对缅甸仰光地区登革热病例报告的 SARIMA 时间序列预测

Soe Htet Aung, A. M. Kyaw, Suparat Phuanukoonnon, P. Jittamala, N. Soonthornworasiri
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引用次数: 0

摘要

登革热在缅甸是一项重大的公共卫生挑战,需要准确的监测才能减轻其影响。本研究旨在利用 2002 年 1 月至 2022 年 12 月的历史数据,建立缅甸仰光地区登革热病例预测模型,以加强流行病学监测和疫情管理。这项回顾性观察研究采用季节自回归综合移动平均模型(SARIMA)进行预测分析,对仰光地区 2002 年 1 月至 2022 年 12 月的登革热病例进行了研究。确定的最准确模型是 SARIMA (2,0,1) (1,1,1) 12,其 AIC(阿凯克信息准则)为 206.19,MAPE(平均绝对百分比误差)为 1.47%。根据该模型,登革热病例的高峰期预计为 2023 年 7 月,该年 1 月至 12 月间估计将出现 451 例登革热病例。仰光各乡镇登革热发病率的空间差异强调了采取有针对性干预措施的必要性。虽然 SARIMA 模型很有价值,但还必须考虑许多其他风险因素,如气候、移民模式、病毒特征和社会生态因素,以提高预测的准确性。这些发现有助于公共卫生决策者预防和管理缅甸登革热疫情。不过,还需要开展更多研究,将更多风险因素纳入模型,以全面了解登革热流行病学并提高预测准确性。
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A SARIMA time series forecasting for dengue cases for reporting to Yangon Region, Myanmar
Dengue fever is a significant public health challenge in Myanmar, which requires accurate monitoring to mitigate its impact. The study aimed to develop a forecasting model for dengue cases in Myanmar's Yangon region using historical data from January 2002 to December 2022, with the objective of enhancing epidemiological surveillance and outbreak management. This retrospective observational study examines dengue cases in Yangon from January 2002 to December 2022, employing Seasonal Autoregressive Integrated Moving Average (SARIMA) models for predictive analysis. The most accurate model identified was SARIMA (2,0,1) (1,1,1) 12, with an AIC (Akaike Information Criterion) of 206.19 and MAPE (Mean Absolute Percentage Error) of 1.47%. According to the model, a peak in dengue cases was expected in July 2023, with an estimated 451 cases between January and December that year. Spatial variations in dengue incidence across Yangon's townships emphasize the need for targeted interventions. While the SARIMA model is valuable, it would also be important to consider many other risk factors like climate, migration patterns, virus characteristics, and socioecological factors to improve forecasting accuracy. These findings can aid public health policymakers in preventing and managing dengue outbreaks in Myanmar. However, additional research is needed to incorporate additional risk factors into the model to comprehensively understand dengue epidemiology and improve forecasting accuracy.
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来源期刊
Journal of Public Health and Development
Journal of Public Health and Development Social Sciences-Health (social science)
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0.50
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64
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