Model parameters.

The pandemic has significantly affected many countries including the USA, UK, Asia, the Middle East and Africa region, and many other countries. Similarly, it has substantially affected Malaysia, making it crucial to develop efficient and precise forecasting tools for guiding public health policies and approaches. Our study is based on advanced deep-learning models to predict the SARS-CoV-2 cases. We evaluate the performance of Long Short-Term Memory (LSTM), Bi-directional LSTM, Convolutional Neural Networks (CNN), CNN-LSTM, Multilayer Perceptron, Gated Recurrent Unit (GRU), and Recurrent Neural Networks (RNN). We trained these models and assessed them using a detailed dataset of confirmed cases, demographic data, and pertinent socio-economic factors. Our research aims to determine the most reliable and accurate model for forecasting SARS-CoV-2 cases in the region. We were able to test and optimize deep learning models to predict cases, with each model displaying diverse levels of accuracy and precision. A comprehensive evaluation of the models’ performance discloses the most appropriate architecture for Malaysia’s specific situation. This study supports ongoing efforts to combat the pandemic by offering valuable insights into the application of sophisticated deep-learning models for precise and timely SARS-CoV-2 case predictions. The findings hold considerable implications for public health decision-making, empowering authorities to create targeted and data-driven interventions to limit the virus’s spread and minimize its effects on Malaysia’s population.

新冠大流行已对包括美国、英国、亚洲、中东及非洲地区在内的诸多国家造成严重冲击，同时也对马来西亚产生了显著影响，因此开发高效精准的预测工具以指导公共卫生政策与防控策略的制定显得至关重要。本研究基于先进深度学习模型开展新型冠状病毒（SARS-CoV-2）感染病例的预测工作，对长短期记忆网络（Long Short-Term Memory，LSTM）、双向长短期记忆网络、卷积神经网络（Convolutional Neural Networks，CNN）、CNN-LSTM、多层感知机、门控循环单元（Gated Recurrent Unit，GRU）以及循环神经网络（Recurrent Neural Networks，RNN）的模型性能进行了评估。研究团队使用包含确诊病例数据、人口统计数据及相关社会经济因素的详细数据集对上述模型进行训练与评估。本研究旨在确定适用于该地区新冠病例预测的最可靠、精准的模型。我们完成了各深度学习模型的测试与优化以实现病例预测，各模型均展现出不同水平的准确度与精确性。通过对模型性能的全面评估，可明确适配马来西亚具体国情的最优模型架构。本研究通过为精准及时的新冠病例预测提供复杂深度学习模型的应用思路，为当前抗疫工作提供了有价值的参考。研究结果对公共卫生决策制定具有重要意义，可助力相关部门制定针对性且基于数据的干预措施，以限制病毒传播并减轻其对马来西亚民众的影响。