AI News Archive: August 12, 2026 — Part 23
Sourced from 500+ daily AI sources, scored by relevance.
- Incidence-weighted force of infection for predicting first reported health-zone cases during the 2026 Bundibugyo virus disease outbreak: a rolling-origin evaluation
Anticipating which health zone will report the next confirmed case is operationally distinct from forecasting national case counts and matters for prepositioning response capacity; most spatial spread models rely on mobile-phone mobility data unavailable in the Democratic Republic of the Congo (DRC). We modelled the discrete-time hazard of a first reported confirmed case across 106 health zones in four provinces affected by the 2026 Bundibugyo virus disease outbreak (47 affected, 59 at risk, 26 July 2026), comparing four connectivity specifications,none, road-distance, a gravity score, and an incidence-weighted force-of-infection (FOI) term, fitted within an identical Bayesian hierarchical hazard architecture. Evaluation used a rolling-origin design, cluster bootstrap resampling, leave-one-origin-out and non-overlapping-origin checks, and a kernel-parameter sensitivity grid, with top-10 hit rate the pre-specified primary metric, matched to the operational question of which few zones warrant attention; AUC-PR, top-5 hit rate, and median rank percentile were secondary. FOI had the highest top-10 hit rate (42.6%), approaching conventional significance against road-distance and no-connectivity comparators. On AUC-PR, a model with no connectivity term performed as well as or better than any connectivity specification (0.437 vs. 0.409 for FOI), a discrepancy we report rather than omit. Rankings were stable across the sensitivity grid (Spearman; 0.90-0.99) and across robustness checks. An incidence-weighted connectivity term modestly and specifically improves identification of the highest-risk zones, concentrated in top-k ranking rather than uniform across metrics. The evaluation is pseudo-prospective, since historical data-vintage snapshots could not rule out retrospective revision, pending verification via a pre-registered top-20 ranking. Keywords: Bundibugyo virus disease; Ebola; spatial epidemiology; hazard model; Bayesian statistics; Democratic Republic of the Congo; disease surveillance
- Establishing wastewater-based SARS-CoV-2 variant surveillance independent of clinical isolates
The COVID-19 pandemic remains a global concern, partly due to the rapid mutation rate of SARS-CoV-2 and the emergence of new variants. Wastewater surveillance has proven effective in estimating infection incidence and detecting variants earlier than clinical testing. Its importance has grown as testing rates decline due to milder disease progression. However, current methods typically rely on the prior classification of SARS-CoV-2 lineages or their signature mutations, which may delay detection. We present an alternative method that identifies changes in the viral genetic population over time without requiring prior lineage classification. This population-based approach was applied to sequencing data from wastewater samples, which are generally noisier than clinical samples. We analyzed publicly available sequencing samples from wastewater plants covering Swiss catchments in Altenrhein, St. Gall, Geneva, and Zurich. To address noise, only samples with read depths above 40 and genome coverage of at least 90% were included. Genetic diversity within pooled populations over two time periods was compared to assess changes in viral composition. We demonstrate that SARS-CoV-2 variants can be detected in wastewater sequencing data without prior lineage classification. Our method successfully detected shifts in genetic populations that corresponded to the emergence of known variants of concern (VOCs) in the analyzed regions. Notably, it also revealed the rising prevalence during the first surges of the Omicron variant. Despite the increased noise in wastewater compared to clinical samples, our approach remains effective. However, achieving reliable predictions depends on high sequencing depth, broad genome coverage, and frequent sampling.
- Burden and Seasonal Variations of Cutaneous Leishmaniasis in Afghanistan During 2024.
Background: Cutaneous leishmaniasis (CL) remains a major neglected tropical disease in Afghanistan; however, recent nationwide evidence on its geographical and temporal distribution is limited. This study assessed the reported burden, spatial distribution and seasonal variation of cutaneous leishmaniasis across Afghanistan during 2024. Methods: A nationwide retrospective ecological study was conducted using aggregated routine surveillance data obtained from the National Malaria and Other Vector-Borne Diseases Program. All cases reported from government health facilities across Afghanistans 34 provinces between 1 January and 31 December 2024 were included. Reported incidence rates were calculated per 10,000 population using national and subnational population estimates. Cases were analyzed by surveillance classification, province, city, rural district, month, and season. Results: A total of 87,245 cases were reported during 2024, corresponding to an overall incidence of 25.1/10,000 population. Anthroponotic and zoonotic CL accounted for 80,202 (91.9%) and 7,043 (8.1%) cases, respectively. Jowzjan recorded the highest provincial incidence (105.0/10,000), while Kabul and Herat contributed the largest absolute numbers of cases. Shiberghan had the highest urban incidence, whereas Panjwai, Ghoryan and Hazrat-e-Sultan recorded exceptionally high rural rates. May had the highest monthly burden, while August had the lowest. Autumn accounted for the largest seasonal proportion (27.92%), whereas summer had the lowest burden (16.98%). Considerable incompleteness in district-level reporting was observed. Conclusion: CL constituted a substantial but highly concentrated public health burden in Afghanistan during 2024. Strengthened surveillance, improved diagnostic confirmation and geographically targeted case-management and vector-control interventions are particularly needed in identified high-burden provinces, cities and rural districts.
- A fit-for-purpose sequencing strategy for West Nile virus genomic surveillance using NAT-reactive blood donations
Background. West Nile virus (WNV) genomic surveillance in the United States relies largely on mosquito and avian sequencing, while human-derived genomes remain scarce. Nucleic acid testing (NAT)-reactive blood donations provide a standardized source of acute human-phase virus, but low donor viremia complicates genome recovery. This study evaluated a sequencing strategy for WNV surveillance using these samples. Study Design and Methods. Amplicon sequencing, hybridization capture, and shotgun RNA-seq were evaluated for WNV lineage 1a recovery from donor plasma. Amplicon performance was characterized using a WHO International Standard dilution panel quantified by RT-dPCR, contemporary 2025 donations, archival 2010-2011 donations, and technical replicates. Two donations were processed by all three methods from matched plasma to compare performance metrics and consensus concordance. Results. Amplicon sequencing recovered near-complete genomes across the full dilution panel, including the lowest measured input, and across the viral-load range represented by the selected donor samples. Recovery from the two archival plasma samples was similar to that observed among contemporary donations. In the two matched donations, all three methods generated identical consensus sequences across shared callable positions. At lower input, amplicon and capture maintained near-complete recovery, whereas shotgun RNA-seq decreased to 87.2% coverage at 10X. For libraries achieving near-complete recovery, WNV-mapped-read requirements were similar, but amplicon sequencing required substantially fewer total reads. Discussion. NAT-reactive blood donations can support WNV genomic surveillance. Amplicon sequencing is an efficient first-pass approach for expected lineage 1a WNV, with capture and shotgun RNA-seq serving as escalation strategies for divergent lineages or unbiased pathogen detection.
- Sixteen Days Undetected: Growth Dynamics and the Case for Pre-Positioned Response Capacity in the 2026 Bundibugyo Virus Disease Outbreak, Democratic Republic of the Congo A back-calculation and growth-rate analysis using corrected daily surveillance data
Objectives: To estimate early growth rate, back-calculate transmission onset, and characterise the case-fatality trajectory of the 2026 Bundibugyo virus disease (BDBV) outbreak in the Democratic Republic of the Congo, the largest recorded BDBV outbreak to date. Design or methods: We analysed a corrected daily surveillance series (65 observations, 14 May to 27 July 2026) using non-linear least-squares regression and a Bayesian Poisson growth model fitted by Markov chain Monte Carlo, with five sensitivity analyses. Results: Early confirmed cases grew at 0.1261 per day (95% CI 0.0885-0.1636), a doubling time of 5.50 days (4.24-7.83), three-fold faster than previous BDBV outbreaks (15-18 days). Bayesian back-calculation placed transmission onset on 19 April 2026 (95% highest-density interval 9-27 April), 16 days before the WHO alert and 25 days before laboratory confirmation. Confirmed case-fatality ratio rose from 12.1% to 44.3%; a higher ratio among suspected than confirmed cases on 21 May (23.6% vs 10.8%; p=0.0080) supported progressive reclassification rather than increasing virulence. Conclusions: Rapid BDBV growth leaves little time for outbreak-triggered mobilisation. Sentinel alerts based on unexplained healthcare-worker death clusters, together with pre-positioned surveillance, diagnostic, and response capacity, could reduce avoidable amplification before confirmation.
- TMEM106B haplotypes show distinct associations with tau and TDP-43 pathologies in the aging brain
A central challenge in post-GWAS biology is determining how inherited variation within disease-associated loci shapes molecular mechanisms and clinical phenotypes. Here, we examined four previously identified TMEM106B haplotypes (T1-T4), defined by distinct combinations of coding, structural and regulatory variants. We integrated transcriptomic, proteomic, and neuropathological data from 1,209 individuals across two independent complementary ageing cohorts. Although T2 and T3 both carry the p.Ser185 coding variant, they showed opposing associations with tau pathology, indicating that the surrounding haplotypic background modifies disease susceptibility. T3, which is enriched in cognitively healthy centenarians, was associated with lower tau pathology, lower C-terminal TMEM106B abundance, and reduced detection of an inflammatory microglial state, differing from the association pattern observed for T2. By contrast, T1 was associated with more extensive TDP-43 pathology, neuronal endolysosomal dysregulation, and increased C-terminal TMEM106B abundance. These findings identify haplotype-specific associations with differential proteinopathy burden, illustrating how haplotype-resolved analyses can connect GWAS signals to candidate molecular pathways.
- Identifying patients with a phenotype consistent with chronic postsurgical pain after hip and knee arthroplasty using robust, scalable k-medoids clustering analysis
Background Chronic postsurgical pain (CPSP) affects between 7-23% and 13-44% of patients after hip and knee arthroplasty, respectively. Standardised methods of pain assessment provide superior evaluation of pain, including the Oxford Joint Score Pain Subscale (OJS-PS). We aim to estimate the proportion of patients with a phenotype consistent with CPSP through a k-medoids clustering technique and identify a threshold on the OJS-PS to highlight such patients at a population level. Methods In this cross-sectional study Patient Reported Outcomes Measures data 6-months after hip and knee arthroplasty from 2017 to 2025 were examined. An adapted k-medoid clustering technique utilising subsampling, batch assignment and probabilistic consensus allocated clusters. A receiver operator characteristic analysis identified a threshold on the OJS-PS noting the lowest scoring cluster. Our categorisation was compared to self-reported severe or moderate pain; sensitivity, specificity and accuracy of this categorisation were calculated. Results We analysed 109,542 hip and 113,799 knee arthroplasty patients; three clusters were used in each analysis. After hip arthroplasty: 14.4% of patients were assigned to the cluster with the lowest median OJS-PS of 11 [IQR 8 - 13]. A threshold of 15.5 classified patients as severe or moderate pain with 60.6% sensitivity, 91.0% specificity and 85.7% accuracy. Similarly, after knee arthroplasty, 25.3% were assigned to the cluster with the lowest median OJS-PS of 14 [IQR 11 - 16]. A threshold of 18.5 on the OJS-PS had an 85.4% sensitivity, 88.4% specificity and 87.8% accuracy for classifying patients with self-reported severe or moderate pain. Conclusions This robust and scalable clustering technique on ordinal clinical data estimates the proportion of patients reporting a phenotype consistent with CPSP. On a population level the thresholds identified on the OJS-PS could aid screening for potential CPSP patients 6 months after hip and knee arthroplasties.
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