Géodes: the health indicators web portal of the French Public Health Agency

Lucas, E.; Guillet, A; Bonaldi, Christophe; Caserio-Schönemann, Céline; Strat, Yann Le

doi:10.1093/eurpub/ckz186.643

Cited by 2 publications

(1 citation statement)

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“…Concerning IRIS data, exact rates are not displayed in order to avoid identification of people tested, especially those who test positive. A screenshot of the Géodes tool 18 , created by Santé publique France, with regards to IRIS areas, is shown in Figure 1. The positivity rate corresponds to the number of positive tests compared to the number of tests carried out.…”

Section: Data Covid-19 Positivity and Hospitalization Datasetsmentioning

confidence: 99%

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

Skianis¹,

Nikolentzos

Gallix

et al. 2022

Preprint

View full text Add to dashboard Cite

The pandemic of COVID-19 is undoubtedly one of the biggest challenges for modern healthcare. From the beginning of the outbreak, new variants have also proven to be even more contagious, accelerating the spread and stressing the healthcare system. In order to analyze the spatio-temporal aspects of the spread of COVID-19, technology has helped us to track, identify and store information regarding positivity and hospitalization, across different levels of municipal entities. In this work, we present a method for predicting positive and hospitalized cases at the department level of France via a novel multi-scale graph neural network, integrating information from fine-scale geographical zones of a few thousand inhabitants. Our model manages to outperform baselines and deep learning models, presenting low errors in both prediction tasks. We specifically point out the importance of our contribution in predicting hospitalization since hospitals became critical infrastructure during the pandemic. To the best of our knowledge, this is the first work to exploit high-resolution spatio-temporal data in a multi-scale manner, incorporating additional knowledge, such as vaccination rates and population mobility data. We believe that our method may improve future estimations of positivity and hospitalization, which is crucial for healthcare planning.

show abstract

Section: Data Covid-19 Positivity and Hospitalization Datasetsmentioning

confidence: 99%

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

Skianis¹,

Nikolentzos

Gallix

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

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

Skianis¹,

Nikolentzos

Gallix

et al. 2023

Sci Rep

View full text Add to dashboard Cite

The pandemic of COVID-19 is undoubtedly one of the biggest challenges for modern healthcare. In order to analyze the spatio-temporal aspects of the spread of COVID-19, technology has helped us to track, identify and store information regarding positivity and hospitalization, across different levels of municipal entities. In this work, we present a method for predicting the number of positive and hospitalized cases via a novel multi-scale graph neural network, integrating information from fine-scale geographical zones of a few thousand inhabitants. By leveraging population mobility data and other features, the model utilizes message passing to model interaction between areas. Our proposed model manages to outperform baselines and deep learning models, presenting low errors in both prediction tasks. We specifically point out the importance of our contribution in predicting hospitalization since hospitals became critical infrastructure during the pandemic. To the best of our knowledge, this is the first work to exploit high-resolution spatio-temporal data in a multi-scale manner, incorporating additional knowledge, such as vaccination rates and population mobility data. We believe that our method may improve future estimations of positivity and hospitalization, which is crucial for healthcare planning.

show abstract

Géodes: the health indicators web portal of the French Public Health Agency

Cited by 2 publications

References 0 publications

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

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

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

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