The coronavirus disease 2019 (COVID-19) pandemic has been ongoing for around
3 years, and has infected over 750 million people and caused over 6 million
deaths worldwide at the time of writing. Throughout the pandemic, several
strategies for controlling the spread of the disease have been debated by
healthcare professionals, government authorities, and international bodies. To
anticipate the potential impact of the disease, and to simulate the
effectiveness of different mitigation strategies, a robust model of disease
spread is needed. In this work, we explore a novel approach based on
probabilistic planning and dynamic graph analysis to model the spread of
COVID-19 in indoor spaces. We endow the planner with means to control the
spread of the disease through non-pharmaceutical interventions (NPIs) such as
mandating masks and vaccines, and we compare the impact of crowds and capacity
limits on the spread of COVID-19 in these settings. We demonstrate that the use
of probabilistic planning is effective in predicting the amount of infections
that are likely to occur in shared spaces, and that automated planners have the
potential to design competent interventions to limit the spread of the disease.
Our code is fully open-source and is available at:
this https URL .

新冠病毒在室内空间传播的概率规划和动态图分析模型，探讨了非药物干预措施对疾病传播的影响和预测。

使用自动概率规划建模室内空间中 COVID-19 的传播

Modelling the Spread of COVID-19 in Indoor Spaces using Automated  Probabilistic Planning

In recent years, there has been an increasing interest in the use of graph
neural networks (GNNs) for analyzing dynamic graphs, which are graphs that
evolve over time. However, there is still a lack of understanding of how
different temporal graph neural network (TGNs) configurations can impact the
accuracy of predictions on dynamic graphs. Moreover, the hunt for benchmark
datasets for these TGNs models is still ongoing. Up until recently, Pytorch
Geometric Temporal came up with a few benchmark datasets but most of these
datasets have not been analyzed with different TGN models to establish the
state-of-the-art. Therefore, this project aims to address this gap in the
literature by performing a qualitative analysis of spatial-temporal dependence
structure learning on dynamic graphs, as well as a comparative study of the
effectiveness of selected TGNs on node and edge prediction tasks. Additionally,
an extensive ablation study will be conducted on different variants of the
best-performing TGN to identify the key factors contributing to its
performance. By achieving these objectives, this project will provide valuable
insights into the design and optimization of TGNs for dynamic graph analysis,
with potential applications in areas such as disease spread prediction, social
network analysis, traffic prediction, and more. Moreover, an attempt is made to
convert snapshot-based data to the event-based dataset and make it compatible
with the SOTA model namely TGN to perform node regression task.

本文通过定性分析时空依赖结构学习以及比较研究所选的 TGN 模型对节点和边缘预测任务的有效性，并进行了对最佳表现 TGN 模型不同变体的广泛消融研究，以提供有价值的关于动态图形分析设计和优化的洞察，同时将快照数据转换为基于事件的数据集并与目前最先进的模型进行了兼容，以实现节点回归任务。