Research Institute for Tropical Medicine

Large language models (LLMs) are increasingly embedded directly into social media platforms, enabling users to request real-time fact-checks of online content. Using an exhaustive dataset of 1,671,841 English-language fact-checking requests made to Grok and Perplexity on X between February and September 2025, we provide the first large-scale empirical analysis of how LLM-based fact-checking operates in the wild. Fact-checking requests comprise 7.6% of all interactions with the LLM bots, and focus primarily on politics, economics, and current events. We document clear partisan asymmetries in usage. Users requesting fact-checks from Grok are much more likely to be Republican than Democratic, while the opposite is true for fact-check requests from Perplexity -- indicating emerging polarization in attitudes toward specific AI models. At the same time, both Democrats and Republicans are more likely to request fact-checks on posts authored by Republicans, and - consistent with prior work using professional fact-checkers and crowd judgments - posts from Republican-leaning accounts are more likely to be rated as inaccurate by both LLMs. Across posts rated by both LLM bots, evaluations from Grok and Perplexity agree 52.6% of the time and strongly disagree (one party rates a claim as true and the other as false) 13.6% of the time. For a sample of 100 fact-checked posts, 54.5% of Grok bot ratings and 57.7% of Perplexity bot ratings agreed with ratings of human fact-checkers, which is significantly lower than the inter-fact-checker agreement rate of 64.0%; but API-access versions of Grok had higher agreement with fact-checkers than did not significantly differ from inter-fact-checker agreement. Finally, in a preregistered survey experiment with 1,592 U.S. participants, exposure to LLM fact-checks meaningfully shifts belief accuracy, with effect sizes comparable to those observed in studies of professional fact-checking. However, responses to Grok fact-checks are polarized by partisanship when model identity is disclosed, whereas responses to Perplexity are not. Together, these findings show that LLM-based fact-checking is rapidly scaling, is generally informative although far from perfect, while also becoming entangled with polarization and partisanship. Our work highlights both the promise and the risks of integrating AI fact-checking into online public discourse.

Pathogen genomic data provide important insights for public health microbiology, yet genome analysis options often remain highly technical and beyond the reach of many microbiologists and public health practitioners. Pathogenwatch () is a platform that translates pathogen genome data into outputs directly usable for surveillance and public health action. The platform contextualises bacterial, viral, and fungal genomes within a unified framework integrating organism identity, variant or lineage assignment, antimicrobial resistance and virulence gene detection, and geographic and temporal context. Pathogenwatch provides multilocus sequence typing (MLST) for more than 37 bacterial species and core genome MLST (cgMLST) schemes for over 20 priority organisms, with user-uploaded genomes automatically compared against over 875,000 curated public bacterial genomes. The platform has been adopted by 14,389 registered users across 165 countries. In 2025, users uploaded 328,676 genome assemblies and 20,830 read datasets. Pathogenwatch replicates analysis results of complex bioinformatics pipelines. Benchmarking of SARS-CoV-2 lineage assignment against an established reference dataset demonstrated complete concordance for all Variants of Concern and Interest, and full concordance with contemporary Pangolin calls across non-VOC/VOI lineages. Pathogenwatch operates as a continuously deployed, containerised system designed for scalability, reproducibility, and rapid incorporation of new pathogens, positioning it as durable infrastructure for both endemic surveillance and genomic response to emerging threats. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This research was funded by the NIHR (NIHR133307) using UK international development funding from the UK Government to support global health research. The views expressed in this publication are those of the author(s) and not necessarily those of the NIHR or the UK government. Additional funding was provided by the Gates Foundation (grant ref INV-025280 to DMA). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All public data represented in Pathogenwatch are available for download within the application ().

Introduction While pathogen genomics using next-generation sequencing (NGS) has been recommended by the WHO as an essential tool for national communicable disease surveillance programmes, procurement and supply chain management (PSM) systems for this new technology are still evolving. To assess the status of PSM systems for pathogen genomics, we examined perspectives from end-users and manufacturers across South and Southeast Asia.Methods Between 2022 and 2023, a cross-sectional survey was conducted among institutional partners supporting pathogen genomics among primarily low- and middle-income countries in South and Southeast Asia. This was complemented by qualitative interviews with the major regional NGS manufacturers. A PSM framework was employed to assess sales, procurement, production, distribution and post-sales support. Analyses are expressed as proportions and means or medians for continuous variables.Results A total of 42 partners across 13 countries, 3 genomics manufacturers and 22 laboratory personnel contributed data to this assessment. PSM challenges were reported by all countries and for all sequencing platforms. High costs of equipment and consumables were identified by 85% of respondents. Long equipment purchasing lead times and reagent re-supply times were reported by 69% and 77% of countries, respectively, with reagent resupply times averaging 8 weeks (IQR 6.2-9.0). Additional barriers included customs clearance, variability of import procedures, taxes and duties. Manufacturers reported a range of strategies to respond to PSM bottlenecks, including establishing regional hubs, distributor networks and financing schemes.Conclusion Coordinated national and regional efforts are required to improve PSM systems for pathogen genomic sequencing to enhance timely early disease detection and response capacity in South and Southeast Asia.

OBJECTIVES:Although new molecular entity (NME) antibacterials active against multidrug-resistant organisms have been introduced, their availability remains limited even in developed countries. This study aimed to examine the current availability of NME antibacterial agents in Asia. METHODS:NME antibacterials approved by the US FDA from 2010 to 2024 were included. Approval status in ten Asian countries was collected as of June 2025. Population and economic status were sourced from the United Nations and World Bank databases. Investigators reported approval, withdrawal, and unit prices of study drugs as well as the type of healthcare systems using a standardised report form. RESULTS:Among 22 NME antimicrobials included in this study, thirteen were approved in the surveyed countries. Beta-lactam/beta-lactamase inhibitors such as ceftazidime/avibactam and ceftolozane/tazobactam were the most approved class, each available in nine and eight countries, respectively. Agents active against carbapenem-resistant Acinetobacter spp. were available in three countries: cefiderocol (Japan, Singapore, Taiwan) and eravacycline (Singapore). The median number of available NME antibacterials per country was 3.5, ranging from one to six. For most NME antibacterials, a delay of three to five years existed between US FDA approval and approval in study countries. The number of available NME antibacterials showed no significant correlation with gross domestic product, relative drug price, or healthcare delivery system, except for approval lag. CONCLUSIONS:Access to NME antibacterials in Asia remains markedly limited, with substantial approval delays. Considering the severity of antimicrobial resistance in the region, concerted efforts are warranted to improve access to crucial antibiotics in Asia.

Co-infection with multiple severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants, though rare, may have clinical and public health implications, including facilitating variant recombination. Early detection of co-infections is, therefore, crucial. In this study, we report two probable cases of co-infection identified during routine genomic surveillance. Initially suspected as cross-contamination due to the presence of private mutations and nucleotide mixtures flagged by Nextclade and bammix, the samples were re-extracted and re-sequenced after workspace decontamination, yet the anomalies persisted. To investigate further, we developed a bioinformatics pipeline (Katmon) incorporating various tools such as Freyja, with lineage abundance results that illustrated the presence of multiple variants, and VirStrain, which confirmed inconsistent lineage assignments. We also visualized the alternative allele fractions for each lineage-defining mutation and amplicon, showing evidence of two variants, Delta and Omicron, co-existing within a single amplicon. Amplicon sorting effectively separated reads corresponding to the two variants, and the resulting consensus sequences aligned with their respective lineage assignments. These findings suggest that the first sample, PH-RITM-1395, involved a Delta-Omicron co-infection, while the second sample, PH-RITM-4146, probably contains both a co-infection and a recombinant variant. To further support the second sample's recombinant nature, we employed sc2rf, which identified Delta-Omicron breakpoints. Retrospective analysis of 1,078 samples from July 2021 to July 2022, encompassing the period of co-circulation of different variants in the Philippines, flagged four additional co-infection cases, including Delta-Omicron and Beta-Omicron, suggesting a lower bound co-infection prevalence of 0.27% and 0.19%, respectively. Furthermore, the pipeline was used to test previously identified co-infections of different variants from different countries. Our findings underscore the critical importance of real-time genomic surveillance and advanced bioinformatics pipelines in detecting SARS-CoV-2 co-infections and variant recombination.