Francesco SALVARANI, auteur/autrice sur Devinci Higher Education

ICTO2023 The 7th Conference for Information & Communication Technologies for Organization & Society ETHICAL & RESPONSIBLE ARTIFICIAL INTELLIGENCE FOR SUSTAINABLE SOCIETIES, Paris, France, 2023.

Abstract | Links | BibTeX

@conference{salvarani_2570,

title = {A decision aid model for new pandemic episods},

author = {Francesco Salvarani and Peter Saba},

url = {https://www.icto2023.com/_files/ugd/76fc40_31f9eca50eb54c809ece853b8f11e00d.pdf},

year  = {2023},

date = {2023-07-01},

booktitle = {ICTO2023 The 7th Conference for Information & Communication Technologies for Organization & Society ETHICAL & RESPONSIBLE ARTIFICIAL INTELLIGENCE FOR SUSTAINABLE SOCIETIES},

address = {Paris, France},

abstract = {The importance of achieving sustainable development and addressing environmental challenges has gained increasing recognition within the academic community. Scholars have emphasized the interconnected elements of economic wellbeing, social inclusion, and environmental protection (Baghdadi, et al. 2020; Johnson et al., 2021; Dennehy et al., 2021; Dwivedi et al., 2022). Ef- forts have been made to leverage technology and analytics to promote sustainable development and energy conservation (Albizri, 2020; Khene & Masiero, 2022; Masiero & Arvidsson, 2021). However, achieving inclusive and resilient futures for both people and the planet requires collab- oration among academic disciplines, policymakers, practitioners, and the intended beneficiaries of the Sustainable Development Goals (Harfouche et al., 2019; Harfouche et al., 2022).

For instance, even prior to the Covid-19 pandemic, the need to prepare for sudden outbreaks of new infectious diseases was recognized (Osterholm, 2005) . The recent pandemic has further highlighted the urgency of developing action plans to effectively counter future pandemics (Wen- ham et al., 2021). Understanding the behavior and characteristics of new contagious diseases is a gradual process, resulting in a lack of knowledge and potential underestimation of their long-term consequences. Therefore, early-stage preventive measures such as social distancing, mask usage (e.g., FFP2 masks), and frequent handwashing are crucial to mitigate the risks of contagion.

However, the social acceptability of these preventive measures often falls short of what is necessary (Reed et al., 2020). Health policymakers frequently face the dilemma of whether to enforce mandatory measures, carefully weighing the risks and benefits associated with each choice. To address this challenge, this article aims to study a quantitative model that utilizes real data to simulate and examine how the progression of a pandemic influences the spontaneous adherence to prevention measures. By providing quantitative predictions on the acceptability of these measures, this model can serve as a valuable tool to support policymakers in making informed and sustainable decisions.},

keywords = {},

pubstate = {online},

tppubtype = {conference}

}

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The importance of achieving sustainable development and addressing environmental challenges has gained increasing recognition within the academic community. Scholars have emphasized the interconnected elements of economic wellbeing, social inclusion, and environmental protection (Baghdadi, et al. 2020; Johnson et al., 2021; Dennehy et al., 2021; Dwivedi et al., 2022). Ef- forts have been made to leverage technology and analytics to promote sustainable development and energy conservation (Albizri, 2020; Khene & Masiero, 2022; Masiero & Arvidsson, 2021). However, achieving inclusive and resilient futures for both people and the planet requires collab- oration among academic disciplines, policymakers, practitioners, and the intended beneficiaries of the Sustainable Development Goals (Harfouche et al., 2019; Harfouche et al., 2022).
For instance, even prior to the Covid-19 pandemic, the need to prepare for sudden outbreaks of new infectious diseases was recognized (Osterholm, 2005) . The recent pandemic has further highlighted the urgency of developing action plans to effectively counter future pandemics (Wen- ham et al., 2021). Understanding the behavior and characteristics of new contagious diseases is a gradual process, resulting in a lack of knowledge and potential underestimation of their long-term consequences. Therefore, early-stage preventive measures such as social distancing, mask usage (e.g., FFP2 masks), and frequent handwashing are crucial to mitigate the risks of contagion.
However, the social acceptability of these preventive measures often falls short of what is necessary (Reed et al., 2020). Health policymakers frequently face the dilemma of whether to enforce mandatory measures, carefully weighing the risks and benefits associated with each choice. To address this challenge, this article aims to study a quantitative model that utilizes real data to simulate and examine how the progression of a pandemic influences the spontaneous adherence to prevention measures. By providing quantitative predictions on the acceptability of these measures, this model can serve as a valuable tool to support policymakers in making informed and sustainable decisions.

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Francesco Salvarani

Kinetic models for complex systems Conference

Modelling, analysis and numerical methods of complex dynamics, Marseille, France, 2023.

Links | BibTeX