A joint Bayesian spatiotemporal risk prediction model of COVID-19 incidence, IC admission, and death with application to Sweden

Jaya, I Gede Nyoman Mindra; Folmer, Henk; Lundberg, Johan

doi:10.1007/s00168-022-01191-1

Cited by 6 publications

(5 citation statements)

References 103 publications

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“…Another study [ 83 ]) analyzed the spread of COVID-19 in the most affected Brazilian cities using hybrid and single ARIMA models, which integrated EEMD and ARIMA techniques. The results showed that the EEMD performed approximately 27% better than the single model [ 33 , 47 – 49 , 53 – 55 , 71 , 74 , 106 , 110 – 112 , 134 – 143 ].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Notably, the GRU comprises two gates: the Update Gate Z and the Reset Gate R, in contrast to the LSTM's three gates. These gates are instrumental in modulating the flow of information through time, thereby alleviating the detrimental effects of vanishing and exploding gradients [11,[106][107][108][109]. The architecture of the GRU cell, with its two gates and simplified computational structure, offers an elegant solution for capturing both short-and long-term dependencies in sequence data.…”

Section: Literature Reviewmentioning

confidence: 99%

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Deep learning in public health: Comparative predictive models for COVID-19 case forecasting

Tariq,

Ismail

2024

PLoS ONE

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The COVID-19 pandemic has had a significant impact on both the United Arab Emirates (UAE) and Malaysia, emphasizing the importance of developing accurate and reliable forecasting mechanisms to guide public health responses and policies. In this study, we compared several cutting-edge deep learning models, including Long Short-Term Memory (LSTM), bidirectional LSTM, Convolutional Neural Networks (CNN), hybrid CNN-LSTM, Multilayer Perceptron’s, and Recurrent Neural Networks (RNN), to project COVID-19 cases in the aforementioned regions. These models were calibrated and evaluated using a comprehensive dataset that includes confirmed case counts, demographic data, and relevant socioeconomic factors. To enhance the performance of these models, Bayesian optimization techniques were employed. Subsequently, the models were re-evaluated to compare their effectiveness. Analytic approaches, both predictive and retrospective in nature, were used to interpret the data. Our primary objective was to determine the most effective model for predicting COVID-19 cases in the United Arab Emirates (UAE) and Malaysia. The findings indicate that the selected deep learning algorithms were proficient in forecasting COVID-19 cases, although their efficacy varied across different models. After a thorough evaluation, the model architectures most suitable for the specific conditions in the UAE and Malaysia were identified. Our study contributes significantly to the ongoing efforts to combat the COVID-19 pandemic, providing crucial insights into the application of sophisticated deep learning algorithms for the precise and timely forecasting of COVID-19 cases. These insights hold substantial value for shaping public health strategies, enabling authorities to develop targeted and evidence-based interventions to manage the virus spread and its impact on the populations of the UAE and Malaysia. The study confirms the usefulness of deep learning methodologies in efficiently processing complex datasets and generating reliable projections, a skill of great importance in healthcare and professional settings.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Deep learning in public health: Comparative predictive models for COVID-19 case forecasting

Tariq,

Ismail

2024

PLoS ONE

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“…Bayesian spatiotemporal models have been widely utilized for COVID-19 analysis and forecasting globally, including in the United States [100][101][102], England [103,104], Spain, Italy, Germany, Sweden [87,[105][106][107], Africa [108], and in some regional areas such as the West Java Province, Indonesia [109], and the Greater Seoul Area, Korea [110]. As an illustration, Nazia et al [111] applied a Bayesian hierarchical spatiotemporal model to assess the COVID-19 risk.…”

Section: Covid-19mentioning

confidence: 99%

A Review of Bayesian Spatiotemporal Models in Spatial Epidemiology

Wang,

Chen,

Xue

2024

IJGI

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Spatial epidemiology investigates the patterns and determinants of health outcomes over both space and time. Within this field, Bayesian spatiotemporal models have gained popularity due to their capacity to incorporate spatial and temporal dependencies, uncertainties, and intricate interactions. However, the complexity of modelling and computations associated with Bayesian spatiotemporal models vary across different diseases. Presently, there is a limited comprehensive overview of Bayesian spatiotemporal models and their applications in epidemiology. This article aims to address this gap through a thorough review. The review commences by delving into the historical development of Bayesian spatiotemporal models concerning disease mapping, prediction, and regression analysis. Subsequently, the article compares these models in terms of spatiotemporal data distribution, general spatiotemporal data models, environmental covariates, parameter estimation methods, and model fitting standards. Following this, essential preparatory processes are outlined, encompassing data acquisition, data preprocessing, and available statistical software. The article further categorizes and summarizes the application of Bayesian spatiotemporal models in spatial epidemiology. Lastly, a critical examination of the advantages and disadvantages of these models, along with considerations for their application, is provided. This comprehensive review aims to enhance comprehension of the dynamic spatiotemporal distribution and prediction of epidemics. By facilitating effective disease scrutiny, especially in the context of the global COVID-19 pandemic, the review holds significant academic merit and practical value. It also aims to contribute to the development of improved ecological and epidemiological prevention and control strategies.

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“…, T; n represents the number of nonoverlapping areas; T is the total time points. Let us first assume the number of reported cases y dit follows a Poisson distribution with a mean of E(y dit ) = λ dit and a corresponding variance of Var(y dit ) = λ dit [47]:…”

Section: Generalized Additive Mixed Model Via Log Linear Modelmentioning

confidence: 99%

“…However, this measurement is not reliable, particularly in situations with high population variability and small numbers of disease cases, due to the presence of significant sampling errors. The SIR is calculated by dividing the number of reported cases, y dit , by the expected number of disease cases, E dit [47,49]:…”

Section: Generalized Additive Mixed Model Via Log Linear Modelmentioning

confidence: 99%

Multivariate Bayesian Semiparametric Regression Model for Forecasting and Mapping HIV and TB Risks in West Java, Indonesia

Jaya,

Handoko,

Andriyana

et al. 2023

Mathematics

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Multivariate “Bayesian” regression via a shared component model has gained popularity in recent years, particularly in modeling and mapping the risks associated with multiple diseases. This method integrates joint outcomes, fixed effects of covariates, and random effects involving spatial and temporal components and their interactions. A shared spatial–temporal component considers correlations between the joint outcomes. Notably, due to spatial–temporal variations, certain covariates may exhibit nonlinear effects, necessitating the use of semiparametric regression models. Sometimes, choropleth maps based on regional data that is aggregated by administrative regions do not adequately depict infectious disease transmission. To counteract this, we combine the area-to-point geostatistical model with inverse distance weighted (IDW) interpolation for high-resolution mapping based on areal data. Additionally, to develop an effective and efficient early warning system for controlling disease transmission, it is crucial to forecast disease risk for a future time. Our study focuses on developing a novel multivariate Bayesian semiparametric regression model for forecasting and mapping HIV and TB risk in West Java, Indonesia, at fine-scale resolution. This novel approach combines multivariate Bayesian semiparametric regression with geostatistical interpolation, utilizing population density and the Human Development Index (HDI) as risk factors. According to an examination of annual data from 2017 to 2021, HIV and TB consistently exhibit recognizable spatial patterns, validating the suitability of multivariate modeling. The multivariate Bayesian semiparametric model indicates significant linear effects of higher population density on elevating HIV and TB risks, whereas the impact of the HDI varies over time and space. Mapping of HIV and TB risks in 2022 using isopleth maps shows a clear HIV and TB transmission pattern in West Java, Indonesia.

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A joint Bayesian spatiotemporal risk prediction model of COVID-19 incidence, IC admission, and death with application to Sweden

Cited by 6 publications

References 103 publications

Deep learning in public health: Comparative predictive models for COVID-19 case forecasting

Deep learning in public health: Comparative predictive models for COVID-19 case forecasting

A Review of Bayesian Spatiotemporal Models in Spatial Epidemiology

Multivariate Bayesian Semiparametric Regression Model for Forecasting and Mapping HIV and TB Risks in West Java, Indonesia

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