Deep untargeted #wastewater #metagenomic #sequencing from #sewersheds across the #USA

 

Abstract

Wastewater monitoring enables non-invasive, population-scale tracking of community infections independent of healthcare-seeking behavior and clinical diagnosis. Metagenomic sequencing extends this capability by enabling broad, pathogen-agnostic detection, genomic characterization, and identification of novel or unexpected threats. Here, we present data from CASPER (the Coalition for Agnostic Sequencing of Pathogens from Environmental Reservoirs), a U.S.-based wastewater metagenomic sequencing network designed for deep, untargeted pathogen monitoring at national scale. This release includes 1,206 samples collected between December 2023 and December 2025 from 27 sites across nine states, covering 13 million people. Deep sequencing (~1 billion read pairs per sample) generated 1.2 trillion read pairs (347 terabases), enabling detection of even rare taxa, with CASPER representing 66% of all untargeted wastewater sequencing data currently available on the NCBI Sequence Read Archive. Virus abundance trends correlate with nationwide wastewater PCR and clinical data for SARS-CoV-2, influenza A, and respiratory syncytial virus, while the pathogen-agnostic approach captures emerging threats, including avian influenza H5N1 during initial dairy cattle outbreaks, West Nile virus, and measles, among hundreds of viral taxa. As the largest publicly available untargeted wastewater sequencing dataset to date, CASPER provides a shared and growing resource for pathogen surveillance and microbial ecology.

Competing Interest Statement

D.H.O. received support for this project from Inkfish and Heart of Racing. D.H.O. is a managing partner of Pathogenuity LLC, a consultancy that advises on topics including environmental monitoring for pathogens. P.C.S. hold several patents related to diagnostic and surveillance technologies and is a co-founder and equity holder in Delve Biosciences and Lyra Labs, a board member and equity holder in Polaris Genomics, and an equity holder of NextGenJane. P.C.S was formerly a co-founder of Sherlock Biosciences and board member of Danaher Corporation, until December 2024. All potential conflicts are managed in accordance with institutional policy.

Funding Statement

L.J.J. was supported by the Draper Scholar program at The Charles Stark Draper Laboratory. J.K., O.S.H., R.F-O., S.L.G., W.J.B., H.B., D.P.R., K.S., J.D.F., and M.R.M. received support for this work from Coefficient Giving via a gift to SecureBio. C.R., A.T-M., E.E.C., M.C.J., and D.H.O. were supported by Inkfish and Heart of Racing. L.J.J., J.P., and P.C.S. were supported by the CDC Pathogen Genomics Centers of Excellence (contract INTF5104H78W22195346) and a CDC Broad Agency Announcement (contract 75D30123C17983). J.E.L. and G.A. were supported by a subcontract under CDC Broad Agency Announcement contract 75D30123C17983. H.M.S-G. and A.A. were supported in part by the National Institute on Drug Abuse of the National Institutes of Health under Award Number U01DA053941, and by the University of Miami Initiative on Virology and Infectious Disease and SecureBio. R.P. was supported by the Illinois Department of Public Health and the Chicago Department of Public Health. This work used Expanse at the San Diego Supercomputer Center through allocation BIO240238 to J.A.R. from the Advanced Cyberinfrastructure Coordination Ecosystem: Services & Support (ACCESS) program, which is supported by U.S. National Science Foundation grants #2138259, #2138286, #2138307, #2137603, and #2138296.

Source: 

Link: https://www.medrxiv.org/content/10.64898/2026.03.05.26345726v1

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Adult #obesity and #risk of severe #infections: a multicohort study with global burden estimates

 

Summary

Background

Adult obesity has been linked to specific infections, but evidence across the full spectrum of infectious diseases remains scarce. In this multicohort study with impact modelling, we examined the association between this preventable risk factor and the incidence, hospitalisations, and mortality of 925 bacterial, viral, parasitic, and fungal infectious diseases, and estimated their global and regional attributable impact.

Methods

We used pooled data from two Finnish cohort studies and repeated analyses in an independent population from the UK Biobank. BMI was assessed at baseline (1998–2002 in the Finnish studies; 2006–10 in UK Biobank), and participants were categorised as having healthy weight (18·5–24·9 kg/m2), overweight (25·0–29·9 kg/m2) or obesity, classified as class I (30·0–34·9 kg/m2), class II (35·0–39·9 kg/m2), or class III (≥40·0 kg/m2). Participants were followed up through national hospitalisation and mortality registries for hospital admissions and deaths due to infectious diseases. Using hazard ratios derived from the Finnish cohorts and UK Biobank, along with obesity prevalence estimates from the Global Burden of Diseases, Injuries, and Risk Factors Study database, we estimated the proportion of fatal infections attributable to obesity globally, regionally, and by country for the years 2018 (before), 2021 (during), and 2023 (after the COVID-19 pandemic).

Findings

The analysis included 67 766 adults (mean age 42·1 [SD 10·8] years; 49 516 [73·1%] females, 18 250 [26·9%] males) from the Finnish cohorts and 479 498 adults (mean age 57·0 [SD 8·1] years; 261 084 [54·4%] females, 218 414 [45·6%] males) from UK Biobank. Participants had no recent history of infection-related hospitalisations at baseline. During follow-up, there were 8230 incident infection cases in the Finnish cohorts and 81 945 in UK Biobank. Compared with individuals of healthy weight, those with class III obesity had a three-times higher risk of infection-related hospital admissions (Finnish cohorts 2·75 [95% CI 2·24–3·37], UK Biobank 3·07 [2·95–3·19]), death (Finnish cohorts 3·06 [1·25–7·49], UK Biobank 3·54 [3·15–3·98]), or either outcome (Finnish cohorts 2·69 [2·19–3·30], UK Biobank 3·07 [2·95–3·19]). The corresponding pooled hazard ratio for either fatal or non-fatal severe infection among individuals with any obesity (classes I–III) was 1·7 (1·7–1·8). This association was consistent across different indicators of obesity (BMI, waist circumference, and waist-to-height ratio), demographic and clinical subgroups, and a wide range of infections (non-fatal and fatal, acute and chronic, bacterial and viral [including subtypes], and parasitic and fungal). Applying these risk estimates to global burden of disease data, the population attributable fractions of infection-related deaths due to obesity were estimated at 8·6% (6·6–11·1) in 2018, 15·0% (12·8–17·4) in 2021, and 10·8% (8·6–13·6) in 2023.

Interpretation

Adult obesity is a risk factor for infection-related hospitalisations and mortality across diverse pathogen types, populations, and baseline clinical profiles, with evidence suggesting that approximately one in ten infection-related deaths worldwide might be attributable to obesity.

Funding

Wellcome Trust, Medical Research Council, and Research Council of Finland.

Source: 

Link: https://www.sciencedirect.com/science/article/pii/S0140673625024742?dgcid=rss_sd_all

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#Ai and infectious disease #diagnostics: state of the art and future #perspectives

 

Summary

Artificial intelligence (AI) is reshaping infectious disease diagnostics by supporting clinical decision making, optimising laboratory and clinical workflows, and enabling real-time disease surveillance. AI approaches improve pathogen detection, antimicrobial stewardship, and treatment monitoring, enhancing diagnostic accuracy, efficiency, and scalability. The role of AI in combating antimicrobial resistance is particularly significant, enabling rapid pathogen identification and personalised treatment. Despite progress over the past two decades, widespread AI adoption in infectious disease diagnostics faces challenges. In high-income countries, fragmented data ecosystems, incomplete datasets, and algorithmic bias hinder clinical integration. Meanwhile, low-income and middle-income countries contend with limited digital infrastructure, unstandardised data, and financial constraints, exacerbating disparities in diagnostic access. Further barriers include concerns over interoperability, data privacy, cybersecurity, and the regulation of AI implementation. This paper examines the role of AI in infectious disease diagnostics, highlighting both opportunities and limitations. It underscores the need for coordinated investments in digital infrastructure, harmonised data-sharing frameworks, and clinician engagement to support equitable, sustainable adoption. Addressing these challenges will enable health-care systems to harness the potential of AI to improve infectious disease detection, prevention, and management of infectious diseases, thereby strengthening global health resilience.

Source: Lancet Infectious Diseases, https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(25)00354-8/abstract?rss=yes

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#Risk #assessment of #zoonotic #viruses in #urban-adapted #wildlife

Abstract

The repeated emergence of pandemic viruses underscores the linkages between land-use change and wildlife disease, and urban-adapted wildlife are of special interest due to their close proximity to humans. However, viral diversity within urban-adapted species and their zoonotic potential remain largely unexplored. We compiled a large dataset on seven priority urban-adapted mammal species and their viruses covering over 115 countries from 1574 to 2023. These urban-adapted species host 286 virus species spanning 24 orders and 38 families, 14 of which are potentially high risk for human infection. Raccoons carried the most high-risk viruses, while raccoon dogs had increased viral positivity in urban habitats compared to raccoons, wild boars, and red foxes. Many viruses in urban-adapted species were phylogenetically related to those found in humans, and we also observed evidence of possible viral spillback. These results highlight zoonotic risks associated with urban-adapted species and suggest enhanced surveillance to mitigate future outbreaks.

Source: BioRxIV, https://www.biorxiv.org/content/10.1101/2024.12.18.629064v1

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