Selected article for: "country spread and high level"
Author: swarna kamal paul; Saikat Jana; Parama Bhaumik
Title: A multivariate spatiotemporal spread model of COVID-19 using ensemble of ConvLSTM networks
  • Document date: 2020_4_22
    • ID: nng76upj_4
    Snippet: Hu et. al. developed a modified stacked autoencoder model of the epidemic spread in China and they claimed to achieve high level of forecasting accuracy [2] . On observing a universality in the epidemic spread in each country, Fanelli and Piazza [3] applied meanfield kinetics of Susceptible-Infected-Recovered/Dead epidemic model to forecast the spread and provided an estimation of peak infections in Italy. Zhan et. al. [8] integrated the intercit.....
    Document: Hu et. al. developed a modified stacked autoencoder model of the epidemic spread in China and they claimed to achieve high level of forecasting accuracy [2] . On observing a universality in the epidemic spread in each country, Fanelli and Piazza [3] applied meanfield kinetics of Susceptible-Infected-Recovered/Dead epidemic model to forecast the spread and provided an estimation of peak infections in Italy. Zhan et. al. [8] integrated the intercity migration data in China with Susceptible-Exposed-Infected-Removed model to forecast an estimation of epidemic spread in China. Xi et. al. [4] used deep residual networks to model spatiotemporal characteristics of the spread of influenza and experimented with real dataset of Shenzen city in China. Shi et. al. [5] proposed convolutional LSTM network for spatiotemporal modelling of localized rainfall over a short period of time and used it for rainfall nowcasting. Yuan et. al. [6] used an ensemble of ConvLSTM models to predict road accidents by using heterogeneous multi-layered spatiotemporal data.

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