tetano
Editor, Senior Moderator
Fundam Res
. 2024 Mar 5;4(3):527-539.
doi: 10.1016/j.fmre.2024.02.006. eCollection 2024 May. High-resolution short-term prediction of the COVID-19 epidemic based on spatial-temporal model modified by historical meteorological data
Bin Chen[SUP] 1 2 [/SUP], Ruming Chen[SUP] 1 2 [/SUP], Lin Zhao[SUP] 1 [/SUP], Yuxiang Ren[SUP] 1 [/SUP], Li Zhang[SUP] 1 [/SUP], Yingjie Zhao[SUP] 1 [/SUP], Xinbo Lian[SUP] 1 [/SUP], Wei Yan[SUP] 1 [/SUP], Shuoyuan Gao[SUP] 1 [/SUP]
Affiliations
In the global challenge of Coronavirus disease 2019 (COVID-19) pandemic, accurate prediction of daily new cases is crucial for epidemic prevention and socioeconomic planning. In contrast to traditional local, one-dimensional time-series data-based infection models, the study introduces an innovative approach by formulating the short-term prediction problem of new cases in a region as multidimensional, gridded time series for both input and prediction targets. A spatial-temporal depth prediction model for COVID-19 (ConvLSTM) is presented, and further ConvLSTM by integrating historical meteorological factors (Meteor-ConvLSTM) is refined, considering the influence of meteorological factors on the propagation of COVID-19. The correlation between 10 meteorological factors and the dynamic progression of COVID-19 was evaluated, employing spatial analysis techniques (spatial autocorrelation analysis, trend surface analysis, etc.) to describe the spatial and temporal characteristics of the epidemic. Leveraging the original ConvLSTM, an artificial neural network layer is introduced to learn how meteorological factors impact the infection spread, providing a 5-day forecast at a 0.01° × 0.01° pixel resolution. Simulation results using real dataset from the 3.15 outbreak in Shanghai demonstrate the efficacy of Meteor-ConvLSTM, with reduced RMSE of 0.110 and increased R [SUP]2[/SUP] of 0.125 (original ConvLSTM: RMSE = 0.702, R [SUP]2[/SUP] = 0.567; Meteor-ConvLSTM: RMSE = 0.592, R [SUP]2[/SUP] = 0.692), showcasing its utility for investigating the epidemiological characteristics, transmission dynamics, and epidemic development.
Keywords: COVID-19; ConvLSTM; Meteorological factors; Prediction; Refined prediction; Spatial-temporal analysis.
. 2024 Mar 5;4(3):527-539.
doi: 10.1016/j.fmre.2024.02.006. eCollection 2024 May. High-resolution short-term prediction of the COVID-19 epidemic based on spatial-temporal model modified by historical meteorological data
Bin Chen[SUP] 1 2 [/SUP], Ruming Chen[SUP] 1 2 [/SUP], Lin Zhao[SUP] 1 [/SUP], Yuxiang Ren[SUP] 1 [/SUP], Li Zhang[SUP] 1 [/SUP], Yingjie Zhao[SUP] 1 [/SUP], Xinbo Lian[SUP] 1 [/SUP], Wei Yan[SUP] 1 [/SUP], Shuoyuan Gao[SUP] 1 [/SUP]
Affiliations
- PMID: 38933202
- PMCID: PMC11197671
- DOI: 10.1016/j.fmre.2024.02.006
In the global challenge of Coronavirus disease 2019 (COVID-19) pandemic, accurate prediction of daily new cases is crucial for epidemic prevention and socioeconomic planning. In contrast to traditional local, one-dimensional time-series data-based infection models, the study introduces an innovative approach by formulating the short-term prediction problem of new cases in a region as multidimensional, gridded time series for both input and prediction targets. A spatial-temporal depth prediction model for COVID-19 (ConvLSTM) is presented, and further ConvLSTM by integrating historical meteorological factors (Meteor-ConvLSTM) is refined, considering the influence of meteorological factors on the propagation of COVID-19. The correlation between 10 meteorological factors and the dynamic progression of COVID-19 was evaluated, employing spatial analysis techniques (spatial autocorrelation analysis, trend surface analysis, etc.) to describe the spatial and temporal characteristics of the epidemic. Leveraging the original ConvLSTM, an artificial neural network layer is introduced to learn how meteorological factors impact the infection spread, providing a 5-day forecast at a 0.01° × 0.01° pixel resolution. Simulation results using real dataset from the 3.15 outbreak in Shanghai demonstrate the efficacy of Meteor-ConvLSTM, with reduced RMSE of 0.110 and increased R [SUP]2[/SUP] of 0.125 (original ConvLSTM: RMSE = 0.702, R [SUP]2[/SUP] = 0.567; Meteor-ConvLSTM: RMSE = 0.592, R [SUP]2[/SUP] = 0.692), showcasing its utility for investigating the epidemiological characteristics, transmission dynamics, and epidemic development.
Keywords: COVID-19; ConvLSTM; Meteorological factors; Prediction; Refined prediction; Spatial-temporal analysis.