Prediction of COVID-19 Based on Multivariable LSTM Neural Network
QI Yue
SHA Kun
Abstract：This paper explores the influence of meteorological factors on the prediction of COVID-19.Considering the meteo-rological factors such as daily maximum temperature,daily minimum temperature,daily average temperature,daily average wind speed and the existing daily confirmed case data of COVID-19,a multivariate Long short-term memory neural network(LSTM)pre-diction model is constructed.At the same time,in order to improve the accuracy and operation efficiency of the prediction model,the dimension of the multivariate input data is reduced,and the meteorological factors with high correlation with the daily number of confirmed cases of COVID-19 are selected as the input of the model using Spearman correlation coefficient.The multivariable LSTM model with high correlation meteorological factors and existing daily confirmed case data of COVID-19 as model input has the best prediction effect.The multivariable LSTM prediction model established in this experiment can accurately predict the number of con-firmed cases of COVID-19,and has a strong generalization ability.
Keywords：meteorological factorsLSTMCOVID-19forecast
Publication Date：2025-12-20
Online Publishing Date：2026-03-09(First online date of this platform, not the publication date of the document)
Pages：8( 3378-3385 )