tetano
Editor, Senior Moderator
Epidemiol Infect. 2019 Dec 20;147:e325. doi: 10.1017/S0950268819002140. [h=1]Influenza activity prediction using meteorological factors in a warm temperate to subtropical transitional zone, Eastern China.[/h]
Liu W[SUP]1[/SUP], Dai Q[SUP]1[/SUP], Bao J[SUP]2[/SUP], Shen W[SUP]1[/SUP], Wu Y[SUP]1[/SUP], Shi Y[SUP]1[/SUP], Xu K[SUP]1[/SUP], Hu J[SUP]1[/SUP], Bao C[SUP]1[/SUP], Huo X[SUP]1[/SUP].
[h=3]Author information[/h] 1 Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China. 2 Jiangsu Meteorological Service Center, Nanjing, China.
[h=3]Abstract[/h] 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.
[h=4]KEYWORDS:[/h] Forecast; influenza activity; meteorological factor; random forest model
PMID: 31858924 DOI: 10.1017/S0950268819002140
Liu W[SUP]1[/SUP], Dai Q[SUP]1[/SUP], Bao J[SUP]2[/SUP], Shen W[SUP]1[/SUP], Wu Y[SUP]1[/SUP], Shi Y[SUP]1[/SUP], Xu K[SUP]1[/SUP], Hu J[SUP]1[/SUP], Bao C[SUP]1[/SUP], Huo X[SUP]1[/SUP].
[h=3]Author information[/h] 1 Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China. 2 Jiangsu Meteorological Service Center, Nanjing, China.
[h=3]Abstract[/h] 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.
[h=4]KEYWORDS:[/h] Forecast; influenza activity; meteorological factor; random forest model
PMID: 31858924 DOI: 10.1017/S0950268819002140