arXiv subjects

Caterina Scoglio

Publications and source records attributed to Caterina Scoglio.

At least 19 recordsLinked to original sources

arXiv2026

A two-parameter, minimal-data model to predict dengue cases: the 2022-2023 outbreak in Florida, USA

Reliable and timely dengue predictions can provide an actionable lead time for targeted vector control and clinical preparedness, reducing preventable disease and health-system costs in at-risk communities. However, many forecasting approaches depend on site-specific covariates and entomological surveillance, which limits portability to data-sparse settings. In this work, we mathematically prove that a parabolic ICC structure, previously established for the basic SIR model, also holds in a substantially more complex model: a two-population (human-mosquito) four-serotype dengue transmission model with primary and secondary infections and mild/severe disease classes. To predict the number of new cases, we propose a data-parsimonious (DP) framework built on the incidence-cumulative cases (ICC) curve that requires only the human incidence time series of the target season and estimates only two parameters, thereby reducing estimation noise and computational burden. We further develop a Bayesian extension that accounts for case-reporting and fitting uncertainty, producing calibrated predictive intervals. The Bayesian model yields improved predictive performance compared to the parabolic ICC model. We evaluate the framework for dengue outbreaks in Florida in 2022-2023, where standardized clinical tests and reporting support accurate case determination.

stat.AP↗

arXiv2026

EpidemIQs: Prompt-to-Paper LLM Agents for Epidemic Modeling and Analysis

Large Language Models (LLMs) offer new opportunities to accelerate complex interdisciplinary research domains. Epidemic modeling, characterized by its complexity and reliance on network science, dynamical systems, epidemiology, and stochastic simulations, represents a prime candidate for leveraging LLM-driven automation. We introduce EpidemIQs, a novel multi-agent LLM framework that integrates user inputs and autonomously conducts literature review, analytical derivation, network modeling, mechanistic modeling, stochastic simulations, data visualization and analysis, and finally documentation of findings in a structured manuscript, through five predefined research phases. We introduce two types of agents: a scientist agent for planning, coordination, reflection, and generation of final results, and a task-expert agent to focus exclusively on one specific duty serving as a tool to the scientist agent. The framework consistently generated complete reports in scientific article format. Specifically, using GPT 4.1 and GPT 4.1 Mini as backbone LLMs for scientist and task-expert agents, respectively, the autonomous process completes with average total token usage 870K at a cost of about $1.57 per study, successfully executing all phases and final report. We evaluate EpidemIQs across several different epidemic scenarios, measuring computational cost, workflow reliability, task success rate, and LLM-as-Judge and human expert reviews to estimate the overall quality and technical correctness of the generated results. Through our experiments, the framework consistently addresses evaluation scenarios with an average task success rate of 79%. We compare EpidemIQs to an iterative single-agent LLM, benefiting from the same system prompts and tools, iteratively planning, invoking tools, and revising outputs until task completion. The comparisons suggest a consistently higher performance of EpidemIQs.

cs.SI↗

arXiv2025

A Data-Parsimonious Model for Long-Term Risk Assessments of West Nile Virus Spillover

Many West Nile virus (WNV) forecasting frameworks incorporate entomological or avian surveillance data, which may be unavailable in some regions. We introduce a novel data-parsimonious probabilistic model to predict both the timing of outbreak onset and the seasonal severity of WNV spillover. Our approach combines a temperature-driven compartmental model of WNV with nonparametric kernel density estimation methods to construct a joint probability density function and a Poisson rate surface as function of mosquito abundance and normalized cumulative temperature. Calibrated on human incidence records, the model produces reliable forecasts several months before the transmission season begins, supporting proactive mitigation efforts. We evaluated the framework across three counties in California (Orange, Los Angeles, and Riverside), two in Texas (Dallas and Harris), and one in Florida (Duval), representing completely different ecology and distinct climatic regimes, and observed strong agreement across multiple performance metrics.

stat.AP↗

arXiv2025

Probabilistic Assessment of West Nile Virus Spillover Risk Using a Compartmental Mechanistic Model

This paper presents a novel probabilistic approach for assessing the risk of West Nile Disease (WND) spillover to the human population. The assessment has been conducted under two different scenarios: (1) assessment of the onset of spillover, and (2) assessment of the severity of the epidemic after the onset of the disease. A compartmental model of differential equations is developed to describe the disease transmission mechanism, and a probability density function for pathogen spillover to humans is derived based on the model for the assessment of the risk of the spillover onset and the severity of the epidemic. The prediction strategy involves making a long-term forecast and then updating it with a short-term (lead time of two weeks or daily). The methodology is demonstrated using detailed outbreak data from high-case counties in California, including Orange County, Los Angeles County, and Kern County. The predicted results are compared with actual infection dates reported by the California Department of Public Health for 2022-2024 to assess prediction accuracy. The performance accuracy is evaluated using a logarithmic scoring system and compared with one of the most renowned predictive models to assess its effectiveness. In all prediction scenarios, the model demonstrated strong performance. Lastly, the method is applied to explore the impact of global warming on spillover risk, revealing an increasing trend in the number of high-risk days and a shift toward a greater proportion of these days over time for the onset of the disease.

stat.AP↗

arXiv2024

FastGEMF: Scalable High-Speed Simulation of Stochastic Spreading Processes over Complex Multilayer Networks

Predicting the spread of processes across complex multi-layered networks has long challenged researchers due to the intricate interplay between network structure and propagation dynamics. Each layer of these networks possesses unique characteristics, further complicating analysis. To authors' knowledge, a comprehensive framework capable of simulating various spreading processes across different layers, particularly in networks with millions of nodes and connections, has been notably absent. This study introduces a novel framework that efficiently predicts Markov Chain processes over large-scale networks, while significantly reducing time and space complexity. This approach enables exact simulation of spreading processes across extensive real-world multi-layer networks, accounting for diverse influencers on each layer. FastGEMF provides a baseline framework for exact simulating stochastic spread processes, facilitating comparative analysis of models across diverse domains, from epidemiology to social media dynamics.

cs.SI↗

arXiv2024

SIR Epidemics in Interconnected Networks: threshold curve and phase transition

To simplify mathematical models of disease spread, we often assume equal contact rates among hosts, but real-world scenarios differ. Network-based frameworks help capture these complexities and structural variations in actual systems. We explore two scenarios involving Susceptible-Infected-Recovered (SIR) dynamics in interconnected networks. First, we study how the epidemic threshold of a contact network changes when coupled with another network, holding infection strength constant. Our model treats both contact networks and interconnections generically. We depict the epidemic threshold curve for interconnected networks, accounting for initial infection in either or both networks. If normalized infection strengths surpass this threshold curve, the disease spreads; below it, it does not, regardless of interconnection level. In the second scenario, we investigate disease spillover, where a novel host population network is affected by a reservoir network. A clear phase transition occurs when the number of links or inter-network infection rate exceeds a threshold while other parameters remain fixed. Spillover exhibits two regimes: major and minor, based on interpopulation links and inter-network infection strength. High spillover probability occurs in the major region and low in the minor. The threshold link count varies with network topology for similar infected numbers in the reservoir network. In sum, our work enhances understanding of SIR dynamics in interconnected networks, offering insights into epidemic behavior in complex systems.

math.DS↗

arXiv2023

Generalized epidemic model incorporating non-Markovian infection processes and waning immunity

The Markovian approach, which assumes exponentially distributed interinfection times, is dominant in epidemic modeling. However, this assumption is unrealistic as an individual's infectiousness depends on its viral load and varies over time. In this paper, we present a Susceptible-Infected-Recovered-Vaccinated-Susceptible epidemic model incorporating non-Markovian infection processes. The model can be easily adapted to accurately capture the generation time distributions of emerging infectious diseases, which is essential for accurate epidemic prediction. We observe noticeable variations in the transient behavior under different infectiousness profiles and the same basic reproduction number R0. The theoretical analyses show that only R0 and the mean immunity period of the vaccinated individuals have an impact on the critical vaccination rate needed to achieve herd immunity. A vaccination level at the critical vaccination rate can ensure a very low incidence among the population in case of future epidemics, regardless of the infectiousness profiles.

q-bio.PE↗

arXiv2022

Feature Learning and Network Structure from Noisy Node Activity Data

In the studies of network structures, much attention has been devoted to developing approaches to reconstruct networks and predict missing links when edge-related information is given. However, such approaches are not applicable when we are only given noisy node activity data with missing values. This work presents an unsupervised learning framework to learn node vectors and construct networks from such node activity data. First, we design a scheme to generate random node sequences from node context sets, which are generated from node activity data. Then, a three-layer neural network is adopted training the node sequences to obtain node vectors, which allow us to construct networks and capture nodes with synergistic roles. Furthermore, we present an entropy-based approach to select the most meaningful neighbors for each node in the resulting network. Finally, the effectiveness of the method is validated through both synthetic and real data.

cs.NI↗

arXiv2021

Layer reconstruction and missing link prediction of multilayer network with a Maximum A Posteriori estimation

A multilayer network is composed of multiple layers, where different layers have the same set of vertices but represent different types of interactions. Nevertheless, some layers are interdependent or structurally similar in the multilayer network. In this paper, we present a maximum a posteriori estimation based model to reconstruct a specific layer in the multilayer network. The SimHash algorithm is used to compute the similarities between various layers. And the layers with similar structures are used to determine the parameters of the conjugate prior. With this model, we can also predict missing links and direct experiments for finding potential links. We test the method through two real multilayer networks, and the results show that the maximum a posteriori estimation is promising in reconstructing the layer of interest even with a large number of missing links.

cs.SI↗

arXiv2021

A principled approach for weighted multilayer network aggregation

A multilayer network depicts different types of interactions among the same set of nodes. For example, protease networks consist of five to seven layers, where different layers represent distinct types of experimentally confirmed molecule interactions among proteins. In a multilayer protease network, the co-expression layer is obtained through the meta-analysis of transcriptomic data from various sources and platforms. While in some researches the co-expression layer is in turn represented as a multilayered network, a fundamental problem is how to obtain a single-layer network from the corresponding multilayered network. This process is called multilayer network aggregation. In this work, we propose a maximum a posteriori estimation-based algorithm for multilayer network aggregation. The method allows to aggregate a weighted multilayer network while conserving the core information of the layers. We evaluate the method through an unweighted friendship network and a multilayer gene co-expression network. We compare the aggregated gene co-expression network with a network obtained from conflated datasets and a network obtained from averaged weights. The Von Neumann entropy is adopted to compare the mixedness of the three networks, and, together with other network measurements, shows the effectiveness of the proposes method.

cs.SI↗

arXiv2020

Robustness of supply chain networks against underload cascading failures

In today's global economy, supply chain (SC) entities have become increasingly interconnected with demand and supply relationships due to the need for strategic outsourcing. Such interdependence among firms not only increases efficiency but also creates more vulnerabilities in the system. Natural and human-made disasters such as floods and transport accidents may halt operations and lead to economic losses. Due to the interdependence among firms, the adverse effects of any disruption can be amplified and spread throughout the systems. This paper aims at studying the robustness of SC networks against cascading failures. Considering the upper and lower bound load constraints, i.e., inventory and cost, we examine the fraction of failed entities under load decrease and load fluctuation scenarios. The simulation results obtained from synthetic networks and a European supply chain network [1] both confirm that the recovery strategies of surplus inventory and backup suppliers often adopted in actual SCs can enhance the system robustness, compared with the system without the recovery process. In addition, the system is relatively robust against load fluctuations but is more fragile to demand shocks. For the underload-driven model without the recovery process, we found an occurrence of a discontinuous phase transition. Differently from other systems studied under overload cascading failures, this system is more robust for power-law distributions than uniform distributions of the lower bound parameter for the studied scenarios.

physics.soc-ph↗

arXiv2019

Generation of swine movement network and analysis of efficient mitigation strategies for African swine fever virus

Animal movement networks are essential in understanding and containing the spread of infectious diseases in farming industries. Due to its confidential nature, movement data for the US swine farming population is not readily available. Hence, we propose a method to generate such networks from limited data available in the public domain. As a potentially devastating candidate, we simulate the spread of African swine fever virus (ASFV) in our generated network and analyze how the network structure affects the disease spread. We find that high in-degree farm operations (i.e., markets) play critical roles in the disease spread. We also find that high in-degree based targeted isolation and hypothetical vaccinations are more effective for disease control compared to other centrality-based mitigation strategies. The generated networks can be made more robust by validation with more data whenever more movement data will be available.

q-bio.PE↗

arXiv2019

Generalized group-based epidemic model for spreading processes on networks: GgroupEM

We develop a generalized group-based epidemic model (GgroupEM) framework for any compartmental epidemic model (for example; susceptible-infected-susceptible, susceptible-infected-recovered, susceptible-exposed-infected-recovered). Here, a group consists of a collection of individual nodes. This model can be used to understand the important dynamic characteristics of a stochastic epidemic spreading over very large complex networks, being informative about the state of groups. Aggregating nodes by groups, the state space becomes smaller than the individual-based approach at the cost of aggregation error, which is strongly bounded by the isoperimetric inequality. We also develop a mean-field approximation of this framework to further reduce the state-space size. Finally, we extend the GgroupEM to multilayer networks. Since the group-based framework is computationally less expensive and faster than an individual-based framework, then this framework is useful when the simulation time is important.

physics.soc-ph↗

arXiv2018

SIS Epidemics in Multilayer-based Temporal Networks

To improve the accuracy of network-based SIS models we introduce and study a multilayer representation of a time-dependent network. In particular, we assume that individuals have their long-term (permanent) contacts that are always present, identifying in this way the first network layer. A second network layer also exists, where the same set of nodes can be connected by occasional links, created with a given probability. While links of the first layer are permanent, a link of the second layer is only activated with some probability and under the condition that the two nodes, connected by this link, are simultaneously participating to the temporary link. We develop a model for the SIS epidemic on this time-dependent network, analyze equilibrium and stability of the corresponding mean-field equations, and shed some light on the role of the temporal layer on the spreading process.

physics.soc-ph↗

arXiv2018

Generalization of Effective Conductance Centrality for Egonetworks

We study the popular centrality measure known as effective conductance or in some circles as information centrality. This is an important notion of centrality for undirected networks, with many applications, e.g., for random walks, electrical resistor networks, epidemic spreading, etc. In this paper, we first reinterpret this measure in terms of modulus (energy) of families of walks on the network. This modulus centrality measure coincides with the effective conductance measure on simple undirected networks, and extends it to much more general situations, e.g., directed networks as well. Secondly, we study a variation of this modulus approach in the egocentric network paradigm. Egonetworks are networks formed around a focal node (ego) with a specific order of neighborhoods. We propose efficient analytical and approximate methods for computing these measures on both undirected and directed networks. Finally, we describe a simple method inspired by the modulus point-of-view, called shell degree, which proved to be a useful tool for network science.

physics.data-an↗

arXiv2016

Network clustering and community detection using modulus of families of loops

We study the structure of loops in networks using the notion of modulus of loop families. We introduce a new measure of network clustering by quantifying the richness of families of (simple) loops. Modulus tries to minimize the expected overlap among loops by spreading the expected link-usage optimally. We propose weighting networks using these expected link-usages to improve classical community detection algorithms. We show that the proposed method enhances the performance of certain algorithms, such as spectral partitioning and modularity maximization heuristics, on standard benchmarks.

cs.SI↗

arXiv2016

Delocalized Epidemics on Graphs: A Maximum Entropy Approach

The susceptible--infected--susceptible (SIS) epidemic process on complex networks can show metastability, resembling an endemic equilibrium. In a general setting, the metastable state may involve a large portion of the network, or it can be localized on small subgraphs of the contact network. Localized infections are not interesting because a true outbreak concerns network--wide invasion of the contact graph rather than localized infection of certain sites within the contact network. Existing approaches to localization phenomenon suffer from a major drawback: they fully rely on the steady--state solution of mean--field approximate models in the neighborhood of their phase transition point, where their approximation accuracy is worst; as statistical physics tells us. We propose a dispersion entropy measure that quantifies the localization of infections in a generic contact graph. Formulating a maximum entropy problem, we find an upper bound for the dispersion entropy of the possible metastable state in the exact SIS process. As a result, we find sufficient conditions such that any initial infection over the network either dies out or reaches a localized metastable state. Unlike existing studies relying on the solution of mean--field approximate models, our investigation of epidemic localization is based on characteristics of exact SIS equations. Our proposed method offers a new paradigm in studying spreading processes over complex networks.

physics.soc-ph↗

arXiv2016

GEMFsim: A Stochastic Simulator for the Generalized Epidemic Modeling Framework

The recently proposed generalized epidemic modeling framework (GEMF) \cite{sahneh2013generalized} lays the groundwork for systematically constructing a broad spectrum of stochastic spreading processes over complex networks. This article builds an algorithm for exact, continuous-time numerical simulation of GEMF-based processes. Moreover the implementation of this algorithm, GEMFsim, is available in popular scientific programming platforms such as MATLAB, R, Python, and C; GEMFsim facilitates simulating stochastic spreading models that fit in GEMF framework. Using these simulations one can examine the accuracy of mean-field-type approximations that are commonly used for analytical study of spreading processes on complex networks.

physics.soc-ph↗