AI summary · arXiv: 2610.29766v1 · q-bio.PE
Bayesian Comparison of Covid Origins Hypotheses
Ask questions about this paper to our AI assistant
How to chat with this paper? Illustrated guide
You can also chat with multiple papers at once here.
Keep this conversation — a free account saves your questions and answers, your collections of papers and your alerts.
Create a free accountTools for this paper
Advanced tools Premium
For researchers who follow a field closely: citation alerts, journal-club slides, version comparison and Zotero export. Discover Premium
Papers around this one
See them on a mapRanked by how close their title and abstract are to this paper’s. Select some to read them together.
Closest papers
-
Mismatched and underrepresented: bat–virus diversity and phylogeographic bias in experimental infection studiesLuis R. Víquez-R, Thomas P. Y. Mazzarulli, Melissa T. R. Hawkins et al. · 2026 · Frontiers in Immunology · Not summarized yet
Abstract
Despite growing interest in bat–virus interactions, experimental infection studies (EIS) remain taxonomically and geographically restricted, limiting inference about bat immunology and zoonotic risk. We performed a systematic literature search (Jan 1900–Jan 2025) and manually curated peer-reviewed primary studies of viral experimental infections in live bats and bat-derived cell lines (BdCL). From 545 candidate records we retained 127 papers (83 live-bat, 44 BdCL), comprising 315 unique bat–virus trials. We categorized host taxonomy, viral family (ICTV), and biogeographical congruence, to quantify temporal trends, taxonomic coverage, and host–virus pairing patterns. Results show stark underr…
-
A joint Bayesian hierarchical model for estimating SARS-CoV-2 diagnostic and subgenomic RNA viral dynamics and seroconversionTracy Q. Dong, Elizabeth R. Brown · 2023 · Applications · AI summary available
Abstract
Understanding the viral dynamics and immunizing antibodies of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is crucial for devising better therapeutic and prevention strategies for COVID-19. Here, we present a Bayesian hierarchical model that jointly estimates the diagnostic RNA viral load reflecting genomic materials of SARS-CoV-2, the subgenomic RNAs (sgRNA) viral load reflecting active viral replication, and the rate and timing of seroconversion reflecting presence of antibodies. Our proposed method accounts for the dynamical relationship and correlation structure between the two types of viral load, allows for borrowing of information between viral load and antibody da…
-
Phylodynamic analysis of the spread of COVID-19 in the National Capital Region, Philippines during the early stages of the pandemicI. G. Panogalinog, Imari Joy Borda, Z. P. Lachica et al. · 2026 · SciEnggJ · Not summarized yet
Abstract
In epidemiological studies, key parameters could be incorrectly estimated when case data are underreported, as demonstrated during the early spread of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Phylodynamic models, which integrate viral genomic data, offer an alternative approach to understand SARS-CoV-2 transmission dynamics. Here we aimed to understand the early spread of SARS-CoV-2 in the National Capital Region of the Philippines (NCR) through phylogenetic and phylodynamic analysis of viral sequences. We investigated how government-imposed community quarantines influenced viral spread and compared these estimates with those from conventional compartmental models. W…
-
Will there be a warning for the next pandemic?J. Lessler, C. Metcalf · 2026 · Science Advances · Not summarized yet
Abstract
How to best allocate resources to combat the threat of pathogen emergence remains an important open question. Using archetypes that characterize the link from genotype to fitness in both zoonotic reservoirs and humans, we show that, across a range of plausible conditions, emergences of pathogens with pandemic potential in humans are unlikely to be preceded by detectable, failed, attempts. Yet, the number of “failed” emergence events contains information about the emergence potential of zoonotic pathogens, and should modify our beliefs about the underlying fitness landscape. Our work suggests that the most important, modifiable, risk factors for emergence may be phenomena that alter fitness l…
-
Statistical inference of virus evolution using ancestral recombination graphsChristopher H. C. Leung, C. Houldcroft, A. Scally · 2026 · bioRxiv · Not summarized yet
Abstract
Ancestral recombination graphs (ARGs) represent the coalescent history at every locus in the genome, subject to mutation and recombination. Modelling viruses using the coalescent with recombination remains less explored, due to the potential for complex, multi-scale dynamics. Using perturbative methods, we establish mathematical bounds that describe the balance between inter-host and within-host coalescence, and demonstrate that the highly-recombinant Epstein-Barr Virus (EBV) coalesces predominantly at the inter-host level. We then modify SINGER, a tool from diploid genetics, for haploid ARG inference with fine-grained variable recombination rates. We provide insight into the paradox of high…
-
Bayesian calibration of adaptive-behavior SIR models for multi-wave COVID-19 incidence in New York CityLuis A. Barboza, Carlos Pasquier, Baltazar Espinoza et al. · 2026 · Not summarized yet
Abstract
Epidemic incidence reflects both transmission dynamics and adaptive human behavior, yet these mechanisms may be difficult to distinguish from aggregate case data alone. We calibrated four susceptible--infected--recovered (SIR) specifications to weekly confirmed COVID-19 incidence in New York City from June to December 2020, comparing a single continuous SIR trajectory, a wave-initialized SIR model, and two adaptive-behavior models with either shared or wave-specific transmission. Inference was performed using rejection Approximate Bayesian Computation (ABC), and in-sample reconstruction was assessed using root mean squared error (RMSE) and the weighted interval score (WIS). Reinitializing th…
-
Scalable near-real-time Bayesian phylogenetics for outbreaks with DelphyP. Varilly, Mark Schifferli, Katherine Yang et al. · 2026 · Nature · Not summarized yet
Abstract
Pathogen genomic analysis is central to tracking, understanding and containing outbreaks1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12–13, but the complexity and cost of state-of-the-art phylogenetic tools limit global access and impact. Here we introduce Delphy, an exact reformulation of Bayesian phylogenetics14, 15, 16–17 designed to transform its speed, scalability and accessibility while retaining Bayesian state-of-the-art accuracy. Delphy’s central data structure, an explicit mutation-annotated tree, takes advantage of the high sequence similarity of large-scale epidemic datasets18, 19–20 for efficient tree exploration and convergence. By reproducing key analyses from recent major epidemics, inc…
-
Parameter non-identifiability limits ecological inference from historical plague mortality recordsNich W. Martin, Nicolas Gauthier · 2026 · bioRxiv · Not summarized yet
Abstract
Historical plague mortality records provide unusually detailed temporal records of epidemic dynamics. However, the ecological processes underlying those dynamics are rarely observed. This creates a fundamental challenge for inference into ecological mechanisms, as multiple combinations of unobserved ecological parameters and initial conditions may produce similar mortality trajectories. We investigated this problem by examining the geometry of parameter compensation for models of the Barcelona plague outbreak of 1490. We fit a mechanistic rat-flea-human transmission model to daily human mortality data under three values of flea search efficiency, then characterized the resulting joint poster…
Go further
How are these papers found?
We compare the title and abstract of this paper with those of every paper summarized on this site, and with the papers Semantic Scholar recommends, those it cites and those citing it (for a much-cited paper, its most recent citations and, from OpenAlex, the most cited ones). Among the prior work and the follow-ups, the papers that use each other in depth, a method it uses and the much-cited follow-ups come a little higher. The levels (very close, close, related) come from the similarity of the texts, not from a judgment of quality; * marks a paper without an abstract, compared by its title. The sentence under a paper is written by an AI from the two abstracts: check it in the papers. A paper marked not relevant by three readers leaves the list.
Updated on 31 October 2026.
Some bits of the article are not summarized yet, you can re-run the summarizing process by clicking on the Run button below.
Disclaimer: The AI-based summarization tool and virtual assistant provided on this website may not always provide accurate and complete summaries or responses. We encourage you to carefully review and evaluate the generated content to ensure its quality and relevance to your needs.