Muñoz-Organero Mario, Callejo Patricia, Hombrados-Herrera Miguel Ángel
Telematic Engineering Department, Universidad Carlos III de Madrid, Leganes, 28911, Madrid, Spain.
Heliyon. 2023 Jun;9(6):e17625. doi: 10.1016/j.heliyon.2023.e17625. Epub 2023 Jun 24.
As a respiratory virus, COVID-19 propagates based on human-to-human interactions with positive COVID-19 cases. The temporal evolution of new COVID-19 infections depends on the existing number of COVID-19 infections and the people's mobility. This article proposes a new model to predict upcoming COVID-19 incidence values that combines both current and near-past incidence values together with mobility data. The model is applied to the city of Madrid (Spain). The city is divided into districts. The weekly COVID-19 incidence data per district is used jointly with a mobility estimation based on the number of rides reported by the bike-sharing service in the city of Madrid (BiciMAD). The model employs a Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) to detect temporal patterns for COVID-19 infections and mobility data, and combines the output of the LSTM layers into a dense layer that can learn the spatial patterns (the spread of the virus between districts). A baseline model that employs a similar RNN but only based on the COVID-19 confirmed cases with no mobility data is presented and used to estimate the model gain when adding mobility data. The results show that using the bike-sharing mobility estimation the proposed model increases the accuracy by 11.7% compared with the baseline model.
作为一种呼吸道病毒，新型冠状病毒肺炎（COVID-19）通过与COVID-19阳性病例的人际接触进行传播。新的COVID-19感染的时间演变取决于现有的COVID-19感染数量和人们的流动性。本文提出了一种新模型，将当前和近期发病率值与流动性数据结合起来，以预测即将到来的COVID-19发病率值。该模型应用于西班牙马德里市。该市被划分为多个区。每个区的每周COVID-19发病率数据与基于马德里市共享单车服务（BiciMAD）报告的骑行次数的流动性估计共同使用。该模型采用长短期记忆（LSTM）递归神经网络（RNN）来检测COVID-19感染和流动性数据的时间模式，并将LSTM层的输出合并到一个密集层中，该密集层可以学习空间模式（病毒在各区之间的传播）。提出了一个采用类似RNN但仅基于COVID-19确诊病例且无流动性数据的基线模型，并用于估计添加流动性数据时模型的增益。结果表明，与基线模型相比，使用共享单车流动性估计，所提出的模型提高了11.7%的准确率。
