Collective Intelligence for Preventing Pandemic Crises: A Model-Centralized Organizational Framework

Abstract

Pandemic propagation, a highly nonlinear and complicated process, is difficult to understand, predict, and prevent in reality. The explosive growth of mass data and intelligent technologies poses new insights for solving this challenge. From a systematic perspective, this article proposes an organizational framework for pandemic crisis control. As a result, a model as a core component serves as a pandemic simulation and analog control. The collective data are sourced from realistic dynamics and feed the model after parameterization processing. Some advanced intelligent technologies are adopted to optimize simulation results and assist policymaking. To enhance the applicability of the framework, four typical routes and three levels of examples are provided. The routes contain diverse fields such as computer science, epidemiology, biomedicine, and social science. The examples are in relation to the few, regular, and rich levels of data information. Finally, this article paves the way for intelligent pandemic crises prevention and yields a fresh application paradigm in the field of collective intelligence (CI).