Analysing the combined health, social and economic impacts of the corovanvirus pandemic using agent-based social simulation

使用基于智能体的仿真评估疫情期间的供应链韧性

Evaluating Supply Chain Resilience During Pandemic Using Agent-based Simulation

本文提出融合扩展SIR模型与供需经济模型的智能体仿真，评估疫情下供应链策略，并用机器学习估计企业近似最优韧性方案。

A general framework for optimising cost-effectiveness of pandemic response under partial intervention measures

The COVID-19 pandemic created enormous public health and socioeconomic challenges. The health effects of vaccination and non-pharmaceutical interventions (NPIs) were often contrasted with significant social and economic costs. We describe a general framework aimed to derive adaptive cost-effective interventions, adequate for both recent and emerging pandemic threats. We also quantify the net health benefits and propose a reinforcement learning approach to optimise adaptive NPIs. The approach utilises an agent-based model simulating pandemic responses in Australia, and accounts for a heterogene…

ABM-SIRTEM：一种用于大流行应对的基于智能体的混合模型与流行病学模型

ABM-SIRTEM: A Hybrid Agent-Based and Epidemiological Model for Pandemic Response

提出混合模型ABM-SIRTEM，结合流行病学与基于智能体方法，在个体层面纳入职业、经济生产力和福利，动态建模干预遵从性，并用美国四州数据校准，以研究大流行应对中疾病传播与社会经济行为的相互作用。

City-scale Simulation of Covid-19 Pandemic and Intervention Policies using Agent-based Modelling

During the Covid-19 pandemic, most governments across the world imposed policies like lock-down of public spaces and restrictions on people's movements to minimize the spread of the virus through physical contact. However, such policies have grave social and economic costs, and so it is important to pre-assess their impacts. In this work we aim to visualize the dynamics of the pandemic in a city under different intervention policies, by simulating the behavior of the residents. We develop a very detailed agent-based model for a city, including its residents, physical and social spaces like hom…

A Microscopic Pandemic Simulator for Pandemic Prediction Using Scalable Million-Agent Reinforcement Learning

Microscopic epidemic models are powerful tools for government policy makers to predict and simulate epidemic outbreaks, which can capture the impact of individual behaviors on the macroscopic phenomenon. However, existing models only consider simple rule-based individual behaviors, limiting their applicability. This paper proposes a deep-reinforcement-learning-powered microscopic model named Microscopic Pandemic Simulator (MPS). By replacing rule-based agents with rational agents whose behaviors are driven to maximize rewards, the MPS provides a better approximation of real world dynamics. To …