COVID-19 Prevention, which combines the soft approaches and best practices for public health safety, is the only recommended solution from the health science and management society side considering the pandemic era. In an attempt to evaluate the validity of such claims in a conflict and COVID-19-affected country like Afghanistan, we conducted a large-scale digital social experiment using conversational AI and social platforms from an info-epidemiology and an infoveillance perspective. This served as a means to uncover an underling truth, give large-scale facilitation support, extend the soft impact of discussion to multiple sites, collect, diverge, converge and evaluate a large amount of opinions and concerns from health experts, patients and local people, deliberate on the data collected and explore collective prevention approaches of COVID-19. Finally, this paper shows that deciding a prevention measure that maximizes the probability of finding the ground truth is intrinsically difficult without utilizing the support of an AI-enabled discussion systems.
@article{arxiv.2106.11023,
title = {A Contribution to COVID-19 Prevention through Crowd Collaboration using Conversational AI & Social Platforms},
author = {Jawad Haqbeen and Takayuki Ito and Sofia Sahab and Rafik Hadfi and Shun Okuhara and Nasim Saba and Murataza Hofaini and Umar Baregzai},
journal= {arXiv preprint arXiv:2106.11023},
year = {2021}
}
Comments
To appear as a workshop paper at AI4SG 2020. 6 pages, 4 figures, 1 table