Pham Linh Manh, Parlavantzas Nikos, Le Huy-Ham, Bui Quang Hung
University of Engineering and Technology, Vietnam National University, 144 Xuan Thuy, Cau Giay, Hanoi 10000, Vietnam.
Campus Universitaire de Beaulieu, Université de Rennes, Inria, CNRS, IRISA, 35042 Rennes, France.
Animals (Basel). 2021 Sep 19;11(9):2743. doi: 10.3390/ani11092743.
The spread of disease in livestock is an important research topic of veterinary epidemiology because it provides warnings or advice to organizations responsible for the protection of animal health in particular and public health in general. Disease transmission simulation programs are often deployed with different species, disease types, or epidemiological models, and each research team manages its own set of parameters relevant to their target diseases and concerns, resulting in limited cooperation and reuse of research results. Furthermore, these simulation and decision support tools often require a large amount of computational power, especially for models involving tens of thousands of herds with millions of individuals spread over a large geographical area such as a region or a country. It is a matter of fact that epidemic simulation programs are often heterogeneous, but they often share some common workflows including processing of input data and execution of simulation, as well as storage, analysis, and visualization of results. In this article, we propose a novel architectural framework for simultaneously deploying any epidemic simulation program both on premises and on the cloud to improve performance and scalability. We also conduct some experiments to evaluate the proposed architectural framework on some aspects when applying it to simulate the spread of African swine fever in Vietnam.
牲畜疾病的传播是兽医流行病学的一个重要研究课题，因为它能为负责动物健康保护（尤其是动物健康，总体上也包括公共健康）的组织提供预警或建议。疾病传播模拟程序通常针对不同物种、疾病类型或流行病学模型进行部署，每个研究团队管理与自身目标疾病及关注点相关的参数集，这导致研究结果的合作与复用受限。此外，这些模拟和决策支持工具通常需要大量计算能力，特别是对于涉及分布在如一个地区或一个国家等大面积地理区域内、拥有数百万个体的数万畜群的模型。事实上，疫情模拟程序往往是异构的，但它们通常共享一些常见工作流程，包括输入数据处理和模拟执行，以及结果的存储、分析和可视化。在本文中，我们提出了一种新颖的架构框架，用于同时在本地和云端部署任何疫情模拟程序，以提高性能和可扩展性。我们还进行了一些实验，在将该架构框架应用于模拟越南非洲猪瘟传播时，从某些方面对其进行评估。
