PDF(Pubmed)
Abstract：
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 2 of 0.125 (original ConvLSTM: RMSE = 0.702, R 2 = 0.567; Meteor-ConvLSTM: RMSE = 0.592, R 2 = 0.692), showcasing its utility for investigating the epidemiological characteristics, transmission dynamics, and epidemic development.
摘要：
在2019年冠状病毒病(COVID-19)大流行的全球挑战中，准确预测每日新病例对于防疫和社会经济计划至关重要。与传统的当地相比，基于一维时间序列数据的感染模型，这项研究引入了一种创新的方法，将一个地区新病例的短期预测问题表述为多维，输入和预测目标的网格化时间序列。提出了COVID-19(ConvLSTM)的时空深度预测模型，并通过整合历史气象因素（Meteor-ConvLSTM）进一步完善ConvLSTM，考虑气象因素对COVID-19传播的影响。评价10个气象因子与COVID-19动态进展的相关性,采用空间分析技术(空间自相关分析，趋势面分析，等。)来描述疫情的时空特征。利用原始的ConvLSTM，引入了人工神经网络层，以了解气象因素如何影响感染传播，以0.01°×0.01°像素分辨率提供5天的预测。使用来自上海3.15疫情的真实数据集的模拟结果证明了Meteor-ConvLSTM的有效性，RMSE降低为0.110，R2增加为0.125（原始ConvLSTM：RMSE=0.702，R2=0.567；流星-ConvLSTM：RMSE=0.592，R2=0.692），展示其对流行病学特征调查的效用，传输动力学，和流行病的发展。
