Phylogenomic early warning signals for SARS-CoV-2 epidemic waves.

BACKGROUND: Epidemic waves of coronavirus disease 2019 (COVID-19) infections have often been associated with the emergence of novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants. Rapid detection of growing genomic variants can therefore serve as a predictor of future waves, enabling timely implementation of countermeasures such as non-pharmaceutical interventions (social distancing), additional vaccination (booster campaigns), or healthcare capacity adjustments. The large amount of SARS-CoV-2 genomic sequence data produced during the pandemic has provided a unique opportunity to explore the utility of these data for generating early warning signals (EWS). METHODS: We developed an analytical pipeline (Transmission Fitness Polymorphism Scanner - designated in an R package mrc-ide/tfpscanner) for systematically exploring all clades within a SARS-CoV-2 virus phylogeny to detect variants showing unusually high growth rates. We investigated the use of these cluster growth rates as the basis for a variety of statistical time series to use as leading indicators for the epidemic waves in the UK during the pandemic between August 2020 and March 2022. We also compared the performance of these phylogeny-derived leading indicators with a range of non-phylogeny-derived leading indicators. Our experiments simulated data generation and real-time analysis. FINDINGS: Using phylogenomic analysis, we identified leading indicators that would have generated EWS ahead of significant increases in COVID-19 hospitalisations in the UK between August 2020 and March 2022. Our results also show that EWS lead time is sensitive to the threshold set for the number of false positive (FP) EWS. It is often possible to generate longer EWS lead times if more FP EWS are tolerated. On the basis of maximising lead time and minimising the number of FP EWS, the best performing leading indicators that we identified, amongst a set of 1.4 million, were the maximum logistic growth rate (LGR) amongst clusters of the dominant Pango lineage and the mean simple LGR across a broader set of clusters. In the case of the former, the time between the EWS and wave inflection points (a conservative measure of wave start dates) for the seven waves ranged between a 20-day lead time and a 7-day lag, with a mean lead time of 5.4 days. The maximum number of FP EWS generated prior to a true positive (TP) EWS was two and this only occurred for two of the seven waves in the period. The mean simple LGR amongst a broader set of clusters also performed well in terms of lead time but with slightly more FP EWS. INTERPRETATION: As a result of the significant surveillance effort during the pandemic, early detection of SARS-CoV-2 variants of concern Alpha, Delta, and Omicron provided some of the first examples where timely detection and characterisation of pathogen variants has been used to tailor public health response. The success of our method in generating early warning signals based on phylogenomic analysis for SARS-CoV-2 in the UK may make it a worthwhile addition to existing surveillance strategies. In addition, the method may be translatable to other countries and/or regions, and to other pathogens with large-scale and rapid genomic surveillance. FUNDING: This research was funded in whole, or in part, by the Wellcome Trust (220885_Z_20_Z). For the purpose of open access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. KOD, OB, VBF and EMV acknowledge funding from the MRC Centre for Global Infectious Disease Analysis (reference MR/X020258/1), jointly funded by the UK Medical Research Council (MRC) and the UK Foreign, Commonwealth & Development Office (FCDO), under the MRC/FCDO Concordat agreement and is also part of the EDCTP2 programme supported by the European Union. RMC acknowledges funding from the Wellcome Trust Collaborators Award (206298/Z/17/Z).

Characterization of Circulating Clostridium difficile Strains, Host Response and Intestinal Microbiome in Hospitalized Children With Diarrhea.

BACKGROUND: Clostridium difficile is capable of causing severe enterocolitis in adults. The significance of toxin-producing C. difficile in children with diarrhea is unclear and practice differs on whether to institute treatment. We aimed to characterize the microbiome in relation to the presence of C. difficile and co-infection with other pathogens and to describe host response to infection. METHODS: Participants were children with acute diarrhea, 0-16 years of age, from whom stool samples had been submitted to the hospital laboratory for routine microbiology/virology. Convenience sampling was used for 50 prospective and 150 retrospective samples. No participants were treated for C. difficile. Rates of culture positivity for C. difficile, presence of toxin and PCR-ribotype were compared between age groups. Presence of other potential pathogens, comorbidities and complications were recorded. Microbiotal diversity was measured by 16S profiling. RESULTS: Nineteen of 77 (25%) children <2 years of age and 13 of 119 (11%) children >2 years of age were C. difficile positive, of whom 10 (53%) and 9 (69%), respectively, carried toxigenic strains. Increased Shannon diversity was seen in children carrying C. difficile, with altered milieu. Presence of C. difficile was not associated with adverse clinical outcomes. In stools containing both Norovirus and C. difficile, there was increased relative abundance of verrucomicrobia. CONCLUSIONS: Children with diarrhea regularly carried toxigenic and non-toxigenic strains of C. difficile, demonstrating enhanced microbiotal diversity, and change in milieu, without apparent morbidity. This unexpected finding is contrary to that seen in adults with C. difficile disease.