Hybrid deep learning of social media big data for predicting the evolution of COVID-19 transmission

Chew, Alvin Wei Ze; Pan, Y.; Wang, Ying; Zhang, Limao

doi:10.1016/j.knosys.2021.107417

Cited by 29 publications

(6 citation statements)

References 45 publications

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“…e fact that in uenza has such a substantial in uence on the economy and peoples' health [9][10][11] makes it imperative to predict who will become ill and when. If IAV-infected people are not identi ed and treated promptly, the number of deaths will rise as a result of increased viral transmission and, possibly, worsening of sickness [12,13]. On the other hand, in uenza vaccines may not be e ective for all people [14], despite the fact that immunisation is recommended for the prevention of the flu as well as the promotion of public health among hospitals, educational institutions, societies, and communities.…”

Section: Introductionmentioning

confidence: 99%

Deep Neural Networks for Optimal Selection of Features Related to Flu

Rao¹,

Kumar

Padmapriya³

et al. 2022

Evidence-Based Complementary and Alternative Medicine

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In recent times, humans who have been exposed to influenza A viruses (IAV) may not become hostile. Despite the fact that KLRD1 has been discovered as an influenza susceptibility biomarker, it remains to be seen if pre-exposure host gene expression can predict flu symptoms. In this paper, we enable the examination of flu using deep neural networks from input human gene expression datasets with various subtype viruses. This study enables the utilization of these datasets to forecast the spread of flu and can provide the necessary steps to eradicate the flu. The simulation is conducted to test the efficiency of the model in predicting the spread against various input datasets. The results of the simulation show that the proposed method offers a better prediction ability of 2.98% more than other existing methods in finding the spread of flu.

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Section: Introductionmentioning

confidence: 99%

Deep Neural Networks for Optimal Selection of Features Related to Flu

Rao¹,

Kumar

Padmapriya³

et al. 2022

Evidence-Based Complementary and Alternative Medicine

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show abstract

“…ML has been used both as a standalone model 26 or as a top layer over classical epidemiological models 27 . ML models have been used to exploit different big data sources 28,29 or incorporating heterogeneous features 30 . Also, several general evaluations of the applicability of these models exist [31][32][33][34] .…”

Section: Related Workmentioning

confidence: 99%

Forecasting COVID-19 spreading through an ensemble of classical and machine learning models: Spain’s case study

Heredia

Díaz

Melguizo

et al. 2023

Sci Rep

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In this work the applicability of an ensemble of population and machine learning models to predict the evolution of the COVID-19 pandemic in Spain is evaluated, relying solely on public datasets. Firstly, using only incidence data, we trained machine learning models and adjusted classical ODE-based population models, especially suited to capture long term trends. As a novel approach, we then made an ensemble of these two families of models in order to obtain a more robust and accurate prediction. We then proceed to improve machine learning models by adding more input features: vaccination, human mobility and weather conditions. However, these improvements did not translate to the overall ensemble, as the different model families had also different prediction patterns. Additionally, machine learning models degraded when new COVID variants appeared after training. We finally used Shapley Additive Explanation values to discern the relative importance of the different input features for the machine learning models’ predictions. The conclusion of this work is that the ensemble of machine learning models and population models can be a promising alternative to SEIR-like compartmental models, especially given that the former do not need data from recovered patients, which are hard to collect and generally unavailable.

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“…These socially generated activities can be collected and analyzed for understanding the relationship between public discourse and how an emergency event unfolds at the ground level [5] . For example, in [35] , Chew et al. used semantic word vectors as a representation of the public’s response to the pandemic to forecast the daily growth rate in the number of global confirmed COVID-19 cases with a lead-time of 1 day for the period January 25, 2020, and May 11, 2020.…”

Section: Related Workmentioning

confidence: 99%