Identifying the Dynamic Pattern and Influencing Factors of Influenza in Northwest China from 2013 to 2020, Based on Dynamic Regression Model and Wavelet Analysis

Abstract

Background: Influenza remains a serious global public health problem and a substantial economic burden. The dynamic pattern of influenza differs considerably among geographic and climatological areas, however, the factors underlying these differences are still uncertain. The aim of this paper is to characterize the dynamic pattern of influenza and its potential influencing factors in Northwest China. Methods: Influenza cases in Ningxia China from Nov. 2013 to Jun. 2020 were served as influenza proxy. Firstly, the baseline seasonal ARIMA model of influenza cases and seasonal pattern were analyzed. Then, the dynamic regression model was used to identifying the potential influencing factors of influenza. In addition, the wavelet analysis was further used to explore the coherence between influenza cases and these significant influencing factors.Results: The high risk periods of influenza in Ningxia presented a winter cycle outbreaks pattern and the fastigium came in January. The seasonal ARIMA(0,0,1)(1,1,0)12 was the optimal baseline forecast model. The dynamic regression models and wavelet analysis indicated that PM2.5 and public awareness are significantly positively associated with influenza, as well as minimum temperature is negatively associated. Conclusion: Meteorological (minimum temperature), pollution (PM2.5) and social (public awareness) factors may significantly associated with influenza in Northwest China. Decreasing PM2.5 concentration or increasing the public awareness prior to the fastigium of influenza may be the serviceable methods to reduce the disease risk of influenza, which have an important implication for policy-makers to choose an optimal time for influenza prevention campaign.

Identification of precipitation zones in Pakistan by global wavelet power spectrum

“…The use of Global Wavelet Power Spectrum has allowed us to hydrologically regionalized the Indus basin of Pakistan and examine the effect of precipitation of five different regions [16][17][18][19][20]. In the above figure, it is mentioned clearly that region 1 and region 5 are strong frequency pattern and the remaining (regions 2, 3, 4) are weak frequency patterns.…”