Objective:To determine the temporal patterns of cumulative incidence of brucellosis using autoregressive integrated moving average models.Methods:This cross-sectional study employed yearly and monthly data of 1117 lab...Objective:To determine the temporal patterns of cumulative incidence of brucellosis using autoregressive integrated moving average models.Methods:This cross-sectional study employed yearly and monthly data of 1117 laboratory-confirmed human brucellosis cases from January 2013 to December 2018 using the Yazd brucellosis national surveillance system.The monthly incidences constructed a timeseries model.The trend of cumulative incidence was perceived by tracing a line plot,which displayed a seasonal trend with periodicity.Thus,the ARIMA models were selected.Thereafter,Akaike information criteria(AIC)and Bayesian information criterion(BIC)values among different models indicated a preferable model from models which were expanded by diverse lags[(3,0,3),(2,0,3),(3,0,2),(4,0,3)and(3,0,4)].Then,the achieved ARIMA model was applied to the forecasting cumulative incidence of monthly brucellosis incidences.All analyses were performed using Stata,version 11.2.Results:For the ARIMA(3,0,4)model,MAPE value was 56.20%with standard error 0.009–0.016,and white noise diagnostic check(Q=19.79,P=0.975)for the residuals of the selected model showed that the data were completely modelled.The monthly incidences that were fitted by the ARIMA(3,0,4)model,with AIC(25.7)and BIC(43.35)with a similar pattern of actual cases from 2013 to 2018 and forecasting incidences from January 2019 to December 2019 were,respectively,0.50,0.44,0.45,0.49,0.55,0.58,0.56,0.51,0.46,0.44,0.45 and 0.49 per 100000 people.Conclusions:In summary,the study showed that the ARIMA(3,0,4)model can be applied to forecast human brucellosis patterns in Yazd province,supplementing present surveillance systems,and may be better for health policy-makers and planners.展开更多
文摘Objective:To determine the temporal patterns of cumulative incidence of brucellosis using autoregressive integrated moving average models.Methods:This cross-sectional study employed yearly and monthly data of 1117 laboratory-confirmed human brucellosis cases from January 2013 to December 2018 using the Yazd brucellosis national surveillance system.The monthly incidences constructed a timeseries model.The trend of cumulative incidence was perceived by tracing a line plot,which displayed a seasonal trend with periodicity.Thus,the ARIMA models were selected.Thereafter,Akaike information criteria(AIC)and Bayesian information criterion(BIC)values among different models indicated a preferable model from models which were expanded by diverse lags[(3,0,3),(2,0,3),(3,0,2),(4,0,3)and(3,0,4)].Then,the achieved ARIMA model was applied to the forecasting cumulative incidence of monthly brucellosis incidences.All analyses were performed using Stata,version 11.2.Results:For the ARIMA(3,0,4)model,MAPE value was 56.20%with standard error 0.009–0.016,and white noise diagnostic check(Q=19.79,P=0.975)for the residuals of the selected model showed that the data were completely modelled.The monthly incidences that were fitted by the ARIMA(3,0,4)model,with AIC(25.7)and BIC(43.35)with a similar pattern of actual cases from 2013 to 2018 and forecasting incidences from January 2019 to December 2019 were,respectively,0.50,0.44,0.45,0.49,0.55,0.58,0.56,0.51,0.46,0.44,0.45 and 0.49 per 100000 people.Conclusions:In summary,the study showed that the ARIMA(3,0,4)model can be applied to forecast human brucellosis patterns in Yazd province,supplementing present surveillance systems,and may be better for health policy-makers and planners.
