Sci Rep March 2023 PMID 37002271

Predicting COVID-19 positivity and hospitalization with multi-scale graph neural networks.

Konstantinos Skianis, Giannis Nikolentzos, Benoit Gallix, Rodolphe Thiebaut, Georgios Exarchakis

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Mentions

Peak: 2 in Jul 2022

across 2 sources since Jul 2022

Extended reach

715

potential audience

Total likes

Video views

Mentions per month

Jul 2022 – Jul 2023

Where it's discussed

X / Twitter3 posts
YouTube1 videos

AI synopsis

What people are saying

The posts show general acceptance of the study's significance, with phrases like 'predicting COVID-19 positivity' and mentions of 'multi-scale graph neural networks,' indicating technical interest. The tone is neutral to slightly positive, emphasizing the potential impact on healthcare planning without extensive critique or emotional language. This suggests a moderate level of engagement and perceived importance within the community of followers.

AgreementModerate agreement

Most discussions acknowledge the relevance of the publication, though some show cautious optimism rather than full endorsement.

InterestModerate interest

Participants demonstrate moderate curiosity, mainly referencing the innovative modeling approach rather than engaging in deep critique.

EngagementModerate engagement

Responses include retweets and brief comments indicating some level of reflection but lack in-depth analysis or critique.

ImpactModerate impact

The discussions suggest perceived importance in predictive modeling for health outcomes, but without extensive debate on broader implications.

Abstract in brief

This study introduces a new multiscale graph neural network that predicts COVID-19 positivity and hospitalization rates by analyzing detailed geographical data and incorporating factors like population mobility and vaccination rates.
The model uses message passing techniques to capture interactions between small geographical zones, leading to more accurate predictions that outperform existing methods.
This approach is particularly important for hospital planning during pandemics, as it provides high-resolution insights into how the virus spreads and affects healthcare systems.

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All mentions

Predicting COVID-19 Positivity and Hospitalization with Multi-Scale Graph Neural Networks

This study introduces a new multiscale graph neural network that predicts COVID-19 positivity and hospitalization rates by analyzing detailed geographical data and incorporating factors like population mobility and…

Medicine RTCL TV · Jul 25, 2023 6 views

Kuro_Unplugged

@kuro_unplugged · X

Predicting COVID-19 positivity and hospitalization with multi-scale graph neural networks https://t.co/kK9SwAdVrV

Mar 31, 2023 · 23 followers View post →

Georgios Exarchakis

@gexarcha · X

RT @RS_COVID19: Predicting COVID-19 positivity and hospitalization with multi-scale graph neural networks #covid https://t.co/qbnXrB0l7m

Jul 15, 2022 · 1 share · 42 followers View post →

Research Square Covid-19 Preprints

@RS_COVID19 · X

Predicting COVID-19 positivity and hospitalization with multi-scale graph neural networks #covid https://t.co/qbnXrB0l7m

Jul 14, 2022 · 1 share · 640 followers View post →