real-world vaccine effectiveness that was partially restored by booster vaccination. The Omicron variants BA.1 and BA.2 did not induce cell syncytia in vitro and favoured a TMPRSS2-independent endosomal entry pathway, these phenotypes mapping to distinct regions of the spike protein. Impaired cell fusion was determined by the receptor-binding domain, while endosomal entry mapped to the S2 domain. Such marked changes in antigenicity and replicative biology may underlie the rapid global spread and altered pathogenicity of the Omicron variant

Genomic epidemiology of SARS-CoV-2 in a UK university identifies dynamics of transmission

AbstractUnderstanding SARS-CoV-2 transmission in higher education settings is important to limit spread between students, and into at-risk populations. In this study, we sequenced 482 SARS-CoV-2 isolates from the University of Cambridge from 5 October to 6 December 2020. We perform a detailed phylogenetic comparison with 972 isolates from the surrounding community, complemented with epidemiological and contact tracing data, to determine transmission dynamics. We observe limited viral introductions into the university; the majority of student cases were linked to a single genetic cluster, likely following social gatherings at a venue outside the university. We identify considerable onward transmission associated with student accommodation and courses; this was effectively contained using local infection control measures and following a national lockdown. Transmission clusters were largely segregated within the university or the community. Our study highlights key determinants of SARS-CoV-2 transmission and effective interventions in a higher education setting that will inform public health policy during pandemics.</jats:p

Replicating superspreader dynamics with birth-death phylodynamic models

Infectious disease outbreaks often exhibit superspreader dynamics with a small fraction of individuals responsible for a large proportion of transmission. Heterogeneity impacts outbreak risk and control efficacy Standard models typically neglect differential transmission, potentially introducing bias. Multi-type models incorporate population heterogeneity with transmissibility typically linked to symptomatic statu

Replicating superspreader dynamics with birth-death phylodynamic models

Infectious disease outbreaks often exhibit superspreader dynamics with a small fraction of individuals responsible for a large proportion of transmission. Heterogeneity impacts outbreak risk and control efficacy Standard models typically neglect differential transmission, potentially introducing bias. Multi-type models incorporate population heterogeneity with transmissibility typically linked to symptomatic statu

Reproducibility of COVID-era infectious disease models

Infectious disease modelling has been prominent throughout the COVID-19 pandemic, helping to understand the virus’ transmission dynamics and inform response policies. Given their potential importance and translational impact, we evaluated the computational reproducibility of infectious disease modelling articles from the COVID era. We found that four out of 100 randomly sampled studies released between January 2020 and August 2022 could be completely computationally reproduced using the resources provided (e.g., code, data, instructions) whilst a further eight were partially reproducible. For the 100 most highly cited articles from the same period we found that 11 were completely reproducible with a further 22 partially reproducible. Reflecting on our experience, we discuss common issues affecting computational reproducibility and how these might be addressed

A data science pipeline applied to Australia's 2022 COVID-19 Omicron waves

The field of software engineering is advancing at astonishing speed, with packages now available to support many stages of data science pipelines. These packages can support infectious disease modelling to be more robust, efficient and transparent, which has been particularly important during the COVID-19 pandemic. We developed a package for the construction of infectious disease models, integrated it with several open-source libraries and applied this composite pipeline to multiple data sources that provided insights into Australia's 2022 COVID-19 epidemic. We aimed to identify the key processes relevant to COVID-19 transmission dynamics and thereby develop a model that could quantify relevant epidemiological parameters.The pipeline's advantages include markedly increased speed, an expressive application programming interface, the transparency of open-source development, easy access to a broad range of calibration and optimisation tools and consideration of the full workflow from input manipulation through to algorithmic generation of the publication materials. Extending the base model to include mobility effects slightly improved model fit to data, with this approach selected as the model configuration for further epidemiological inference. Under our assumption of widespread immunity against severe outcomes from recent vaccination, incorporating an additional effect of the main vaccination programs rolled out during 2022 on transmission did not further improve model fit. Our simulations suggested that one in every two to six COVID-19 episodes were detected, subsequently emerging Omicron subvariants escaped 30–60% of recently acquired natural immunity and that natural immunity lasted only one to eight months on average. We documented our analyses algorithmically and present our methods in conjunction with interactive online code notebooks and plots.We demonstrate the feasibility of integrating a flexible domain-specific syntax library with state-of-the-art packages in high performance computing, calibration, optimisation and visualisation to create an end-to-end pipeline for infectious disease modelling. We used the resulting platform to demonstrate key epidemiological characteristics of the transition from the emergency to the endemic phase of the COVID-19 pandemic

Agent-based modelling of Mycobacterium tuberculosis transmission: a systematic review

Abstract Background Traditional epidemiological models tend to oversimplify the transmission dynamics of Mycobacterium tuberculosis (M.tb) to replicate observed tuberculosis (TB) epidemic patterns. This has led to growing interest in advanced methodologies like agent-based modelling (ABM), which can more accurately represent the complex heterogeneity of TB transmission. Objectives To better understand the use of agent-based models (ABMs) in this context, we conducted a systematic review with two main objectives: (1) to examine how ABMs have been employed to model the intricate heterogeneity of M.tb transmission, and (2) to identify the challenges and opportunities associated with implementing ABMs for M.tb. Search methods We conducted a systematic search following PRISMA guidelines across four databases (MEDLINE, EMBASE, Global Health, and Scopus), limiting our review to peer-reviewed articles published in English up to December 2022. Data were extracted by two investigators using a standardized extraction tool. Prospero registration: CRD42022380580. Selection criteria Our review included peer-reviewed articles in English that implement agent-based, individual-based, or microsimulation models of M.tb transmission. Models focusing solely on in-vitro or within-host dynamics were excluded. Data extraction targeted the methodological, epidemiological, and computational characteristics of ABMs used for TB transmission. A risk of bias assessment was not conducted as the review synthesized modelling studies without pooling epidemiological data. Results Our search initially identified 5,077 studies, from which we ultimately included 26 in our final review after exclusions. These studies varied in population settings, time horizons, and model complexity. While many incorporated population heterogeneity and household structures, some lacked essential features like spatial structures or economic evaluations. Only eight studies provided publicly accessible code, highlighting the need for improved transparency. Authors’ conclusions ABMs are a versatile approach for representing complex disease dynamics, particularly in cases like TB, where they address heterogeneous mixing and household transmission often overlooked by traditional models. However, their advanced capabilities come with challenges, including those arising from their stochastic nature, such as parameter tuning and high computational expense. To improve transparency and reproducibility, open-source code sharing, and standardised reporting are recommended to enhance ABM reliability in studying epidemiologically complex diseases like TB

Replicating superspreader dynamics with compartmental models

Abstract Infectious disease outbreaks often exhibit superspreader dynamics, where most infected people generate no, or few secondary cases, and only a small fraction of individuals are responsible for a large proportion of transmission. Although capturing this heterogeneity is critical for estimating outbreak risk and the effectiveness of group-specific interventions, it is typically neglected in compartmental models of infectious disease transmission—which constitute the most common transmission dynamic modeling framework. In this study we propose different classes of compartmental epidemic models that incorporate transmission heterogeneity, fit them to a number of real outbreak datasets, and benchmark their performance against the canonical superspreader model (i.e., the negative binomial branching process model). We find that properly constructed compartmental models can capably reproduce observed superspreader dynamics and we provide the pathogen-specific parameter settings required to do so. As a consequence, we also show that compartmental models parameterized according to a binary clinical classification have limited support