Arnab Narayan Patowary, Manash Pratim Barman


Predicting the incidence of tuberculosis (TB) plays an important role in planning health control strategies for the future, developing intervention programs and allocating resources. The techniques of time series analysis and forecasting have become a major tool in predicting different health issues. In this context, an attempt has been made to fit seasonal autoregressive integrated moving average (SARIMA) model to the historical data of quarterly TB detection rate in Dibrugarh district of India for the period of 2001-2011, a total of 44 data points. We investigated and found that ARIMA (0,0,0)×(1,1,0)4 model is suitable for the given data set.


, Seasonal Autoregressive Integrated Moving Average (SARIMA), Additive Decomposition, Q-Q plot, normwn.test

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