Architectural Aspects of a Data-Intensive System: A Covid-19 Related Case Study

Abstract

The Covid-19 pandemic caused serious turbulences in most aspects of humans activities. Due to the need to address the epidemic developments at extreme scales, ranging from the entire population of the country down to the level of individual citizens, a construction of adequate mathematical models faces substantial difficulties caused by lacking knowledge of the mechanisms driving transmission of the infections and the very nature of the resulting disease. Therefore, in modeling Covid-19 and its effects, a shift from the knowledge-intensive systems paradigm to the data-intensive one is needed. The current paper is devoted to the architecture of ProME, a data-intensive system for forecasting the Covid-19 and decision making support needed to mitigate the pandemics effects. The system has been constructed to address the mentioned challenges and to allow further relatively easy adaptations to the dynamically changing situation. The system is mainly based on open-source solutions so can be reproduced whenever similar challenges occur.