Novel case-based reasoning system for public health emergencies

Abstract

Purpose: Several threatening infectious diseases, including influenza, Ebola, SARS, and COVID-19, have affected human society over the past decades. These disease outbreaks naturally inspire a demand for sustained and advanced safety and suppression measures. To protect public health and safety, further research developments on emergency analysis methods and approaches for effective emergency treatment generation are urgently needed to mitigate the severity of the pandemic and save lives. Methods: To address these issues, a novel case-based reasoning (CBR) system is proposed using three phases. In the first phase, the similarity between the current case and the historical cases is calculated under a variety of heterogeneous information. In the second phase, a filter approach based on grey clustering analysis is created to retrieve relevant cases. In the third phase, the cases retrieved are taken as initial host nests in a cuckoo search (CS) algorithm, and our system searches an optimal solution through iteration of this algorithm. Results: The proposed model is compared with a CBR method improved by particle swarm optimization (PSO) and a CBR method improved by a differential evolution algorithm (DE), to confirm the efficiency of our CS algorithm in adapting solutions for public health emergencies. The results show that the proposed model is better than the existing algorithms. Conclusion: The proposed model improves the speed of case retrieval using grey clustering and increases solution accuracy with CS algorithms. The present research can contribute to government, CDC, and infectious disease emergency management fields with regard to the implementation of fast and accurate public biohazard prevention and control measures based on a variety of heterogeneous information.