Correlation between influenza-like illness cases and atmospheric environmental factors in Hohhot city and the prediction analysis

Objective To explore the correlations between influenza-like illness cases and meteorological and air quality factors, and to construct a prediction model for influenza-like illness cases with time series analysis , so as to explore the technology of early warning and prediction for influenza. Methods The surveillance data of influenza-like illness cases from 2014 to 2017, as well as the meteorological data and air quality factors data during the same period in Hohhot were collected. The correlations between the correlations between influenza-like illness cases and meteorological and air quality factors were analyzed. The seasonal autoregressive integrated moving average (SARIMA) model was established for influenza-like illness cases in the region. The goodness of fit and prediction accuracy of the models before and after the introduction of external factors were compared. Results The number of influenza-like illness cases in Hohhot showed obvious seasonality and periodicity features. The weekly number of influenza-like illness cases correlated to one week lag of weekly average temperature (rs=0.550, P<0.01), and to one week lag of weekly average humidity (rs=-0.304, P<0.01). The one week lag of weekly average temperature was included in the SARIMA (1, 0, 1) (0, 1, 1)52 prediction model for the weekly number of influenza-like illness cases. Both goodness of fit and prediction accuracy have been improved. Conclusions Partial correlation analysis of influenza-like illness cases and air quality factors showed no statistical significance.The prevalence of influenza-like illness cases was related to the weekly average temperature and humidity. The SARIMA model contained weekly average temperature could be used as a technical method for short-term prediction of influenza epidemic. Key words: Influenza-like illness cases; Meteorological factors; Air quality factors; Seasonal autoregressive integrated moving average model