Hellenic Open University, Patras, Greece.
School of Pedagogical & Technological Education, Athens, Greece.
Adv Exp Med Biol. 2023;1424:175-185. doi: 10.1007/978-3-031-31982-2_19.
The One Health framework, which advocates the crucial interconnection between environmental, animal, and human health and well-being, is becoming of increasing importance and acceptance in health sciences over the last years. The hottest public health topics of the latest years, like zoonotic diseases (e.g., the recent pandemic) or the increasing antibiotic resistance, characterized by many as "pandemic of the future," make the more holistic and combinatorial approach of One Health a necessity to combat such complex problems. Multiple graphs and graph theory have found applications in health sciences for many years, and they can now extend to usage across all levels of a One Health approach to health, ranging from genome, one disease level, to epidemiology and ecosystem graphs. For that last ecosystem layer, a proposed approach is the utilization of process graphs from the chemical engineering field, in order to understand a whole system and what constitute the most crucial aspects of a One Health issue in ecosystem level. Here P-graphs are focused alongside their combinatorial algorithms, implemented in R, and their application researched in an effort to extract information and plan interventions.
近年来，倡导环境、动物和人类健康与福祉之间至关重要的相互联系的“同一健康”框架在健康科学领域变得越来越重要并被广泛接受。近年来最热门的公共卫生话题，如人畜共患疾病（例如最近的大流行）或抗生素耐药性的增加，被许多人称为“未来的大流行”，这使得“同一健康”更全面和综合的方法成为解决此类复杂问题的必要手段。多年来，多种图形和图论已在健康科学中得到应用，现在它们可以扩展到“同一健康”方法在健康的各个层面上的使用，从基因组、一种疾病水平到流行病学和生态系统图。对于最后一个生态系统层，提出的方法是利用化学工程领域的过程图，以便了解整个系统以及构成生态系统层面同一健康问题的最关键方面。本文重点介绍了 P 图及其组合算法，这些算法是在 R 中实现的，并研究了它们在提取信息和规划干预措施方面的应用。
