文献检索文档翻译深度研究
Suppr Zotero 插件Zotero 插件
邀请有礼套餐&价格历史记录

新学期,新优惠

限时优惠:9月1日-9月22日

30天高级会员仅需29元

1天体验卡首发特惠仅需5.99元

了解详情
不再提醒
插件&应用
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
高级版
套餐订阅购买积分包
AI 工具
文献检索文档翻译深度研究
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2025

Forecasting the spread of the third wave of COVID-19 pandemic using time series analysis in Bangladesh.

作者信息

Kibria Hafsa Binte, Jyoti Oishi, Matin Abdul

机构信息

Department of Electrical & Computer Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh.

出版信息

Inform Med Unlocked. 2022;28:100815. doi: 10.1016/j.imu.2021.100815. Epub 2021 Dec 22.


DOI:10.1016/j.imu.2021.100815
PMID:34961844
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8694818/
Abstract

During the third wave of the coronavirus epidemic in Bangladesh, the death and infection rate due to this devastating virus has increased dramatically. The rapid spread of the virus is one of the reasons for this terrible condition. So, identifying the subsequent cases of coronavirus can be a great tool to reduce the mortality and infection rate. In this article, we used the autoregressive integrated moving average-ARIMA(8,1,7) model to estimate the expected daily number of COVID-19 cases in Bangladesh based on the data from April 20, 2021, to July 4, 2021. The ARIMA model showed the best results among the five executed models over Autoregressive Model (AR), Moving Average (MA), Autoregressive Moving Average (ARMA), and Rolling Forest Origin. The findings of this article were used to anticipate a rise in daily cases for the next month in Bangladesh, which can help governments plan policies to prevent the spread of the virus. The forecasting outcome indicated that this new trend(named delta variant) in Bangladesh would continue increasing and might reach 18327 daily new cases within four weeks if strict rules and regulations are not applied to control the spread of COVID-19.

摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c66/8694818/76eee6851adf/gr9_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c66/8694818/5867e7d45599/gr1_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c66/8694818/d938a002d7b5/gr2_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c66/8694818/b4a1661fff81/gr3_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c66/8694818/e793744d51a3/gr4_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c66/8694818/23ae7660aac8/gr5_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c66/8694818/1238c25e0caa/gr6_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c66/8694818/4438c0f137fc/gr7_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c66/8694818/daada0a44a99/gr8_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c66/8694818/76eee6851adf/gr9_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c66/8694818/5867e7d45599/gr1_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c66/8694818/d938a002d7b5/gr2_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c66/8694818/b4a1661fff81/gr3_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c66/8694818/e793744d51a3/gr4_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c66/8694818/23ae7660aac8/gr5_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c66/8694818/1238c25e0caa/gr6_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c66/8694818/4438c0f137fc/gr7_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c66/8694818/daada0a44a99/gr8_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c66/8694818/76eee6851adf/gr9_lrg.jpg

相似文献

[1]
Forecasting the spread of the third wave of COVID-19 pandemic using time series analysis in Bangladesh.

Inform Med Unlocked. 2022

[2]
Forecasting the spread of the COVID-19 pandemic in Saudi Arabia using ARIMA prediction model under current public health interventions.

J Infect Public Health. 2020-6-8

[3]
Application of one-, three-, and seven-day forecasts during early onset on the COVID-19 epidemic dataset using moving average, autoregressive, autoregressive moving average, autoregressive integrated moving average, and naïve forecasting methods.

Data Brief. 2021-4

[4]
Accuracy comparison of ARIMA and XGBoost forecasting models in predicting the incidence of COVID-19 in Bangladesh.

PLOS Glob Public Health. 2022-5-18

[5]
Prediction of the COVID-19 Pandemic for the Top 15 Affected Countries: Advanced Autoregressive Integrated Moving Average (ARIMA) Model.

JMIR Public Health Surveill. 2020-5-13

[6]
Forecasting the spread of the COVID-19 pandemic in Kenya using SEIR and ARIMA models.

Infect Dis Model. 2022-6

[7]
Forecasting COVID-19 situation in Bangladesh.

Biosaf Health. 2022-2

[8]
Interruption time series analysis using autoregressive integrated moving average model: evaluating the impact of COVID-19 on the epidemic trend of gonorrhea in China.

BMC Public Health. 2023-10-23

[9]
Forecasting the incidence of dengue in Bangladesh-Application of time series model.

Health Sci Rep. 2022-6-8

[10]
Trend Analysis and Forecasting the Spread of COVID-19 Pandemic in Ethiopia Using Box-Jenkins Modeling Procedure.

Int J Gen Med. 2021-4-21

引用本文的文献

[1]
Research on Dynamic Outpatient Respiratory Nosocomial Infection Control Methods Through Multi-Data Prediction.

Risk Manag Healthc Policy. 2025-4-15

[2]
Forecasting of coronavirus active cases by utilizing logistic growth model and fuzzy time series techniques.

Sci Rep. 2024-8-4

[3]
Development of a novel dynamic nosocomial infection risk management method for COVID-19 in outpatient settings.

BMC Infect Dis. 2024-2-19

[4]
An Ensemble Approach for the Prediction of Diabetes Mellitus Using a Soft Voting Classifier with an Explainable AI.

Sensors (Basel). 2022-9-25

[5]
A Bioinformatics Tool for Predicting Future COVID-19 Waves Based on a Retrospective Analysis of the Second Wave in India: Model Development Study.

JMIR Bioinform Biotechnol. 2022-9-22

[6]
Modeling Global COVID-19 Dissemination Data After the Emergence of Omicron Variant Using Multipronged Approaches.

Curr Microbiol. 2022-8-10

[7]
Sero-prevalence and risk factors for Severe Acute Respiratory Syndrome Coronavirus 2 infection in women and children in a rural district of Bangladesh: A cohort study.

J Glob Health. 2022-7-23

[8]
COVID-19 Spatio-Temporal Evolution Using Deep Learning at a European Level.

Sensors (Basel). 2022-5-11

本文引用的文献

[1]
The Epidemiological Characteristics of an Outbreak of 2019 Novel Coronavirus Diseases (COVID-19) - China, 2020.

China CDC Wkly. 2020-2-21

[2]
Prediction of confirmed cases of and deaths caused by COVID-19 in Chile through time series techniques: A comparative study.

PLoS One. 2021

[3]
Analysis and Prediction of COVID-19 Pandemic in Bangladesh by Using ANFIS and LSTM Network.

Cognit Comput. 2021

[4]
Forecasting Models for Coronavirus Disease (COVID-19): A Survey of the State-of-the-Art.

SN Comput Sci. 2020

[5]
ARIMA modelling & forecasting of COVID-19 in top five affected countries.

Diabetes Metab Syndr. 2020

[6]
Forecasting the spread of the COVID-19 pandemic in Saudi Arabia using ARIMA prediction model under current public health interventions.

J Infect Public Health. 2020-6-8

[7]
The scientific literature on Coronaviruses, COVID-19 and its associated safety-related research dimensions: A scientometric analysis and scoping review.

Saf Sci. 2020-9

[8]
Estimation of COVID-19 prevalence in Italy, Spain, and France.

Sci Total Environ. 2020-4-22

[9]
Prediction for the spread of COVID-19 in India and effectiveness of preventive measures.

Sci Total Environ. 2020-4-20

[10]
Time series analysis of malaria in Afghanistan: using ARIMA models to predict future trends in incidence.

Malar J. 2016-11-22

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

推荐工具

医学文档翻译智能文献检索