Rayguru Chinmayee, Husnayain Atina, Chiu Hua-Sheng, Sumazin Pavel, Su Emily Chia-Yu
Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan.
Department of Public Health, Monash University, Indonesia.
Comput Biol Med. 2025 Feb;185:109493. doi: 10.1016/j.compbiomed.2024.109493. Epub 2024 Dec 3.
This study aimed to outline a machine learning model to assess the effectiveness of vaccination in COVID-19 confirmed cases and fatalities. The proposed model was evaluated using external validation to ensure optimal protection of vaccinated populations, distinguishing between males and females.
The data from the Centers for Disease Control and Prevention (CDC) in the US, collected between 2021 and 2023, were preprocessed through merging and imputation. A deep learning long short-term memory (LSTM) model was developed to analyze the effectiveness of vaccination in predicting COVID-19 cases and fatalities. The model, which was validated internally and externally, examined the impact of vaccination according to sex. The performance was assessed against current state-of-the-art models, with the LSTM model exhibiting lower root mean square error (RMSE) values.
We performed intra-, inter-, and external-validation analyses. First, one- and two-dose vaccinations significantly reduced the number of COVID-19 cases and mortality in highly affected states. Second, in the inter-model analysis, the LSTM outperformed the autoregressive integrated moving average (ARIMA) model in predicting cases and deaths, yielding superior results for Texas, California, and Florida. Third, with external validation, our LSTM model effectively predicted vaccination impacts regardless of sex.
Our study demonstrates the effectiveness of COVID-19 vaccination, showing that full vaccination significantly reduced the number of confirmed cases and deaths, influencing future public health policies.
本研究旨在概述一种机器学习模型，以评估疫苗接种在新冠确诊病例和死亡病例中的有效性。使用外部验证对所提出的模型进行评估，以确保对接种人群的最佳保护，并区分男性和女性。
对美国疾病控制与预防中心（CDC）在2021年至2023年期间收集的数据进行合并和插补预处理。开发了一种深度学习长短期记忆（LSTM）模型，以分析疫苗接种在预测新冠病例和死亡方面的有效性。该模型在内部和外部进行了验证，根据性别检查了疫苗接种的影响。与当前最先进的模型进行性能评估，LSTM模型表现出更低的均方根误差（RMSE）值。
我们进行了内部、模型间和外部验证分析。首先，一剂和两剂疫苗接种显著减少了高感染州的新冠病例数和死亡率。其次，在模型间分析中，LSTM在预测病例和死亡方面优于自回归积分移动平均（ARIMA）模型，在德克萨斯州、加利福尼亚州和佛罗里达州产生了更好的结果。第三，通过外部验证，我们的LSTM模型无论性别如何都能有效预测疫苗接种的影响。
我们的研究证明了新冠疫苗接种的有效性，表明全面接种显著减少了确诊病例数和死亡数，对未来的公共卫生政策产生影响。
