Description Of Dynamics Research Articles

Exploring action-law of microplastic abundance variation in river waters at coastal regions of China based on machine learning prediction

Surface waters, particularly the river systems, constitute a vital freshwater resource for human beings and aquatic life on Earth. In economically developed and densely populated coastal regions, river water is facing severe microplastic pollution, posing a threat to public health and ecological safety. Reliable prediction of microplastic abundance (MPA) can significantly reduce the costs associated with microplastic field sampling and analysis. This study employed spatial correlation, geographical detector, principal component analysis and five mainstream machine learning models to analyze 79 datasets of MPAs in seven coastal areas of China and performed correlation, regression and attribution analyses based on 19 terrestrial influencing factors that potentially affect the MPA life cycle processes (generation, aging, and migration). The results showed that the Neural Network (NN) and the Gaussian Process Regression (GPR) models achieved the best prediction performance, with the predicted R2 close to 1. Principal component analysis and Shapley additive explanations concluded that meteorological factors, in particular the annual geotemperature, surface solar radiation, and annual relative humidity, had a key influence on the aging of microplastics. The second key factor in improving the MPA prediction ability was the dynamic description of microplastic migration, which was primarily governed by hydrological factors such as annual precipitation and average terrain slope. Unexpectedly, the effects of land use and level of urbanization were relatively small in describing the generation of microplastics. Only the percentage of built areas was strongly correlated with the MPA levels. Note that the MPA prediction and its contribution factors may vary across different basins. Nevertheless, the findings of this study are applicable to predicting and analyzing the distribution of microplastics in other coastal rivers, and for indicating the main contributing factors, ultimately serving as a basis for guiding microplastic pollution control strategies in different river basins.

Association of COVID-19 with thyroid dysfunction and autoimmune thyroid disease: A retrospective cohort study

BackgroundSince the end of the COVID-19 pandemic, the potential roles of thyroid-inflammatory derangements in driving or being associated with the prognosis of COVID-19 remain controversial. We aimed to clarify the association between COVID-19 infection and thyroid dysfunction, and highlight the impacts of subsequent autoimmune thyroid disease (AITD) on the prognosis of COVID-19. MethodsThe retrospective, multicenter, cohort study enrolled 2,339 participants with COVID-19 from three hospitals located in the north, middle, and south regions of Shaan Xi Province, China, between December 2022 and July 2023. 464 non-COVID-19 patients within the same period were supplemented, divided into groups with and without AITD. At hospital admission (baseline), 3- and 6-month follow-ups, we presented a dynamic description and correlation analysis of thyroid-inflammatory-autoimmune derangements in patients with AITD. ResultsA total of 2,082 COVID-19 patients diagnosed with AITD and 257 cases without AITD were included in the study, and 464 non-COVID-19 patients were supplemented, dividing into 14 AITD and 450 non-AITD cases. We found that COVID-19 infection was closely associated with thyroid dysfunction (χ2 = 1518.129, p = 0.000). AITD patients with COVID-19 showed a higher prevalence of symptoms and comorbidities and longer hospital stays at baseline than non-AITD patients with COVID-19 (p = 0.000, p = 0.000, and p = 0.000). The baseline free triiodothyronine (FT3), free thyroxine, and radioactive iodine uptake at 24 h in AITD cases significantly decreased (p = 0.000, p = 0.000, and p = 0.000), while thyroid stimulating hormone, thyroglobulin, reverse triiodothyronine (rT3), and thyroid antibodies varying elevated from the baseline to the follow-up (baseline: p = 0.000, p = 0.000, p = 0.000, p = 0.000, p = 0.000, and p = 0.000; 3-month follow-up: p = 0.000, p = 0.000, p = 0.000, p = 0.000, p = 0.030, and p = 0.000). C-reactive protein, calcitonin, interleukin-6, -8, -10, and tumor necrosis factor-α rose significantly at baseline (p = 0.000, p = 0.000, p = 0.000, p = 0.000, p = 0.000, and p = 0.000) in AITD. Interferon-α and interferon-γ at baseline showed a significant decrease (p = 0.000 and p = 0.000), and remained at low levels after 6 months (p = 0.000 and p = 0.000). FT3 and rT3 were positively and negatively correlated with hospitalization, respectively (r = -0.208 and 0.231; p = 0.000 and p = 0.000). ROC curves showed that FT3 and rT3 had better robustness in predicting severe COVID-19 prognosis (AUC = 0.801 and 0.705). Ordered logistic regression revealed that ORs were 0.370, 0.048, and 0.021 for AITD [(subacute thyroiditis, Grave's disease, and Hashimoto's thyroiditis compared to non-thyroidal illness syndrome (NTIS)] with COVID-19 risk, indicating that NTIS was the predominant risk factor for the severity of COVID-19. ConclusionsA robust association has been identified, wherein COVID-19 infection is closely associated with thyroid dysfunction, and the subsequent AITD may aggravate the poor prognosis of COVID-19.

OQuPy: A Python package to efficiently simulate non-Markovian open quantum systems with process tensors.

Non-Markovian dynamics arising from the strong coupling of a system to a structured environment is essential in many applications of quantum mechanics and emerging technologies. Deriving an accurate description of general quantum dynamics including memory effects is, however, a demanding task, prohibitive to standard analytical or direct numerical approaches. We present a major release of our open source software package, OQuPy (Open Quantum System in Python), which provides several recently developed numerical methods that address this challenging task. It utilizes the process tensor approach to open quantum systems (OQS) in which a single map, the process tensor, captures all possible effects of an environment on the system. The representation of the process tensor in a tensor network form allows for an exact yet highly efficient description of non-Markovian OQS (NM-OQS). The OQuPy package provides methods to (1) compute the dynamics and multi-time correlations of quantum systems coupled to single and multiple environments, (2) optimize control protocols for NM-OQS, (3) simulate interacting chains of NM-OQS, and (4) compute the mean-field dynamics of an ensemble of NM-OQS coupled to a common central system. Our aim is to provide an easily accessible and extensible tool for researchers of OQS in fields such as quantum chemistry, quantum sensing, and quantum information.

Abelian groups acting on the line

We study the action of finitely generated Abelian subgroups of Homeo+(R).We propose a presentation where the focus is on understanding the set of stabilizers, which yields a dynamical description of the action that is both elementary and self-contained.

Strong coupling yields abrupt synchronization transitions in coupled oscillators

Coupled oscillator networks often display transitions between qualitatively different phase-locked solutions—such as synchrony and rotating wave solutions—following perturbation or parameter variation. In the limit of weak coupling, these transitions can be understood in terms of commonly studied phase approximations. As the coupling strength increases, however, predicting the location and criticality of transition, whether continuous or discontinuous, from the phase dynamics may depend on the order of the phase approximation—or a phase description of the network dynamics that neglects amplitudes may become impossible altogether. Here we analyze synchronization transitions and their criticality systematically for varying coupling strength in theory and experiments with coupled electrochemical oscillators. First, we analyze bifurcations analysis of synchrony and splay states in an abstract phase model and discuss conditions under which synchronization transitions with different criticalities are possible. In particular, we show that such conditions can be understood by considering the relative contributions of higher harmonics to the phase dynamics. Second, we illustrate that transitions with different criticality indeed occur in experimental systems. Third, we highlight that the amplitude dynamics observed in the experiments can be captured in a numerical bifurcation analysis of delay-coupled oscillators. Our results showcase that reduced order phase models may miss important features that one would expect in the dynamics of the full system. Published by the American Physical Society 2024

Data-driven discovery of dynamics from time-resolved coherent scattering

Coherent X-ray scattering (CXS) techniques are capable of interrogating dynamics of nano- to mesoscale materials systems at time scales spanning several orders of magnitude. However, obtaining accurate theoretical descriptions of complex dynamics is often limited by one or more factors—the ability to visualize dynamics in real space, computational cost of high-fidelity simulations, and effectiveness of approximate or phenomenological models. In this work, we develop a data-driven framework to uncover mechanistic models of dynamics directly from time-resolved CXS measurements without solving the phase reconstruction problem for the entire time series of diffraction patterns. Our approach uses neural differential equations to parameterize unknown real-space dynamics and implements a computational scattering forward model to relate real-space predictions to reciprocal-space observations. This method is shown to recover the dynamics of several computational model systems under various simulated conditions of measurement resolution and noise. Moreover, the trained model enables estimation of long-term dynamics well beyond the maximum observation time, which can be used to inform and refine experimental parameters in practice. Finally, we demonstrate an experimental proof-of-concept by applying our framework to recover the probe trajectory from a ptychographic scan. Our proposed framework bridges the wide existing gap between approximate models and complex data.

Spin Dynamics of Radical Pairs Using the Stochastic Schrödinger Equation in MolSpin.

The chemical reactivity of radical pairs is strongly influenced by the interactions of electronic and nuclear spins. A detailed understanding of these effects requires a quantum description of the spin dynamics that considers spin-dependent reaction rates, interactions with external magnetic fields, spin-spin interactions, and the loss of spin coherence caused by coupling to a fluctuating environment. Modeling real chemical and biochemical systems, which frequently involve radicals with multinuclear spin systems, poses a severe computational challenge. Here, we implement a method based on the stochastic Schrödinger equation in the software package MolSpin. Large electron-nuclear spin systems can be simulated efficiently, with asymmetric spin-selective recombination reactions, anisotropic hyperfine interactions, and nonzero exchange and dipolar couplings. Spin-relaxation can be modeled using the stochastic time-dependence of spin interactions determined by molecular dynamics and quantum chemical calculations or by allowing rate coefficients to be explicitly time-dependent. The flexibility afforded by this approach opens new avenues for exploring the effects of complex molecular motions on the spin dynamics of chemical transformations.

Short-time and long-time dynamics of the zeolite films in the light of alternative diffusion models

Analysis of the dynamics of diffusion of two mixtures of hydrogen and heavier components through a flat zeolite film was carried out. The concepts of short-time and long-time dynamics were introduced. They derive from the observation of temporal trajectories obtained by applying abrupt perturbations of concentrations on one side of the membrane.An alternative description of the diffusion dynamics was proposed compared to the commonly accepted model that employs a matrix of thermodynamic factors Γ. In this way, the difficulty of analytical determination of this matrix for more complex adsorption equilibria was eliminated.The model equations were expressed in a matrix form, which enables its easy extension to any number of components. The proposed research methodology was illustrated using an example of diffusion of two selected mixtures, i.e. {H2, n-C4H10} and {H2, CO2} in silicalite-1. These gaseous mixtures have applications in specific technological processes.The two models were used to perform quantitative benchmarking of short-time and long-time dynamics of flat zeolite membranes. The assumed boundary conditions correspond to a semi-batch Wicke-Kallenbach diffusion cell. It was demonstrated that the phenomena occurring in short-time and long-time diffusion represent two different dynamic features important from a scientific and practical point of view.A quantitative measure for degree of confinement was proposed based on dynamic adsorption-desorption hysteresis.

Fronto-Central Changes in Multiple Frequency Bands in Active Tactile Width Discrimination Task.

The neural basis of tactile processing in humans has been extensively studied; however, the neurophysiological basis of human width discrimination remains relatively unexplored. In particular, the changes that occur in neural networks underlying active tactile width discrimination learning have yet to be described. Here, it is hypothesized that subjects learning to perform the active version of the width discrimination task would present changes in behavioral data and in the neurophysiological activity, specifically in networks of electrodes relevant for tactile and motor processing. The specific hypotheses tested here were that the performance and response latency of subjects would change between the first and the second blocks; the power of the different frequency bands would change between the first and the second blocks; electrode F4 would encode task performance and response latency through changes in the power of the delta, theta, alpha, beta, and low-gamma frequency bands; the relative power in the alpha and beta frequency bands in electrodes C3 and C4 (Interhemispheric Spectral Difference-ISD) would change because of learning between the first and the second blocks. To test this hypothesis, we recorded and analyzed electroencephalographic (EEG) activity while subjects performed a session where they were tested twice (i.e., two different blocks) in an active tactile width discrimination task using their right index finger. Subjects (n = 18) presented high performances (high discrimination accuracy) already in their first block, and therefore no significant improvements were found in the second block. Meanwhile, a reduction in response latency was observed between the two blocks. EEG recordings revealed an increase in power for the low-gamma frequency band (30-45 Hz) for electrodes F3 and C3 from the first to the second block. This change was correlated with neither performance nor latency. Analysis of the neural activity in electrode F4 revealed that the beta frequency band encoded the subjects' performance. Meanwhile, the delta frequency band in the same electrode revealed a complex pattern where blocks appeared clustered in two different patterns: an Upper Pattern (UP), where power and latency were highly correlated (Rho = 0.950), and a sparser and more uncorrelated Lower Pattern (LP). Blocks belonging to the UP or LP patterns did not differ in performance and were not specific to the first or the second block. However, blocks belonging to the LP presented an increase in response latency, increased variability in performance, and an increased ISD in alpha and beta frequency bands for the pair of electrodes C3-C4, suggesting that the LP may reflect a state related to increased cognitive load or task difficulty. These results suggest that changes in performance and latency in an active tactile width discrimination task are encoded in the delta, alpha, beta, and low-gamma frequency bands in a fronto-central network. The main contribution of this study is therefore related to the description of neural dynamics in frontal and central networks involved in the learning process of active tactile width discrimination.

Entanglement membrane in exactly solvable lattice models

Entanglement membrane theory is an effective coarse-grained description of entanglement dynamics and operator growth in chaotic quantum many-body systems. The fundamental quantity characterizing the membrane is the entanglement line tension. However, determining the entanglement line tension for microscopic models is in general exponentially difficult. We compute the entanglement line tension in a recently introduced class of exactly solvable yet chaotic unitary circuits, so-called generalized dual-unitary circuits, obtaining a nontrivial form that gives rise to a hierarchy of velocity scales with vE<vB. For the lowest level of the hierarchy, L¯2 circuits, the entanglement line tension can be computed entirely, while for the higher levels the solvability is reduced to certain regions in spacetime. This partial solvability enables us to place bounds on the entanglement velocity. We find that L¯2 circuits saturate certain bounds on entanglement growth that are also saturated in holographic models. Furthermore, we relate the entanglement line tension to temporal entanglement and correlation functions. We also develop methods of constructing generalized dual-unitary gates, including constructions based on complex Hadamard matrices that exhibit additional solvability properties and constructions that display behavior unique to local dimension greater than or equal to three. Our results shed light on entanglement membrane theory in microscopic Floquet lattice models and enable us to perform nontrivial checks on the validity of its predictions by comparison to exact and numerical calculations. Moreover, they demonstrate that generalized dual-unitary circuits display a more generic form of information dynamics than dual-unitary circuits. Published by the American Physical Society 2024

Comparison between EEG and MEG of static and dynamic resting-state networks.

The characterisation of resting-state networks (RSNs) using neuroimaging techniques has significantly contributed to our understanding of the organisation of brain activity. Prior work has demonstrated the electrophysiological basis of RSNs and their dynamic nature, revealing transient activations of brain networks with millisecond timescales. While previous research has confirmed the comparability of RSNs identified by electroencephalography (EEG) to those identified by magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI), most studies have utilised static analysis techniques, ignoring the dynamic nature of brain activity. Often, these studies use high-density EEG systems, which limit their applicability in clinical settings. Addressing these gaps, our research studies RSNs using medium-density EEG systems (61 sensors), comparing both static and dynamic brain network features to those obtained from a high-density MEG system (306 sensors). We assess the qualitative and quantitative comparability of EEG-derived RSNs to those from MEG, including their ability to capture age-related effects, and explore the reproducibility of dynamic RSNs within and across the modalities. Our findings suggest that both MEG and EEG offer comparable static and dynamic network descriptions, albeit with MEG offering some increased sensitivity and reproducibility. Such RSNs and their comparability across the two modalities remained consistent qualitatively but not quantitatively when the data were reconstructed without subject-specific structural MRI images.

The adaptive consensus reaching process with the ER-PSO interaction mechanism and its application in public charging stations evaluation

BackgroundPublic charging stations (PCSs) have become integral public infrastructure in China, responding to the considerable and rapidly growing demand. Evaluating the service quality of PCSs by a diverse user base has become essential for the effective management of PCSs. ObjectivesIn the group evaluation of PCSs' service quality, which encompasses broad public interests, consensus reaching process (CRP) is commonly utilized to manage group conflicts and achieve evaluation consensus. The intricate interactions among decision makers (DMs) significantly influence the evolution and resilience of collective viewpoints. Against this backdrop, this research aims to develop an adaptive CRP to enhance the discriminatory power of group consensus and improve consensus robustness in the presence of non-cooperative behavior. MethodologyWith the above objectives, this study proposes an adaptive CRP supported by a novel interaction mechanism, consisting of two stages: (1) In the interaction network generation stage, based on the Erdős-Rényi (ER) random network model, innovative interaction signals are generated to guide the generation of an ER-based interaction network, enhancing the dissemination of dominant viewpoints, and constraining the influence of minority opinions. (2) In the decision opinion interaction stage, based on the Particle Swarm Optimization (PSO) algorithm, utilizing novel dual individual fitness functions, the PSO algorithm is employed to formulate the expected updated belief, driving the internal evolution and convergence of opinions. The proposed adaptive CRP is implemented in the group evaluation of PCSs' service quality in Nanjing. FindingsComparative analysis reveals that the proposed CRP offers significant advantages in consensus efficiency, the discriminatory power of group consensus, and consensus robustness in the presence of non-cooperative behavior compared to existing CRPs. NoveltyDiffering from previous CRP research primarily focuses on minimizing adjustment scales, this study emphasizes the adaptive evolution and convergence of opinions, providing a more suitable description of group opinion dynamics. Additionally, from a methodological perspective, this research utilizes the ER network model and the PSO algorithm to support the adaptive CRP at both structural and preference levels, enhancing consensus discriminatory power and robustness.

Analysis of Glucose Responsive Glucagon Therapeutics using Computational Models of the Glucoregulatory System.

Glucose-responsive glucagon (GRG) therapeutics are a promising technology for reducing the risk of severe hypoglycemia as a complication of diabetes mellitus. Herein, the performance of candidate GRGs in the literature by modeling the kinetics of activation and connecting them as input into physiological glucoregulatory models is evaluated and projected the two distinct GRG designs, experimental results reported in Wu etal. (GRG-I) and Webber etal. (GRG-II) is considered. Both are evaluated using a multi-compartmental glucoregulatory model (IMPACT) and used to compare in-vivo experimental data of therapeutic performance in rats and mice. For GRG-I and GRG-II, the total integrated glucose material balances are overestimated by 41.5% ± 14% and underestimated by 24.8% ± 16% compared to in-vivo time-course data, respectively. These large differences to the relatively simple computational descriptions of glucagon dynamics in the model, which underscores the urgent need for improved glucagon models is attributed. Additionally, therapeutic insulin and glucagon infusion pumps are modeled for type 1 diabetes mellitus (T1DM) human subjects to extend the results to additional datasets. These observations suggest that both the representative physiological and non-physiological models considered in this work require additional refinement to successfully describe clinical data that involve simultaneous, coupled insulin, glucose, and glucagon dynamics.

Modification of the electronic oscillation spectrum in metallic clusters, caused by the presence of a large spatial scale of inhomogeneity

This work presents a theoretical analysis of the oscillations of a system of degenerate electrons in metallic submicron clusters, taking into account the presence of a large-scale spatial inhomogeneity caused by quantum shell effects. The analysis was carried out in a model formulation in which the large-scale effect was taken into account by the presence of an effective external field acting on the electrons. Two approaches to the description of electron dynamics were considered: kinetic and hydrodynamic. The results obtained within these approaches are in good qualitative and quantitative agreement with each other and demonstrate that for certain cluster sizes, a spectrum of oscillations of the electronic system appears with frequencies differing by the value Δω ∼ ω0/R20 and localized in the vicinity of plasmon frequency ω0 (R0, radius of cluster). The impact of the large-scale effect on the processes of light and electron scattering by metal clusters is estimated. It is shown that maxima appear in the absorption spectrum of the electromagnetic waves, and they turn out to be shifted from the plasmon frequency by value Δω ∼ ω0/R20. Within the framework of the Born approximation, it is shown that a maximum at kR0 ∼ 4π appears in the cross section of elastic scattering of electrons on a metal cluster. The results obtained are important from the point of view of interpreting experiments on plasmonic structures, in particular, on metal films.

Multichannel Quantum Defect Theory with a Frame Transformation for Ultracold Atom-Molecule Collisions in Magnetic Fields.

We extend the powerful formalism of multichannel quantum defect theory combined with a frame transformation to ultracold atom-molecule collisions in magnetic fields. By solving the coupled-channel equations with hyperfine and Zeeman interactions omitted at short range, the extended theory enables a drastically simplified description of the intricate quantum dynamics of ultracold molecular collisions in terms of a small number of short-range parameters. We apply the formalism to ultracold Mg+NH collisions in a magnetic field, achieving a 10^{4}-fold reduction in computational effort.

Switched Observer Design for a Class of Non-Linear Systems

This paper is concerned with the switched observer design for a class of systems subject to locally Lipschitz non-linearities. By performing a suitable description of the estimation error dynamics into a linear parameter varying (LPV) system representation, sufficient conditions for the existence of a switching output injection gain are proposed such that the asymptotic stability of the estimation error is guaranteed. These conditions can be conveniently expressed by means of linear matrix inequalities (LMIs), which are easily computationally tractable. A numerical example is provided to show the favorable performance achieved by the proposed observer, which can be applied to a large class of non-linear systems.

Quantum state over time is unique

The conventional framework of quantum theory treats space and time in vastly different ways by representing temporal correlations via quantum channels and spatial correlations via multipartite quantum states—an imbalance absent in classical probability theory. Since Leifer and Spekkens [] called for a causally neutral formulation of quantum theory in their seminal work, numerous attempts have been made to rectify this asymmetry by proposing a dynamical description of a quantum system encapsulated by a static , without a definite consensus on which one is most appropriate. In this paper, we propose sets of operationally motivated axioms for quantum states over time alternative to the ones proposed by Fullwood and Parzygnat [], which we show is unable to induce a unique quantum state over time. Our proposed axioms are better suited to describe quantum states over any spacetime regions beyond two points. Through this reformulation, we prove that the Fullwood-Parzygnat state over time uniquely satisfies all these operational axioms, unifying the bipartite spacetime correlations of quantum systems. Published by the American Physical Society 2024

Intermediate-times dilemma for open quantum system: Filtered approximation to the refined weak-coupling limit.

The famous Davies-GKSL secular Markovian master equationis tremendously successful in approximating the evolution of open quantum systems in terms of just a few parameters. However, the fully secular Davies-GKSL equationfails to accurately describe timescales short enough, i.e., comparable to the inverse of differences of frequencies present in the system of interest. A complementary approach that works well for short times but is not suitable after this short interval is known as the quasisecular master equation. Still, both approaches fail to have any faithful dynamics in the intermediate-time interval. Simultaneously, descriptions of dynamics that apply to the aforementioned "gray zone" often are computationally much more complex than master equationsor are mathematically not well-structured. The filtered approximation (FA) to the refined weak-coupling limit has the simplistic spirit of the Davies-GKSL equationand allows capturing the dynamics in the intermediate-time regime. At the same time, our non-Markovian equationyields completely positive dynamics. We exemplify the performance of the FA equationin the cases of the spin-boson system and qutrit-boson system in which two distant timescales appear.

Describing group evolution in temporal data using multi-faceted events

Groups—such as clusters of points or communities of nodes—are fundamental when addressing various data mining tasks. In temporal data, the predominant approach for characterizing group evolution has been through the identification of “events”. However, the events usually described in the literature, e.g., shrinks/growths, splits/merges, are often arbitrarily defined, creating a gap between such theoretical/predefined types and real-data group observations. Moving beyond existing taxonomies, we think of events as “archetypes” characterized by a unique combination of quantitative dimensions that we call “facets”. Group dynamics are defined by their position within the facet space, where archetypal events occupy extremities. Thus, rather than enforcing strict event types, our approach can allow for hybrid descriptions of dynamics involving group proximity to multiple archetypes. We apply our framework to evolving groups from several face-to-face interaction datasets, showing it enables richer, more reliable characterization of group dynamics with respect to state-of-the-art methods, especially when the groups are subject to complex relationships. Our approach also offers intuitive solutions to common tasks related to dynamic group analysis, such as choosing an appropriate aggregation scale, quantifying partition stability, and evaluating event quality.

MULTI-AGENT SIMULATION MODEL OF INFECTIOUS DISEASE SPREAD

The aim of the research is to develop a multi-agent simulation model for predicting the spread of infectious diseases, particularly COVID-19. In the context of the COVID-19 pandemic, there emerged an urgent need to create tools for forecasting and analyzing the dynamics of epidemics, as well as for evaluating the effectiveness of management decisions. The use of mathematical models in this process allows for an adequate description of the infection spread dynamics, which is essential for making informed decisions. The article discusses traditional approaches to epidemic modeling, such as the predator-prey model and the compartmental SIR (Susceptible-Infectious-Recovered) model. The predator-prey model describes the interaction between two species in an ecosystem using differential equations, which allows for modeling population dynamics. The compartmental SIR model divides the population into three groups: susceptible, infected, and recovered, which enables the analysis of the spread of infectious diseases. However, these models have limitations, particularly due to assumptions about population homogeneity and constant parameters. To more accurately model complex epidemic processes, a multi-agent simulation model was developed. In this model, agents interact within a defined area, mimicking real conditions of infection spread. Agents are divided into three classes: healthy, infected, and recovered. The movement of agents is modeled using random walk in a two-dimensional space, taking into account the possibility of contact between them, which can lead to infection. Infected agents transition to the recovered class after a certain period of illness and can no longer be infected. Modeling results showed that the multi-agent model allows for more accurate prediction of infection spread dynamics. Numerous experiments were conducted, demonstrating the model's adequacy in replicating the infection process, peak infection rates, and recovery periods. The influence of various parameters, such as the duration of illness, on the epidemic dynamics was investigated. The obtained results confirm that considering individual characteristics and behavioral traits of agents improves the accuracy of modeling. This allows the multi-agent simulation model to be used for developing effective control strategies and predicting the spread of infectious diseases, which can be useful for making management decisions in real pandemic conditions.