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Study on the relationship between the incidence of influenza and climate indicators and the prediction of influenza incidence

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Abstract

In recent 2 years, the incidence of influenza showed a slight upward trend in Guangxi; therefore, some joint actions should be done to help preventing and controlling this disease. The factors analysis of affecting influenza and early prediction of influenza incidence may help policy-making so as to take effective measures to prevent and control influenza. In this study, we used the cross correlation function (CCF) to analyze the effect of climate indicators on influenza incidence, ARIMA and ARIMAX (autoregressive integrated moving average model with exogenous input variables) model methods to do predictive analysis of influenza incidence. The results of CCF analysis showed that climate indicators (PM2.5, PM10, SO2, CO, NO2, O3, average temperature, maximum temperature, minimum temperature, average relative humidity, and sunshine duration) had significant effects on the incidence of influenza. People need to take good precautions in the days of severe air pollution and keep warm in cold weather to prevent influenza. We found that the ARIMAX (1,0,1)(0,0,1)12 with NO2 model has good predictive performance, which can be used to predict the influenza incidence in Guangxi, and the predicted incidence may be useful in developing early warning systems and providing important evidence for influenza control policy-making and public health intervention.

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Funding

This work was supported by the Natural science funding of Xinjiang Uygur Autonomous Region (Grant No.2019D01C206), China.

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Authors and Affiliations

  1. College of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, 830011, People’s Republic of China

    Yanling Zheng, Kai Wang, Liping Zhang & Lei Wang

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  1. Yanling Zheng
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  2. Kai Wang
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  3. Liping Zhang
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Correspondence to Yanling Zheng.

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Zheng, Y., Wang, K., Zhang, L. et al. Study on the relationship between the incidence of influenza and climate indicators and the prediction of influenza incidence. Environ Sci Pollut Res 28, 473–481 (2021). https://doi.org/10.1007/s11356-020-10523-7

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  • DOI: https://doi.org/10.1007/s11356-020-10523-7

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