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Isfahan and Covid-19: Deep spatiotemporal representation
Chaos, Solitons & Fractals ( IF 5.3 ) Pub Date : 2020-10-05 , DOI: 10.1016/j.chaos.2020.110339
Rahele Kafieh 1 , Narges Saeedizadeh 1 , Roya Arian 1 , Zahra Amini 1 , Nasim Dadashi Serej 1 , Atefeh Vaezi 2 , Shaghayegh Haghjooy Javanmard 3
Affiliation  

The coronavirus COVID-19 is affecting 213 countries and territories around the world. Iran was one of the first affected countries by this virus. Isfahan, as the third most populated province of Iran, experienced a noticeable epidemic. The prediction of epidemic size, peak value, and peak time can help policymakers in correct decisions. In this study, deep learning is selected as a powerful tool for forecasting this epidemic in Isfahan. A combination of effective Social Determinant of Health (SDH) and the occurrences of COVID-19 data are used as spatiotemporal input by using time-series information from different locations. Different models are utilized, and the best performance is found to be for a tailored type of long short-term memory (LSTM). This new method incorporates the mutual effect of all classes (confirmed/ death / recovered) in the prediction process. The future trajectory of the outbreak in Isfahan is forecasted with the proposed model. The paper demonstrates the positive effect of adding SDHs in pandemic prediction. Furthermore, the effectiveness of different SDHs is discussed, and the most effective terms are introduced. The method expresses high ability in both short- and long- term forecasting of the outbreak. The model proves that in predicting one class (like the number of confirmed cases), the effect of other accompanying numbers (like death and recovered cases) cannot be ignored. In conclusion, the superiorities of this model (particularity the long term predication ability) turn it into a reliable tool for helping the health decision-makers.



中文翻译:

伊斯法罕和 Covid-19:深度时空表征

冠状病毒 COVID-19 正在影响全球 213 个国家和地区。伊朗是最先受到该病毒影响的国家之一。伊斯法罕作为伊朗人口第三大省,经历了一场引人注目的疫情。疫情规模、峰值、高峰时间的预测可以帮助政策制定者做出正确的决策。在这项研究中,深度学习被选为预测伊斯法罕疫情的有力工具。通过使用来自不同位置的时间序列信息,将有效的健康社会决定因素 (SDH) 和 COVID-19 数据的发生情况相结合作为时空输入。使用不同的模型,发现最佳性能是针对定制类型的长短期记忆 (LSTM)。这种新方法在预测过程中结合了所有类别(确诊/死亡/康复)的相互影响。使用所提出的模型预测了伊斯法罕疫情的未来轨迹。该论文证明了在大流行预测中添加 SDH 的积极作用。此外,还讨论了不同 SDH 的有效性,并介绍了最有效的术语。该方法在爆发的短期和长期预测方面表现出很高的能力。该模型证明,在预测某一类别(如确诊病例数)时,不能忽视其他伴随数字(如死亡和康复病例)的影响。总之,该模型的优越性(特别是长期预测能力)使其成为帮助健康决策者的可靠工具。

更新日期:2020-10-16
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