Using Self Organising Maps to Predict and Contain Natural Disasters and Pandemics

Moodley, Raymond
Chiclana, Francisco
Caraffini, Fabio
Gongora, Mario Augusto
Journal Title
Journal ISSN
Volume Title
Peer reviewed
The unfolding COVID-19 pandemic has highlighted the global need for robust predictive and containment tools and strategies. COVID-19 continues to cause widespread economic and social turmoil, and whilst the current focus is on both minimising the spread of the disease and deploying a range of vaccines to save lives, attention will soon turn to future proofing. In line with this, this paper proposes a prediction and containment model that could be used for pandemics and natural disasters. It combines selective lockdowns and protective cordons to rapidly contain the hazard whilst allowing minimally impacted local communities to conduct "business as usual" and/or offer support to highly impacted areas. A flexible, easy to use data analytics model, based on Self Organising Maps, is developed to facilitate easy decision making by governments and organisations. Comparative tests using publicly available data for Great Britain (GB) show that through the use of the proposed prediction and containment strategy, it is possible to reduce the peak infection rate, whilst keeping several regions (up to 25% of GB parliamentary constituencies) economically active within protective cordons.
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
Self Organising Maps, Predicting Pandemics, Protective Cordons, Pandemic and Natural Disaster Containment, Epidemiology
Moodley, R., Chiclana, F., Caraffini, F., Gongora, M. (2021) Using Self - Organising Maps to Predict and Contain Natural Disasters and Pandemics. International Journal of Intelligent Systems, pp.1-19.
Research Institute
Institute of Artificial Intelligence (IAI)