ArticleIranian journal of veterinary research2022
Temporal trends and prediction of bovine tuberculosis: a time series analysis in the North-East of Iran.
Article in Iranian journal of veterinary research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed, 2 citations in OpenAlex.
- Measuring and Forecasting the Healthy Eating Index in Iran: 1991-2027.International journal of preventive medicine · 2025Article
- The effect of PhytopajAvicenna journal of phytomedicineArticle
- Modeling Time Series of Mortality Cases Due to Traffic and Non-Traffic Accidents in Northwestern Iran from 2013 to 2022.Iranian journal of nursing and midwifery researchArticle
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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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Registered trials
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