Spatiotemporal&amp;nbsp;Prediction of COVID-19 Mortality&amp;nbsp;and Risk Assessment

Torres-Signes, Antoni; Frías, María Pilar; Ruiz-Medina, M. D.

doi:10.21203/rs.3.rs-56955/v1

Cited by 2 publications

(2 citation statements)

References 44 publications

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“…For example, Giuliani et al (2020) have used GLM to predict COVID-19 infections in regions of Italy, and found the spatial interactions of nearby places to have a high influence on modeling; this shows the importance of accounting for the spatial effects explicitly. In a parallel vein, Bayesian modeling methods have also been used in this epidemiological context (Aswi et al 2019;Song et al 2019;Torres-Signes et al 2020;Gelman et al 2013).…”

Section: Introductionmentioning

confidence: 99%

A Bayesian machine learning approach for spatio-temporal prediction of COVID-19 cases

Niraula

Mateu

Chaudhuri

2022

Stoch Environ Res Risk Assess

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Modeling the spread of infectious diseases in space and time needs to take care of complex dependencies and uncertainties. Machine learning methods, and neural networks, in particular, are useful in modeling this sort of complex problems, although they generally lack of probabilistic interpretations. We propose a neural network method embedded in a Bayesian framework for modeling and predicting the number of cases of infectious diseases in areal units. A key feature is that our combined model considers the impact of human movement on the spread of the infectious disease, as an additional random factor to the also considered spatial neighborhood and temporal correlation components. Our model is evaluated over a COVID-19 dataset for 245 health zones of Castilla-Leon (Spain). The results show that a Bayesian model informed by a neural network method is generally able to predict the number of cases of COVID-19 in both space and time, with the human mobility factor having a strong influence on the model, together with the number of infections and deaths in nearby areas.

show abstract

Section: Introductionmentioning

confidence: 99%

A Bayesian machine learning approach for spatio-temporal prediction of COVID-19 cases

Niraula

Mateu

Chaudhuri

2022

Stoch Environ Res Risk Assess

View full text Add to dashboard Cite

show abstract

“…For example, Giuliani et al (2020) have used GLM to predict COVID-19 infections in regions of Italy, and found the spatial interactions of nearby places to have a high influence on modeling; this shows the importance of accounting for the spatial effects explicitly. In a parallel vein, Bayesian modeling methods have also been used in this epidemiological context (Gelman et al, 2013;Aswi et al, 2019;Song et al, 2019;Torres-Signes et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

A Bayesian Machine Learning Approach for Spatio-Temporal Prediction of COVID-19 Cases

Niraula

Mateu

Chaudhuri

2021

Preprint

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Modeling the behavior and spread of infectious diseases on space and time is key in devising public policies for preventive measures. This behavior is so complex that there are lots of uncertainties in both the data and in the process itself. We argue here that these uncertainties should be taken into account in the modeling strategy. Machine learning methods, and neural networks, in particular, are useful in modeling this sort of complex problems, although they generally lack of probabilistic interpretations. We thus present here a neural network method embedded in a Bayesian framework for modeling and predicting the number of cases of infectious diseases in areal units. A key feature is that our combined model considers the impact of human movement on the spread of the infectious disease, as an additional random factor to the also considered spatial neighborhood and temporal correlation components. Our model is evaluated over a COVID-19 dataset for 245 health zones of Castilla-Leon (Spain). The results show that a Bayesian model informed by a neural network method is generally able to predict the number of cases of COVID-19 in both space and time, with the human mobility factor having a strong influence on the model.

show abstract

Spatiotemporal Prediction of COVID-19 Mortality and Risk Assessment

Cited by 2 publications

References 44 publications

A Bayesian machine learning approach for spatio-temporal prediction of COVID-19 cases

A Bayesian machine learning approach for spatio-temporal prediction of COVID-19 cases

A Bayesian Machine Learning Approach for Spatio-Temporal Prediction of COVID-19 Cases

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