Effect of intervention policies in an agent based spatial epidemic model of Gwalior

Effect of lockdown interventions to control the COVID-19 epidemic in India

The pandemic caused by the novel Coronavirus SARS-CoV2 has been responsible for life threatening health complications, and extreme pressure on healthcare systems. While preventive and definite curative medical interventions are yet to arrive, Non-Pharmaceutical Interventions (NPIs) like physical isolation, quarantine and drastic social measures imposed by governing agencies are effective in arresting the spread of infections in a population. In densely populated countries like India, lockdown interventions are partially effective due to social and administrative complexities. Using detailed de…

Modeling indoor-level non-pharmaceutical interventions during the COVID-19 pandemic: a pedestrian dynamics-based microscopic simulation approach

Mathematical modeling of epidemic spreading has been widely adopted to estimate the threats of epidemic diseases (i.e., the COVID-19 pandemic) as well as to evaluate epidemic control interventions. The indoor place is considered to be a significant epidemic spreading risk origin, but existing widely-used epidemic spreading models are usually limited for indoor places since the dynamic physical distance changes between people are ignored, and the empirical features of the essential and non-essential travel are not differentiated. In this paper, we introduce a pedestrian-based epidemic spreading…

A Microscopic Epidemic Model and Pandemic Prediction Using Multi-Agent Reinforcement Learning

This paper introduces a microscopic approach to model epidemics, which can explicitly consider the consequences of individual's decisions on the spread of the disease. We first formulate a microscopic multi-agent epidemic model where every agent can choose its activity level that affects the spread of the disease. Then by minimizing agents' cost functions, we solve for the optimal decisions for individual agents in the framework of game theory and multi-agent reinforcement learning. Given the optimal decisions of all agents, we can make predictions about the spread of the disease. We show that…

高效估算多层级多组件干预措施的局部治疗效应以应对阿片类药物危机

Computationally Efficient Estimation of Localized Treatment Effects for Multi-Level, Multi-Component Interventions to Address the Opioid Crisis

本文提出一种双层元模型框架，通过两阶段序列设计高效采样，用于估算多层级干预措施的局部治疗效应，减少计算资源消耗，应用于估算巴比妥酸盐分发和纳洛酮分布对过量死亡率的影响。

基于强化学习的传染病干预措施优化——基于英国COVID-19疫情数据的实证分析

Optimization of Infectious Disease Intervention Measures Based on Reinforcement Learning -- Empirical analysis based on UK COVID-19 epidemic data

本文基于改进的个体智能体传播模型Covasim构建强化学习决策框架，通过多算法与多动作空间实验及“时间覆盖”理论分析，在英国COVID-19数据上验证了该方法能有效抑制疫情并维护经济稳定。

疫情防控政策制定中的临界流动性

Critical mobility in policy making for epidemic containment

该研究构建流动性与接触耦合的智能体模型，发现诱发重大疫情的临界流动性，并据此提出可抑制发病高峰与反复波次的缓解框架。