Spatio-temporal variation of Covid-19 health outcomes in India using deep learning based models

Middya, Asif Iqbal; Roy, Sarbani

doi:10.1016/j.techfore.2022.121911

Cited by 7 publications

(2 citation statements)

References 22 publications

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“…To expand the exploration of network public opinion monitoring, some scholars have introduced the concept of geographical analysis (Chen et al, 2021;Koylu, 2019). This trend is highly welcomed, as geographical data can provide strong support for reducing the network public opinion risks (Dong et al, 2017;Franch-Pardo et al, 2020;Middya and Roy, 2022).…”

Section: Introductionmentioning

confidence: 99%

Risk identification of public opinion on social media: a new approach based on cross-spatial network analysis

Li,

Xu,

Riaz

et al. 2024

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Purpose This study aims to use geographical information on social media for public opinion risk identification during a crisis. Design/methodology/approach This study constructs a double-layer network that associates the online public opinion with geographical information. In the double-layer network, Gaussian process regression is used to train the prediction model for geographical locations. Second, cross-space information flow is described using local government data availability and regional internet development indicators. Finally, the structural characteristics and information flow of the double-layer network are explored to capture public opinion risks in a fine-grained manner. This study used the early stages of the COVID-19 outbreak for validation analyses, and it collected more than 90,000 pieces of public opinion data from microblogs. Findings In the early stages of the COVID-19 outbreak, the double-layer network exhibited a radiating state, and the information dissemination was more dependent on the nodes with higher in-degree. Moreover, the double-layer network structure showed geographical differences. The risk contagion was more significant in areas where information flow was prominent, but the influence of nodes was reduced. Originality/value Public opinion risk identification that incorporates geographical scenarios contributes to enhanced situational awareness. This study not only effectively extends geographical information on social media, but also provides valuable insights for accurately responding to public opinion.

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Section: Introductionmentioning

confidence: 99%

Risk identification of public opinion on social media: a new approach based on cross-spatial network analysis

Li,

Xu,

Riaz

et al. 2024

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“…The implementation of Deep Learning (DL) in time series has been one of the most used techniques so far (Middya and Roy 2022), where the greatest advantage is the ability to identify predictive patterns and make decisions in uncertain situations, since they are able to organize and learn autonomously to approach resolutions of complex and nonlinear functions (Wu et al 2020).…”

Section: Introductionmentioning

confidence: 99%

Forecasting Time Series Covid 19 Using Deep Learning Methods: South America Comparative Case Study

Alfaro,

Vargas,

Fuertes

et al. 2023

Preprint

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The global pandemic of COVID-19 that took place in 2020 and 2021 posed different challenges for health systems worldwide, revealing various deficiencies and generating supply problems and a breakdown in medical services. Given this situation, it is crucial to have predictive methodologies that can accurately estimate the behavior of diseases of this type. This would allow countries to be better prepared in the future and respond effectively to future similar situations, avoiding a repetition of large-scale events. In the literature, deep learning techniques, in particular, have shown promise in this field. In this paper, a comparative study is performed between individual Deep Learning models, such as Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), along with hybrid models that combine Conv1D with LSTM or GRU. The objective is to predict contagion curves in Latin American countries, specifically in Argentina, Brazil, Chile, Colombia, and Peru. These countries present a dwindling number of studies in the existing literature, which makes this work especially relevant. The results emphasize the competitiveness of the hybrid models, which show MAPE values ranging from 0.1–1%. In contrast, the individual models present slightly higher MAPE, in the range of 0.2–0.8%. These results demonstrate the effectiveness of the proposed Deep Learning models in predicting the spread of COVID-19 in South America.

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Using deep learning via long-short-term memory model prediction of COVID-19 situation in India

Rout¹,

Sahu²,

Das

et al. 2023

Deep Learning in Personalized Healthcare and Decision Support

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Spatio-temporal variation of Covid-19 health outcomes in India using deep learning based models

Cited by 7 publications

References 22 publications

Risk identification of public opinion on social media: a new approach based on cross-spatial network analysis

Risk identification of public opinion on social media: a new approach based on cross-spatial network analysis

Forecasting Time Series Covid 19 Using Deep Learning Methods: South America Comparative Case Study

Using deep learning via long-short-term memory model prediction of COVID-19 situation in India

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