Related papers: Epidemiological Agent-Based Modelling Software (Ep…
We describe Epilocal, a simple R program designed to automatically download the most recent data on reported infected SARS-CoV-2 cases for all Italian provinces and regions, and to provide a simple descriptive analysis. For each province…
During the ongoing COVID-19 pandemic, mathematical models of epidemic spreading have emerged as powerful tools to produce valuable predictions of the evolution of the pandemic, helping public health authorities decide which intervention…
In this paper we present ACEMod, an agent-based modelling framework for studying influenza epidemics in Australia. The simulator is designed to analyse the spatiotemporal spread of contagion and influenza spatial synchrony across the…
Reliable epidemiological reasoning requires synthesizing study evidence to infer disease burden, transmission dynamics, and intervention effects at the population level. Existing medical question answering benchmarks primarily emphasize…
AI agents are increasingly deployed as quasi-autonomous systems for specialized tasks, yet their potential as computational models of decision-making remains underexplored. We develop a generative AI agent to study repetitive policy…
Agent-based modelling (ABM), simulation (ABS), and distributed computation (ABC) are established methods. The Internet and Web-based technologies are suitable carriers. This paper is a technical report with some tutorial aspects of the…
Agent-Based Models are a powerful class of computational models widely used to simulate complex phenomena in many different application areas. However, one of the most critical aspects, poorly investigated in the literature, regards an…
The June software package is an open-source framework for the detailed simulation of epidemics based on social interactions in a virtual population reflecting age, gender, ethnicity, and socio-economic indicators in England. In this paper,…
The recent rapid development of large language models (LLMs) has sparked a new wave of technological revolution in medical artificial intelligence (AI). While LLMs are designed to understand and generate text like a human, autonomous agents…
The reproduction of realistic dynamics in financial markets is of great significance, as it enhances our understanding of market evolution beyond other physical processes, and facilitates the development and backtesting of investment…
Scientific research follows multi-turn, multi-step workflows that require proactively searching the literature, consulting figures and tables, and integrating evidence across papers to align experimental settings and support reproducible…
Incorporating decision-making dynamics during an outbreak poses a challenge for epidemiology, faced by several modeling approaches siloed by different disciplines. We propose an epi-economic model where high-frequency choices of individuals…
We present the R package SimInf which provides an efficient and very flexible framework to conduct data-driven epidemiological modeling in realistic large scale disease spread simulations. The framework integrates infection dynamics in…
Innovation diffusion has been studied extensively in a variety of disciplines, including sociology, economics, marketing, ecology, and computer science. Traditional literature on innovation diffusion has been dominated by models of…
World models have emerged as a unifying paradigm for learning latent dynamics, simulating counterfactual futures, and supporting planning under uncertainty. In this paper, we argue that computational epidemiology is a natural and…
Modelling epidemics using contact networks provides a significant improvement over classical compartmental models by explicitly incorporating the network of contacts. However, while network-based models describe disease spread on a given…
Developing artificial intelligence based assistive systems to aid Persons with Dementia (PwD) requires large amounts of training data. However, data collection poses ethical, legal, economic, and logistic issues. Synthetic data generation…
We propose a novel approach to the statistical analysis of stochastic simulation models and, especially, agent-based models (ABMs). Our main goal is to provide fully automated, model-independent and tool-supported techniques and algorithms…
This paper describes an agent-based model of epidemics dynamics. This model is willingly simplified, as its goal is not to predict the evolution of the epidemics, but to explain the underlying mechanisms in an interactive way. This model…
The challenges posed by epidemics and pandemics are immense, especially if the causes are novel. This article introduces a versatile open-source simulation framework designed to model intricate dynamics of infectious diseases across diverse…