Temporal trends and prediction of bovine tuberculosis: a time series analysis in the North-East of Iran

Document Type : Full paper (Original article)

Authors

1 Department of Food Hygiene and Quality Control, Faculty of Veterinary Medicine, University of Tehran, Tehran, Iran; 2Department of Epidemiology, Faculty of Public Health, Mashhad University of Medical Science, Mashhad, Iran

2 Department of Food Hygiene and Quality Control, Faculty of Veterinary Medicine, University of Tehran, Tehran, Iran

3 Department of Epidemiology and Biostatistics, Faculty of Public Health, Tehran University of Medical Science, Tehran, Iran

4 Department of Epidemiology, Faculty of Public Health, Iran University of Medical Science, Tehran, Iran

5 Deputy of Bureau Health and Management of Animal Diseases, Veterinary Organization of Iran, Tehran, Iran

6 Department of Physiology, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran

Abstract

Background: Bovine tuberculosis (BTB) is a disease with high economic relevance. Aims: This study aimed to determine a fast alert surveillance system for bTB before the outbreak in the epidemic region of Iran. Methods: This cross-sectional study was conducted using the Auto-Regressive Integrated Moving Average (ARIMA) model for monthly bTB detections (reactors). These reactor cases result from the positive Tuberculin Purified Protein Derivative (PPD) test on cattle farms for the period between April 2007 and March 2019 in Razavi Khorasan province. Autocorrelation functions (ACF) and partial autocorrelation functions (PACF) plots were used to determine model parameters. The Akaike Information Criteria (AIC) were employed to select the best-fitted model. The root mean square error (RMSE) was applied for the evaluation of the models. Then, the best-fitted model was hired to predict the cases for 12 oncoming months. The data were analysed by STATA (ver. 14) software with a significant level at P≤0.05. Results: ARIMA (3, 0, 3) 12 was introduced as a recommended fitted model according to white noise residual test (Q=22.87 and P=0.98), lower AIC (541.85), and more precise model RMSE (1.50). However, the forecast values were more than the observed values. Conclusion: The application and interpretation of ARIMA models are straightforward, and may be used as immediate tools for monitoring systems. However, we proposed an Auto-Regressive Integrated Moving Average with Exogenous Input (ARIMAX) model with some measurable exotic factors such as economic fluctuations, climate changes, and pulmonary tuberculosis to introduce a more precise and accurate model for the fast alert surveillance system.

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References

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