{"title":"研究不同时期气象因素对流感的影响以及基于人工智能 RF-Bi-LSTM 算法的预测:比较 COVID-19 时期与非 COVID-19 时期。","authors":"Hansong Zhu, Si Chen, Weixia Qin, Joldosh Aynur, Yuyan Chen, Xiaoying Wang, Kaizhi Chen, Zhonghang Xie, Lingfang Li, Yu Liu, Guangmin Chen, Jianming Ou, Kuicheng Zheng","doi":"10.1186/s12879-024-09750-x","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>At different times, public health faces various challenges and the degree of intervention measures varies. The research on the impact and prediction of meteorology factors on influenza is increasing gradually, however, there is currently no evidence on whether its research results are affected by different periods. This study aims to provide limited evidence to reveal this issue.</p><p><strong>Methods: </strong>Daily data on influencing factors and influenza in Xiamen were divided into three parts: overall period (phase AB), non-COVID-19 epidemic period (phase A), and COVID-19 epidemic period (phase B). The association between influencing factors and influenza was analysed using generalized additive models (GAMs). The excess risk (ER) was used to represent the percentage change in influenza as the interquartile interval (IQR) of meteorology factors increases. The 7-day average daily influenza cases were predicted using the combination of bi-directional long short memory (Bi-LSTM) and random forest (RF) through multi-step rolling input of the daily multifactor values of the previous 7-day.</p><p><strong>Results: </strong>In periods A and AB, air temperature below 22 °C was a risk factor for influenza. However, in phase B, temperature showed a U-shaped effect on it. Relative humidity had a more significant cumulative effect on influenza in phase AB than in phase A (peak: accumulate 14d, AB: ER = 281.54, 95% CI = 245.47 ~ 321.37; A: ER = 120.48, 95% CI = 100.37 ~ 142.60). Compared to other age groups, children aged 4-12 were more affected by pressure, precipitation, sunshine, and day light, while those aged ≥ 13 were more affected by the accumulation of humidity over multiple days. The accuracy of predicting influenza was highest in phase A and lowest in phase B.</p><p><strong>Conclusions: </strong>The varying degrees of intervention measures adopted during different phases led to significant differences in the impact of meteorology factors on influenza and in the influenza prediction. In association studies of respiratory infectious diseases, especially influenza, and environmental factors, it is advisable to exclude periods with more external interventions to reduce interference with environmental factors and influenza related research, or to refine the model to accommodate the alterations brought about by intervention measures. In addition, the RF-Bi-LSTM model has good predictive performance for influenza.</p>","PeriodicalId":8981,"journal":{"name":"BMC Infectious Diseases","volume":"24 1","pages":"878"},"PeriodicalIF":3.4000,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11360838/pdf/","citationCount":"0","resultStr":"{\"title\":\"Study on the impact of meteorological factors on influenza in different periods and prediction based on artificial intelligence RF-Bi-LSTM algorithm: to compare the COVID-19 period with the non-COVID-19 period.\",\"authors\":\"Hansong Zhu, Si Chen, Weixia Qin, Joldosh Aynur, Yuyan Chen, Xiaoying Wang, Kaizhi Chen, Zhonghang Xie, Lingfang Li, Yu Liu, Guangmin Chen, Jianming Ou, Kuicheng Zheng\",\"doi\":\"10.1186/s12879-024-09750-x\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objective: </strong>At different times, public health faces various challenges and the degree of intervention measures varies. The research on the impact and prediction of meteorology factors on influenza is increasing gradually, however, there is currently no evidence on whether its research results are affected by different periods. This study aims to provide limited evidence to reveal this issue.</p><p><strong>Methods: </strong>Daily data on influencing factors and influenza in Xiamen were divided into three parts: overall period (phase AB), non-COVID-19 epidemic period (phase A), and COVID-19 epidemic period (phase B). The association between influencing factors and influenza was analysed using generalized additive models (GAMs). The excess risk (ER) was used to represent the percentage change in influenza as the interquartile interval (IQR) of meteorology factors increases. The 7-day average daily influenza cases were predicted using the combination of bi-directional long short memory (Bi-LSTM) and random forest (RF) through multi-step rolling input of the daily multifactor values of the previous 7-day.</p><p><strong>Results: </strong>In periods A and AB, air temperature below 22 °C was a risk factor for influenza. However, in phase B, temperature showed a U-shaped effect on it. Relative humidity had a more significant cumulative effect on influenza in phase AB than in phase A (peak: accumulate 14d, AB: ER = 281.54, 95% CI = 245.47 ~ 321.37; A: ER = 120.48, 95% CI = 100.37 ~ 142.60). Compared to other age groups, children aged 4-12 were more affected by pressure, precipitation, sunshine, and day light, while those aged ≥ 13 were more affected by the accumulation of humidity over multiple days. The accuracy of predicting influenza was highest in phase A and lowest in phase B.</p><p><strong>Conclusions: </strong>The varying degrees of intervention measures adopted during different phases led to significant differences in the impact of meteorology factors on influenza and in the influenza prediction. In association studies of respiratory infectious diseases, especially influenza, and environmental factors, it is advisable to exclude periods with more external interventions to reduce interference with environmental factors and influenza related research, or to refine the model to accommodate the alterations brought about by intervention measures. In addition, the RF-Bi-LSTM model has good predictive performance for influenza.</p>\",\"PeriodicalId\":8981,\"journal\":{\"name\":\"BMC Infectious Diseases\",\"volume\":\"24 1\",\"pages\":\"878\"},\"PeriodicalIF\":3.4000,\"publicationDate\":\"2024-08-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11360838/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"BMC Infectious Diseases\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1186/s12879-024-09750-x\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"INFECTIOUS DISEASES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"BMC Infectious Diseases","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s12879-024-09750-x","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"INFECTIOUS DISEASES","Score":null,"Total":0}
引用次数: 0
摘要
目的:在不同时期,公共卫生面临各种挑战,干预措施的程度也不尽相同。气象因素对流感影响和预测的研究逐渐增多,但其研究成果是否会受到不同时期的影响,目前尚无证据。本研究旨在为揭示这一问题提供有限的证据:方法:将厦门市流感影响因素的每日数据分为三个部分:总体期(AB阶段)、非COVID-19流行期(A阶段)和COVID-19流行期(B阶段)。采用广义相加模型(GAMs)分析影响因素与流感之间的关系。超额风险(ER)用来表示随着气象因素的四分位数间隔(IQR)的增加,流感的百分比变化。使用双向长短记忆(Bi-LSTM)和随机森林(RF)相结合的方法,通过多步滚动输入前 7 天的日多因素值,预测 7 天的日平均流感病例:在 A 和 AB 阶段,气温低于 22 ℃ 是流感的风险因素。然而,在 B 阶段,气温对流感的影响呈 U 型。相对湿度对流感的累积影响在 AB 阶段比 A 阶段更明显(高峰期:累积 14 天,AB:ER = 281.54,95% CI = 245.47 ~ 321.37;A:ER = 120.48,95% CI = 100.37 ~ 142.60)。与其他年龄组相比,4-12 岁儿童受气压、降水、日照和日光的影响更大,而年龄≥ 13 岁的儿童受多日湿度累积的影响更大。A 阶段预测流感的准确率最高,B 阶段最低:结论:在不同阶段采取不同程度的干预措施,导致气象因素对流感的影响和流感预测的显著差异。在呼吸道传染病(尤其是流感)与环境因素的关联研究中,最好排除外部干预措施较多的时期,以减少对环境因素和流感相关研究的干扰,或完善模型以适应干预措施带来的变化。此外,RF-Bi-LSTM 模型对流感具有良好的预测性能。
Study on the impact of meteorological factors on influenza in different periods and prediction based on artificial intelligence RF-Bi-LSTM algorithm: to compare the COVID-19 period with the non-COVID-19 period.
Objective: At different times, public health faces various challenges and the degree of intervention measures varies. The research on the impact and prediction of meteorology factors on influenza is increasing gradually, however, there is currently no evidence on whether its research results are affected by different periods. This study aims to provide limited evidence to reveal this issue.
Methods: Daily data on influencing factors and influenza in Xiamen were divided into three parts: overall period (phase AB), non-COVID-19 epidemic period (phase A), and COVID-19 epidemic period (phase B). The association between influencing factors and influenza was analysed using generalized additive models (GAMs). The excess risk (ER) was used to represent the percentage change in influenza as the interquartile interval (IQR) of meteorology factors increases. The 7-day average daily influenza cases were predicted using the combination of bi-directional long short memory (Bi-LSTM) and random forest (RF) through multi-step rolling input of the daily multifactor values of the previous 7-day.
Results: In periods A and AB, air temperature below 22 °C was a risk factor for influenza. However, in phase B, temperature showed a U-shaped effect on it. Relative humidity had a more significant cumulative effect on influenza in phase AB than in phase A (peak: accumulate 14d, AB: ER = 281.54, 95% CI = 245.47 ~ 321.37; A: ER = 120.48, 95% CI = 100.37 ~ 142.60). Compared to other age groups, children aged 4-12 were more affected by pressure, precipitation, sunshine, and day light, while those aged ≥ 13 were more affected by the accumulation of humidity over multiple days. The accuracy of predicting influenza was highest in phase A and lowest in phase B.
Conclusions: The varying degrees of intervention measures adopted during different phases led to significant differences in the impact of meteorology factors on influenza and in the influenza prediction. In association studies of respiratory infectious diseases, especially influenza, and environmental factors, it is advisable to exclude periods with more external interventions to reduce interference with environmental factors and influenza related research, or to refine the model to accommodate the alterations brought about by intervention measures. In addition, the RF-Bi-LSTM model has good predictive performance for influenza.
期刊介绍:
BMC Infectious Diseases is an open access, peer-reviewed journal that considers articles on all aspects of the prevention, diagnosis and management of infectious and sexually transmitted diseases in humans, as well as related molecular genetics, pathophysiology, and epidemiology.