Influenza activity prediction using meteorological factors in a warm temperate to subtropical transitional zone, Eastern China

• Published in: Epidemiology and infection Vol. 147; pp. 1 - 8
• Main Authors: Liu, Wendong, Dai, Qigang, Bao, Jing, Shen, Wenqi, Wu, Ying, Shi, Yingying, Xu, Ke, Hu, Jianli, Bao, Changjun, Huo, Xiang
• Format: Journal Article
• Language: English
• Published: England Cambridge University Press 01.01.2019
• Subjects: Accuracy; Atmospheric models; Betainfluenzavirus; China - epidemiology; Climate; Clinical Decision Rules; Dependent variables; Disease control; Environmental factors; Epidemics; Forecasting; Health promotion; Health surveillance; Hot Temperature; Humans; Influenza; Influenza A; Influenza A virus; Influenza, Human - epidemiology; Meteorological Concepts; Model accuracy; Model matching; Models, Biological; Original Paper; Performance evaluation; Prediction models; Public health; Public Health Surveillance; Regression Analysis; Regression models; Seasonal variations; Seasons; Time series; Tropical Climate; Variables; Viruses; China; meteorological factor; influenza activity; random forest model; Forecast
• ISSN: 0950-2688; 1469-4409; 1469-4409
• DOI: 10.1017/S0950268819002140

Influenza activity is subject to environmental factors. Accurate forecasting of influenza epidemics would permit timely and effective implementation of public health interventions, but it remains challenging. In this study, we aimed to develop random forest (RF) regression models including meterological factors to predict seasonal influenza activity in Jiangsu provine, China. Coefficient of determination (R2) and mean absolute percentage error (MAPE) were employed to evaluate the models' performance. Three RF models with optimum parameters were constructed to predict influenza like illness (ILI) activity, influenza A and B (Flu-A and Flu-B) positive rates in Jiangsu. The models for Flu-B and ILI presented excellent performance with MAPEs <10%. The predicted values of the Flu-A model also matched the real trend very well, although its MAPE reached to 19.49% in the test set. The lagged dependent variables were vital predictors in each model. Seasonality was more pronounced in the models for ILI and Flu-A. The modification effects of the meteorological factors and their lagged terms on the prediction accuracy differed across the three models, while temperature always played an important role. Notably, atmospheric pressure made a major contribution to ILI and Flu-B forecasting. In brief, RF models performed well in influenza activity prediction. Impacts of meteorological factors on the predictive models for influenza activity are type-specific.