A scalable COVID-19 screening platform

The COVID-19 pandemic has put a strain on health facilities world-wide. However, remote screening can lessen the burden on medical resources. If done manually, screening does not scale to the large number of people that require it. We are constructing an automated remote screening system for Romania. The system needs to support simultaneous use by many persons (presumably a significant part of the population). Considering the urgency, we propose a lightweight web-based platform that can run on low-end server infrastructure. One auxiliary goal of the platform is for it to use machine learning (ML) to be able to adapt its screening results (recommendations) to the evolution of pandemic. We considered using cloud services, however, due to privacy considerations regarding medical data and legal issues we designed the platform to be able to run, but to not require cloud services. Some of the decisions that we made were to offload computation (ML model inference) to the browser, minimize the number of requests to the server, as well as the code size, and construct a server architecture that uses buffers for writing to the database and which can scale on demand.