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明日登革热：孟加拉国自回归积分滑动平均（ARIMA）模型和季节性自回归积分滑动平均（SARIMA）模型得出的预测性见解：一项时间序列分析

Dengue in Tomorrow: Predictive Insights From ARIMA and SARIMA Models in Bangladesh: A Time Series Analysis.

作者信息

Hasan Pratyay, Khan Tazdin Delwar, Alam Ishteaque, Haque Mohammad Emdadul

机构信息

Department of Respiratory Medicine (OPD) Dhaka Medical College Hospital Dhaka Bangladesh.

Department of Cardiac Anesthesiology Ibrahim Cardiac Hospital & Research Institute Dhaka Bangladesh.

出版信息

Health Sci Rep. 2024 Dec 18;7(12):e70276. doi: 10.1002/hsr2.70276. eCollection 2024 Dec.

DOI:10.1002/hsr2.70276
PMID:39698526
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11653090/
Abstract

BACKGROUNDS AND AIMS

Dengue fever has been a continued public health problem in Bangladesh, with a recent surge in cases. The aim of this study was to train ARIMA and SARIMA models for time series analysis on the monthly prevalence of dengue in Bangladesh and to use these models to forecast the dengue prevalence for the next 12 months.

METHODS

This secondary data-based study utilizes AutoRegressive Integrated Moving Average (ARIMA) and Seasonal AutoRegressive Integrated Moving Average (SARIMA) models to forecast dengue prevalence in Bangladesh. Data was sourced from the Institute of Epidemiology Disease Control and Research (IEDCR) and the Directorate General of Health Services (DGHS). STROBE Guideline for observational studies was followed for reporting this study.

RESULTS

The ARIMA (1,1,1) and SARIMA (1,1,2) models were identified as the best-performing models. The forecasts indicate a steady dengue prevalence for 2024 according to ARIMA, while SARIMA predicts significant fluctuations. It was observed that ARIMA (1,1,1) and SARIMA (1,2,2) (1,1,2) were the most suitable models for prediction of dengue prevalence.

CONCLUSION

These models offer valuable insights for healthcare planning and resource allocation, although external factors and complex interactions must be considered. Dengue prevalence is expected to rise in future in Bangladesh.

摘要

背景与目的

登革热一直是孟加拉国持续存在的公共卫生问题，近期病例数激增。本研究的目的是训练自回归积分滑动平均（ARIMA）模型和季节性自回归积分滑动平均（SARIMA）模型，用于对孟加拉国登革热的月度流行情况进行时间序列分析，并使用这些模型预测未来12个月的登革热流行情况。

方法

这项基于二手数据的研究利用自回归积分滑动平均（ARIMA）模型和季节性自回归积分滑动平均（SARIMA）模型预测孟加拉国的登革热流行情况。数据来源于流行病学、疾病控制与研究机构（IEDCR）和卫生服务总局（DGHS）。本研究遵循观察性研究的STROBE指南进行报告。

结果

ARIMA（1,1,1）和SARIMA（1,1,2）模型被确定为表现最佳的模型。根据ARIMA模型，预测显示2024年登革热流行情况将保持稳定，而SARIMA模型预测会有显著波动。据观察，ARIMA（1,1,1）和SARIMA（1,2,2）（1,1,2）是预测登革热流行情况最合适的模型。

结论

这些模型为医疗规划和资源分配提供了有价值的见解，不过必须考虑外部因素和复杂的相互作用。预计未来孟加拉国登革热流行情况将上升。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3d6e/11653090/739f43eca557/HSR2-7-e70276-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3d6e/11653090/de0116eb5db2/HSR2-7-e70276-g006.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3d6e/11653090/8e4049b1c641/HSR2-7-e70276-g008.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3d6e/11653090/022e853f9cb0/HSR2-7-e70276-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3d6e/11653090/2de54c50cbcf/HSR2-7-e70276-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3d6e/11653090/f5deab9402f0/HSR2-7-e70276-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3d6e/11653090/8d49b39c8b76/HSR2-7-e70276-g007.jpg
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