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Comparative Study
. 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 #  1   2 , Si Chen #  3   4   5 , Weixia Qin #  6 , Joldosh Aynur #  7 , Yuyan Chen #  8 , Xiaoying Wang #  7 , Kaizhi Chen  9 , Zhonghang Xie  10   11 , Lingfang Li  10 , Yu Liu  12 , Guangmin Chen  13   14 , Jianming Ou  15   16 , Kuicheng Zheng  17   18
Affiliations
Comparative Study

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 et al. BMC Infect Dis. .

Abstract

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).

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
The geographical location of Xiamen city
Fig. 2
Fig. 2
A brief operation process of the RF in this study
Fig. 3
Fig. 3
The single cell of LSTM and Bi-LSTM
Fig. 4
Fig. 4
The RF-Bi-LSTM process
Fig. 5
Fig. 5
Violin cloud and rain box chart of influenza and meteorology factors (Note: The influenza in this figure include two images, a and b, where a is the main image and b is an enlargement of the box diagram. Since the holiday and allergen in this study are dummy variables, this figure is not shown)
Fig. 6
Fig. 6
Time series of influenza and influencing factors (Note: The units of influenza, pressure, pres-difference, humidity, precipitation, temperature, temp-difference, wind, sunshine and day light are case, hPa, hPa, %, mm, °C, °C, m/s, h and h, respectively. Holiday and allergen are dummy variables in this study.)
Fig. 7
Fig. 7
Heatmap of Spearman correlation analysis of influenza and influencing factors
Fig. 8
Fig. 8
Spearman correlation analysis of annual influenza incidence rate and vaccination rate
Fig. 9
Fig. 9
Correlation between influencing factors and influenza based on GAM analysis
Fig. 10
Fig. 10
The lag and cumulative effects of the association between meteorology factors and influenza based on GAM analysis (Note: lag0-14 represents a single day lag of 0-14d, (3) represents a cumulative 3-day lag over multiple days, (7) represents a cumulative 7-day lag over multiple days, and (14) represents a cumulative 14-day lag over multiple days)
Fig. 11
Fig. 11
Evaluation indicators based on RF-Bi-LSTM predictions
Fig. 12
Fig. 12
Predicted true influenza values over three months based on RF-Bi-LSTM
See this image and copyright information in PMC

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