26 papers
This paper proposes a robust geometric early warning indicator based on the width of the stochastic separatrix in a two-state ecosystem model, which successfully predicts rapid under-ice phytoplankton blooms in the Arctic where conventional critical slowing down signals fail due to strong noise or limited data.
This study demonstrates that assuming a uniform generation time distribution in renewal equation models can lead to inaccurate estimates of the time-dependent reproduction number (Rt) in structured populations, and it proposes a methodology to correct for this mis-specification to improve public health decision-making.
This paper proposes a non-Abelian gauge theory framework using the Doi-Peliti formalism to explain the rapid genetic radiation of *Yersinia pestis* and the existence of historical safe zones like Poland and Bohemia as mathematically necessary topological voids arising from the destructive interference of mutation-driven traveling waves.
This paper analyzes the phase-space features of a Toda-like Hamiltonian constrained by specific conditions to demonstrate that it not only analytically reproduces classical Lotka-Volterra prey-predator dynamics but also exhibits quantum stability through non-perturbative distortions, thereby establishing a predictive framework for quantized competitive biosystems.
This paper extends the existing bijection between ranked tree shapes and F-matrices to fully heterochronous cases by establishing an explicit correspondence that enables efficient enumeration and the development of probabilistic models for phylogenetic trees with leaves at varying sampling times.
This paper introduces the class of -cuttable undirected phylogenetic networks as a computationally useful alternative to tree-child-orientable networks, demonstrating that they are recognizable in polynomial time and render the NP-hard Tree Containment problem solvable in polynomial time for .
This paper introduces CLEF, a controllable sequence editing framework that learns temporal concepts to precisely target the timing and scope of interventions in longitudinal data, significantly outperforming state-of-the-art baselines in generating accurate and realistic counterfactual trajectories for biological and clinical applications.
This paper presents a data-driven quantitative model, calibrated with U.S. spillover data, to assess the weekly probability and spatiotemporal distribution of highly pathogenic avian influenza (H5N1) transmission from wild birds to Danish cattle, identifying coastal and border regions as high-risk areas primarily during winter months to guide targeted surveillance and preparedness.
This paper introduces a stochastic model demonstrating that while extreme value statistics of first-passage times allow a larger population to overcome a spatial disadvantage, a non-reciprocal interaction bias is strictly required to stabilize this transient superiority into macroscopic dominance.
The paper introduces SDSR, a scalable spectral divide-and-conquer algorithm for species tree reconstruction that achieves up to 10-fold faster runtimes compared to standard methods while maintaining comparable accuracy under the multispecies coalescent model.
This paper establishes the existence of traveling wave solutions, including non-monotone waves and front-pulse waves, for the two-species weak competition Lotka-Volterra system across all wave speeds , with a rigorous proof for the critical speed case and the first-time demonstration of front-pulse waves in the critical weak competition regime.
This study demonstrates that neglecting vaccine unwillingness in epidemiological models introduces significant errors in both long-term equilibrium predictions and short-term epidemic wave simulations, with the magnitude of error depending on the disease's infectivity and the speed of vaccination programs.
This paper establishes a functional law of large numbers for a spatial model of Muller's ratchet, proving that the system converges to an infinite system of partial differential equations that rigorously determine population spreading speeds and demonstrate that deleterious mutations can indeed surf deterministic population waves.
This paper presents a large-scale, actor-based model integrating detailed Dutch demographic and mobility data to simulate novel respiratory pathogen transmission on dynamic contact networks, demonstrating how geographic and demographic factors drive epidemic spread and quantifying the impact of interventions like self-isolation and travel restrictions.
This paper introduces CoSiNE, a deep neural network-parameterized continuous-time Markov chain that bridges the gap between expressive deep learning and classical phylogenetic models to capture epistatic interactions in antibody evolution, thereby outperforming state-of-the-art language models in variant effect prediction and enabling efficient affinity optimization through a novel Guided Gillespie sampling scheme.
This study develops a network-based model to overcome the limitations of traditional homogeneous mixing assumptions in studying Frogeye Leaf Spot, revealing that early targeted roguing is the most effective management strategy while tillage practices showed no significant impact on disease spread.
This study demonstrates that while vaccination alone is ineffective against Foot-and-Mouth Disease in Mato Grosso do Sul, Brazil, combining high depopulation capacity with limited vaccination is the optimal strategy, capable of controlling 100% of simulated outbreaks within 10 to 15 days.
This paper derives sharp upper and lower bounds for the basic reproduction number () and final epidemic sizes in multitype SIR models where the next-generation matrix is only partially known through row or column sums, providing complete results for general matrices and partial results for matrices satisfying detailed balance.
This paper introduces an algorithm capable of computing all evolutionarily stable strategies within nondegenerate normal-form games involving three or more players.
This study employs mathematical modeling to demonstrate that viral infectivity and dynamics are often more critical than tumor motility in determining the success of oncolytic virotherapy, offering new insights into infection wave propagation and therapeutic outcomes.