Department of Computer Science & Engineering, GITAM School of Technology, GITAM Deemed to Be University, Visakhapatnam, 530045, India.
School of Technology, Woxsen University, Hyderabad, Telangana, 502345, India.
Sci Rep. 2024 Aug 4;14(1):18039. doi: 10.1038/s41598-024-67161-z.
Coronavirus has long been considered a global epidemic. It caused the deaths of nearly 7.01 million individuals and caused an economic downturn. The number of verified coronavirus cases is increasing daily, putting the whole human race at danger and putting strain on medical experts to eradicate the disease as rapidly as possible. As a consequence, it is vital to predict the upcoming coronavirus positive patients in order to plan actions in the future. Furthermore, it has been discovered all across the globe that asymptomatic coronavirus patients play a significant part in the disease's transmission. This prompted us to incorporate similar examples in order to accurately forecast trends. A typical strategy for analysing the rate of pandemic infection is to use time-series forecasting technique. This would assist us in developing better decision support systems. To anticipate COVID-19 active cases for a few countries, we recommended a hybrid model utilizing a fuzzy time series (FTS) model mixed with a non-linear growth model. The coronavirus positive case outbreak has been evaluated for Italy, Brazil, India, Germany, Pakistan, and Myanmar through June 5, 2020 in phase-1, and January 15, 2022 in phase-2, and forecasts active cases for the next 26 and 14 days respectively. The proposed framework fitting effect outperforms individual logistic growth and the fuzzy time series techniques, with R-scores of 0.9992 in phase-1 and 0.9784 in phase-2. The proposed model provided in this article may be utilised to comprehend a country's epidemic pattern and assist the government in developing better effective interventions.
冠状病毒长期以来一直被认为是一种全球性的流行病。它导致近 701 万人死亡，并导致经济衰退。每日确诊的冠状病毒病例数量不断增加，使整个人类处于危险之中，并使医学专家承受着尽快消除这种疾病的压力。因此，预测即将到来的冠状病毒阳性患者对于未来的规划行动至关重要。此外，全球各地都发现无症状冠状病毒患者在疾病传播中起着重要作用。这促使我们纳入类似的例子，以便准确预测趋势。分析大流行病感染率的典型策略是使用时间序列预测技术。这将帮助我们开发更好的决策支持系统。为了预测几个国家的 COVID-19 活跃病例，我们建议使用混合模型，该模型结合了模糊时间序列（FTS）模型和非线性增长模型。我们通过 2020 年 6 月 5 日的第 1 阶段和 2022 年 1 月 15 日的第 2 阶段评估了意大利、巴西、印度、德国、巴基斯坦和缅甸的冠状病毒阳性病例爆发情况，并分别对未来 26 天和 14 天的活跃病例进行了预测。与单独的逻辑增长和模糊时间序列技术相比，提出的框架拟合效果更好，第 1 阶段的 R 分数为 0.9992，第 2 阶段的 R 分数为 0.9784。本文提出的模型可用于理解一个国家的流行模式，并帮助政府制定更好的有效干预措施。
