AI Article Synopsis
- Visceral leishmaniasis (VLH), also known as kala-azar, is a neglected disease that affects humans in over 50 countries, primarily in the Eastern Mediterranean and Northern America.
- This study aimed to analyze the patterns of VLH occurrences in Ardabil Province, Iran, from 2000 to 2019, using ARIMA time-series models to forecast future cases.
- The ARIMA (5, 0, 1) model was found to be the most effective for predictions, estimating 14 VLH cases in the next 24 months, which can aid public health planners in preemptively addressing potential outbreaks.
Article Abstract
Background: Visceral leishmaniasis in human (VLH) also known as kala-azar is a neglected disease of humans that mainly occurs in more than 50 countries mostly located in the Eastern Mediterranean and the Northern America.
Objective: The purpose of this study was to determine the temporal patterns and predict of occurrence of VL in Ardabil Province, in northwestern Iran using autoregressive integrated moving average (ARIMA) models.
Methods: This descriptive study employed yearly and monthly data of 602 cases of VLH in the province between January 2000 to December 2019, which was provided by the leishmaniasis national surveillance system. The monthly occurrences case constructed the ARIMA model of time-series model. The insignificance of the correlation in the lags of 12, 24 and 36 months, and Chi-square test showed the occurrence of VLH does not have a seasonal pattern. Eleven potential ARIMA models were examined for VLH cases. Finally, the best model was selected with the lower Akaike Information Criteria (AIC) and Bayesian information criterion (BIC) value. Then, the selected model was used to forecast frequency of monthly occurrences case. The forecasting precision was estimated by mean absolute percentage error (MAPE). Data analysis was performed using Stata14 and its package time series analysis.
Results: ARIMA (5, 0, 1) model with AIC (25.7) and BIC (43.35) was selected. The MAPE value was 26.89% and the portmanteau test for white noise was (Q = 23.02, = 0.98) for the residuals of the selected model showed that the data were fully modelled. The total cumulative VLH cases in the next 24 months' in Ardabil province predicted 14 cases (95% CI: 4-54 case).
Conclusion: The ARIMA (5, 0, 1) model can be a useful tool to predict VLH cases as early warning system and the results are helpful for policy makers and primary care physicians in the readiness of public health problems before the outbreak of the disease.
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|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7928107 | PMC |
| http://dx.doi.org/10.4103/jfmpc.jfmpc_1542_20 | DOI Listing |
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