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Smart resilience through IoT‐enabled natural disaster management: A COVID‐19 response in São Paulo state

Authors :
Alessandro S. Santos
Icaro Goncales
Angelina Silva
Rodrigo Neves
Igor Teixeira
Eder Barbosa
Vagner Gava
Olga Yoshida
Source :
IET Smart Cities, Vol 6, Iss 3, Pp 211-224 (2024)
Publication Year :
2024
Publisher :
Wiley, 2024.

Abstract

Abstract Natural disaster management approach establishes stages of prevention, preparation, response, and recovery. With the Internet of Things (IoT), Bigdata, Business Intelligence, and other Information Communication Technologies, data can be gathered to support decisions in stages of the response to natural disaster events. In biological natural disasters, the ICTs can also support efforts to promote social distancing, public health, and economic monitoring to face the threads. São Paulo state used IoT in scenarios to face COVID‐19, such as monitoring vehicular interurban mobility, social distancing, and economic activity. Frameworks, strategies, data views, and use cases are presented to support the decision‐making process to face this biological natural disaster. The data‐driven approach supports several purposes, including the communication of social distancing indices, economic recovery, the progression of contagion, and deaths. It also played a pivotal role in fostering transparency initiatives for society and supporting the crisis committee by facilitating situational analyses, and this approach became standard practice for pandemic response. Studies and innovative visualisation perspectives have produced positive outcomes, guiding the decision‐making process through data analysis. Noteworthy use cases were interurban traffic fence monitoring; mapping of virus spreading; tracking the economic impact concerning recovery plans; and, evaluating the effectiveness of public policies.

Details

Language :
English
ISSN :
26317680
Volume :
6
Issue :
3
Database :
Directory of Open Access Journals
Journal :
IET Smart Cities
Publication Type :
Academic Journal
Accession number :
edsdoj.f2597826f45d4a85b144bafe3e4a81b6
Document Type :
article
Full Text :
https://doi.org/10.1049/smc2.12082