AI-Empowered Data Analytics for Coronavirus Epidemic Monitoring and Control
- 23 June 2020
- book chapter
- other
- Published by Springer Science and Business Media LLC
Abstract
Governments and authorities knew little about the virus since the emergency of COVID-19 outbreak. The Chinese government upon the discovery of the early patients in Wuhan, informed WHO on 31 December 2019, as pneumonia of unknown causes. Epidemiologists, data scientists and biostatisticians have been working hand-in-hand for a common mission of trying to characterize and understand the characteristics of the infection as well as the virus itself, which is SARS alike.Keywords
This publication has 12 references indexed in Scilit:
- High Contagiousness and Rapid Spread of Severe Acute Respiratory Syndrome Coronavirus 2Emerging Infectious Diseases, 2020
- Towards an Artificial Intelligence Framework for Data-Driven Prediction of Coronavirus Clinical SeverityComputers, Materials & Continua, 2020
- Finding an Accurate Early Forecasting Model from Small Dataset: A Case of 2019-nCoV Novel Coronavirus OutbreakInternational Journal of Interactive Multimedia and Artificial Intelligence, 2020
- A review of influenza detection and prediction through social networking sitesTheoretical Biology and Medical Modelling, 2018
- AmPEP: Sequence-based prediction of antimicrobial peptides using distribution patterns of amino acid properties and random forestScientific Reports, 2018
- Feature Selection in Life Science Classification: Metaheuristic Swarm SearchIT Professional, 2014
- Examining Text Categorization Methods for Incidents AnalysisLecture Notes in Computer Science, 2012
- Neuro-Linguistic ProgrammingPublished by Springer Science and Business Media LLC ,2009
- Improving Information Retrieval Effectiveness by Using Domain Knowledge Stored in OntologiesLecture Notes in Computer Science, 2005
- Seasonality and period-doubling bifurcations in an epidemic modelJournal of Theoretical Biology, 1984