TY - DATA T1 - MOESM7 of Time series analysis of malaria in Afghanistan: using ARIMA models to predict future trends in incidence PY - 2016/11/22 AU - Mohammad Anwar AU - Joseph Lewnard AU - Sunil Parikh AU - Virginia Pitzer UR - https://springernature.figshare.com/articles/journal_contribution/MOESM7_of_Time_series_analysis_of_malaria_in_Afghanistan_using_ARIMA_models_to_predict_future_trends_in_incidence/4415486 DO - 10.6084/m9.figshare.c.3628256_D8.v1 L4 - https://ndownloader.figshare.com/files/7141904 KW - Malaria KW - Prediction KW - Afghanistan KW - Environment KW - Autoregressive model N2 - Additional file 7: Annex 2. Pairwise correlation between malaria ARIMA model residuals and external regressor residuals at different lags, after pre-whitening (removing trends and seasonality and fitting ARIMA models to each) (first table). In preliminary analyses, statistically significant correlation was observed between rain and humidity (r = 0.7032, p < 0.001); subsequently, humidity was dropped after it was found not to add meaningful information. Had we not performed pre-whitening, statistically significant correlations existed between malaria and other variables at every lag we analyzed. ER -