The current COVID-19 global pandemic caused by the SARS-CoV-2 betacoronavirus has resulted in over a million deaths and is having a grave socio-economic impact, hence there is an urgency to find solutions to key research challenges. Some important areas of focus are: developing a vaccine, designing or re-purposing existing pharmacological agents for treatment by identifying druggable targets, predicting and diagnosing the disease e.g. clinical decision support, and tracking and reducing the spread. Much of this COVID-19 research is dependent on computation, particularly distributed computing -- a model in which software and computational resources are distributed amongst networked computers and used collectively to solve complex, computationally demanding problems, and to process bigdata. In this article, I review distributed computing technologies -- various types of clusters, grids and clouds -- that can be leveraged to perform these tasks at scale, at high-throughput, with a high degree of parallelism, and which can also be used to work collaboratively. For each architecture, I provide a technical introduction and, where they exist, discuss COVID-19 focused projects that have utilised the technology, as well as projects that can be employed for this important research.