Infectivity Upsurge by COVID-19 Viral Variants in Japan: Evidence from Deep Learning Modeling.
Infectivity Upsurge by COVID-19 Viral Variants in Japan: Evidence from Deep Learning Modeling.
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DOI：
10.3390/ijerph18157799
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发表时间：
2021-07-22
影响因子：
--
通讯作者：
Hirata A
中科院分区：
文献类型：
--
作者：
Rashed EA;Hirata A
COVID-19对健康和经济的重大影响强调了对可靠预测模型的需求，以避免医疗设施因医院超负荷而突然崩溃。根据病毒传播早期阶段获得的数据，已经开发了几种预测模型。然而，随着新病毒变种的出现，目前还不清楚新毒株如何影响使用早期数据采用的模型进行预测的效率。在这项研究中，我们使用机器学习模型分析了每日阳性病例（DPC）数据，以了解新病毒变体对发病率的影响。一个考虑了几个环境和流动性因素的深度学习模型被用来预测日本六个地区的DPC。自COVID-19初期以来，通过使用训练数据进行机器学习预测，已对2021年3月之前获得的数据实现了高质量的估计。然而，在发现新的COVID-19变异体B. 1. 1. 7（Alpha）后，部分地区的疫情大幅上升。在α变体出现后，观察到DPC平均增加20-40%，有效繁殖数增加高达20%。机器学习模型需要大约四周的时间来调整新变体引起的预测误差。机器学习预测与报告值之间的比较表明，新病毒变体的出现应在COVID-19预测模型中加以考虑。这项研究提出了一种简单而有效的方法来量化新病毒变异引起的变化，对全球数据分析具有潜在的实用性。
The significant health and economic effects of COVID-19 emphasize the requirement for reliable forecasting models to avoid the sudden collapse of healthcare facilities with overloaded hospitals. Several forecasting models have been developed based on the data acquired within the early stages of the virus spread. However, with the recent emergence of new virus variants, it is unclear how the new strains could influence the efficiency of forecasting using models adopted using earlier data. In this study, we analyzed daily positive cases (DPC) data using a machine learning model to understand the effect of new viral variants on morbidity rates. A deep learning model that considers several environmental and mobility factors was used to forecast DPC in six districts of Japan. From machine learning predictions with training data since the early days of COVID-19, high-quality estimation has been achieved for data obtained earlier than March 2021. However, a significant upsurge was observed in some districts after the discovery of the new COVID-19 variant B.1.1.7 (Alpha). An average increase of 20–40% in DPC was observed after the emergence of the Alpha variant and an increase of up to 20% has been recognized in the effective reproduction number. Approximately four weeks was needed for the machine learning model to adjust the forecasting error caused by the new variants. The comparison between machine-learning predictions and reported values demonstrated that the emergence of new virus variants should be considered within COVID-19 forecasting models. This study presents an easy yet efficient way to quantify the change caused by new viral variants with potential usefulness for global data analysis.
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影响因子：
5.3
作者：
Devaraj J;Madurai Elavarasan R;Pugazhendhi R;Shafiullah GM;Ganesan S;Jeysree AK;Khan IA;Hossain E
通讯作者：
Hossain E
影响因子：
12.1
作者：
Mofijur M;Fattah IMR;Alam MA;Islam ABMS;Ong HC;Rahman SMA;Najafi G;Ahmed SF;Uddin MA;Mahlia TMI
通讯作者：
Mahlia TMI
SARS-COV-2血统的估计可传播和影响B.1.1.7在英国。
DOI：
10.1126/science.abg3055
发表时间：
2021-04-09
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Davies NG;Abbott S;Barnard RC;Jarvis CI;Kucharski AJ;Munday JD;Pearson CAB;Russell TW;Tully DC;Washburne AD;Wenseleers T;Gimma A;Waites W;Wong KLM;van Zandvoort K;Silverman JD;CMMID COVID-19 Working Group;COVID-19 Genomics UK (COG-UK) Consortium;Diaz-Ordaz K;Keogh R;Eggo RM;Funk S;Jit M;Atkins KE;Edmunds WJ
通讯作者：
Edmunds WJ
影响因子：
3.7
作者：
Noh J;Danuser G
通讯作者：
Danuser G
影响因子：
16.6
作者：
Nouvellet P;Bhatia S;Cori A;Ainslie KEC;Baguelin M;Bhatt S;Boonyasiri A;Brazeau NF;Cattarino L;Cooper LV;Coupland H;Cucunuba ZM;Cuomo-Dannenburg G;Dighe A;Djaafara BA;Dorigatti I;Eales OD;van Elsland SL;Nascimento FF;FitzJohn RG;Gaythorpe KAM;Geidelberg L;Green WD;Hamlet A;Hauck K;Hinsley W;Imai N;Jeffrey B;Knock E;Laydon DJ;Lees JA;Mangal T;Mellan TA;Nedjati-Gilani G;Parag KV;Pons-Salort M;Ragonnet-Cronin M;Riley S;Unwin HJT;Verity R;Vollmer MAC;Volz E;Walker PGT;Walters CE;Wang H;Watson OJ;Whittaker C;Whittles LK;Xi X;Ferguson NM;Donnelly CA
通讯作者：
Donnelly CA