Título Optimisation of Non-Pharmaceutical Measures in COVID-19 Growth via Neural Networks
Autores Riccardi A. , Gemignani J. , FERNÁNDEZ NAVARRO, FRANCISCO DE ASÍS, Heffernan A.
Publicación externa No
Medio IEEE Trans. Emerging Topics Comp. Intell.
Alcance Article
Naturaleza Científica
Ámbito Internacional
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099732580&doi=10.1109%2fTETCI.2020.3046012&partnerID=40&md5=1ddee31514b641d3201d39c547c67b31
Fecha de publicacion 01/01/2021
Scopus Id 2-s2.0-85099732580
DOI 10.1109/TETCI.2020.3046012
Abstract On 19^{\\text{th}} March, the World Health Organisation declared a pandemic. Through this global spread, many nations have witnessed exponential growth of confirmed cases brought under control by severe mass quarantine or lockdown measures. However, some have, through a different timeline of actions, prevented this exponential growth. Currently as some continue to tackle growth, others attempt to safely lift restrictions whilst avoiding a resurgence. This study seeks to quantify the impact of government actions in mitigating viral transmission of SARS-CoV-2 by a novel soft computing approach that makes concurrent use of a neural network model, to predict the daily slope increase of cumulative infected, and an optimiser, with a parametrisation of the government restriction time series, to understand the best set of mitigating actions. Data for two territories, Italy and Taiwan, have been gathered to model government restrictions in travelling, testing and enforcement of social distance measures as well as people connectivity and adherence to government actions. It is found that a larger and earlier testing campaign with tighter entry restrictions benefit both regions, resulting in significantly less confirmed cases. Interestingly, this scenario couples with an earlier but milder implementation of nationwide restrictions for Italy, thus supporting Taiwan\'s lack of nationwide lockdown, i.e. earlier government actions could have contained the growth to a degree that a widespread lockdown would have been avoided, or at least delayed. The results, found with a purely data-driven approach, are in line with the main findings of mathematical epidemiological models, proving that the proposed approach has value and that the data alone contains valuable knowledge to inform decision makers. © 2017 IEEE.
Palabras clave Decision making; Diseases; Public policy; Soft computing; Well testing; Data-driven approach; Decision makers; Epidemiological models; Exponential growth; Government actions; Neural network model; Par
Miembros de la Universidad Loyola

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