Towards a Framework for High-Performance Simulation of Livestock Disease Outbreak: A Case Study of Spread of African Swine Fever in Vietnam

Abstract

Simple Summary Disease transmission simulation programs in veterinary epidemiology in general and in simulation of African swine fever in particular are often very diverse and require great computing power. However, such programs often share similar workflows from processing input/output data, performing simulations, or storing data. Our paper proposes a common architectural framework for livestock disease transmission simulation programs in order to both improve simulation performance and reduce the effort of developing new simulation programs. Our framework was evaluated with a simulation program of African swine fever transmission currently raging in Vietnam and some other countries around the world. The results from the evaluation experiments not only demonstrate the effectiveness of the framework in terms of performance but also have practical consulting value for decision makers in Vietnam and for international colleagues. Abstract 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.