Studying the dynamics of COVID-19 is of paramount importance to understanding the efficiency of restrictive measures and develop strategies to defend against upcoming contagion waves. In this work, we study the spread of COVID-19 using a semi-supervised neural network and assuming a passive part of the population remains isolated from the virus dynamics. We start with an unsupervised neural network that learns solutions of differential equations for different modeling parameters and initial conditions. A supervised method then solves the inverse problem by estimating the optimal conditions that generate functions to fit the data for those infected by, recovered from, and deceased due to COVID-19. This semi-supervised approach incorporates real data to determine the evolution of the spread, the passive population, and the basic reproduction number for different countries.
Critical data analysis of COVID-19 spreading in Indonesia to measure the readiness of new-normal policy
Institut Teknologi Sepuluh Nopember（十月科技学院（印度尼西亚））；Politeknik Caltex Riau（廖内加理工学院）；Politeknik Negeri Malang（玛琅国立理工学院）；Universitas Pembangunan Nasional Veteran Jawa Timur（东爪哇退伍军人国家发展大学）；STIE Perbanas（佩尔巴纳斯经济学院）
Analysing the impact of global demographic characteristics over the COVID-19 spread using class rule mining and pattern matching
Liverpool John Moores University（利物浦约翰摩尔大学）；DeepCamera Research Lab, Interactive Media, Smart System, and Emerging Technologies Center（DeepCamera研究实验室，互动媒体、智能系统与新兴技术中心）；The Regenerative Clinic, Queen Anne Medical Centre（再生诊所，安妮女王医疗中心）；Manchester Metropolitan University（曼彻斯特城市大学）；Khalifa University（哈利法大学）
