tetano
Editor, Senior Moderator
BMC Infect Dis
. 2024 Aug 28;24(1):878.
doi: 10.1186/s12879-024-09750-x. 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
Hansong Zhu[SUP] #[/SUP][SUP] 1 2 [/SUP], Si Chen[SUP] #[/SUP][SUP] 3 4 5 [/SUP], Weixia Qin[SUP] #[/SUP][SUP] 6 [/SUP], Joldosh Aynur[SUP] #[/SUP][SUP] 7 [/SUP], Yuyan Chen[SUP] #[/SUP][SUP] 8 [/SUP], Xiaoying Wang[SUP] #[/SUP][SUP] 7 [/SUP], Kaizhi Chen[SUP] 9 [/SUP], Zhonghang Xie[SUP] 10 11 [/SUP], Lingfang Li[SUP] 10 [/SUP], Yu Liu[SUP] 12 [/SUP], Guangmin Chen[SUP] 13 14 [/SUP], Jianming Ou[SUP] 15 16 [/SUP], Kuicheng Zheng[SUP] 17 18 [/SUP]
Affiliations
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.
Keywords: Bi-LSTM; COVID-19; Influenza; Meteorological; Random forest (RF).
. 2024 Aug 28;24(1):878.
doi: 10.1186/s12879-024-09750-x. 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
Hansong Zhu[SUP] #[/SUP][SUP] 1 2 [/SUP], Si Chen[SUP] #[/SUP][SUP] 3 4 5 [/SUP], Weixia Qin[SUP] #[/SUP][SUP] 6 [/SUP], Joldosh Aynur[SUP] #[/SUP][SUP] 7 [/SUP], Yuyan Chen[SUP] #[/SUP][SUP] 8 [/SUP], Xiaoying Wang[SUP] #[/SUP][SUP] 7 [/SUP], Kaizhi Chen[SUP] 9 [/SUP], Zhonghang Xie[SUP] 10 11 [/SUP], Lingfang Li[SUP] 10 [/SUP], Yu Liu[SUP] 12 [/SUP], Guangmin Chen[SUP] 13 14 [/SUP], Jianming Ou[SUP] 15 16 [/SUP], Kuicheng Zheng[SUP] 17 18 [/SUP]
Affiliations
- PMID: 39198754
- PMCID: PMC11360838
- DOI: 10.1186/s12879-024-09750-x
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.
Keywords: Bi-LSTM; COVID-19; Influenza; Meteorological; Random forest (RF).