Designing and Modeling of Crowdsourcing for Optimizing the Public Healthcare Informatics System in Society 5.0

Abstract

Technology, communications, and social media have changed emergency and disaster response networks. These developments enable affected citizens to generate georeferenced real-time data on important events, fueling this new landscape. Detecting and investigating such events requires crowdsourcing and machine learning. Crowdsourcing generates, aggregates, and filters data, while automatic tools analyze publicly available data using information retrieval techniques. Crowdsourcing encourages and coordinates large-scale participation in many fields. Crowdsourcing useful data and human computation interchangeable knowledge will help public health informatics soon. These efforts will lower any nation's disease burden and healthcare costs. It advances sustainable development goals and milestones. This chapter proposes crowd-sourcing modeling to improve public health surveillance for communicable and non-communicable diseases. These efforts will lower any nation's disease burden and also improve sustainable development goals.