Department of Surgical and Medical Sciences, Magna Graecia University, Catanzaro, 88110, Italy.
Bioinformatics unit, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, 71013, Italy.
Brief Bioinform. 2022 Jul 18;23(4). doi: 10.1093/bib/bbac230.
The control of the diffusion of diseases is a critical subject of a broad research area, which involves both clinical and political aspects. It makes wide use of computational tools, such as ordinary differential equations, stochastic simulation frameworks and graph theory, and interaction data, from molecular to social granularity levels, to model the ways diseases arise and spread. The coronavirus disease 2019 (COVID-19) is a perfect testbench example to show how these models may help avoid severe lockdown by suggesting, for instance, the best strategies of vaccine prioritization.
Here, we focus on and discuss some graph-based epidemiological models and show how their use may significantly improve the disease spreading control. We offer some examples related to the recent COVID-19 pandemic and discuss how to generalize them to other diseases.
疾病传播的控制是一个广泛研究领域的关键课题，涉及临床和政治方面。它广泛利用计算工具，如常微分方程、随机模拟框架和图论，以及从分子到社会粒度级别的相互作用数据，来建模疾病的发生和传播方式。2019 年冠状病毒病 (COVID-19) 是一个完美的测试案例，表明这些模型如何通过例如建议疫苗优先排序的最佳策略，帮助避免严重的封锁。
在这里，我们专注于并讨论一些基于图的流行病学模型，并展示它们的使用如何显著改善疾病传播控制。我们提供了一些与最近的 COVID-19 大流行相关的示例，并讨论了如何将其推广到其他疾病。
