Document Type : Original Articles
Authors
1
Department of Biostatistics and Epidemiology, Faculty of Medicine, Urmia University of Medical Sciences, Urmia, Iran
2
Department of Public Health, Shoushtar Faculty of Medical Science, Shoushtar, Iran
3
MSc in Epidemiology, Department of Epidemiology & Biostatics, Arnold School of Public Health, University of South Carolina, Columbia.
4
Department of Prevention Communicable Diseases, University of Medical Sciences.
10.22092/ari.2024.364621.3000
Abstract
Snakebite incidents are a serious concern. approximately 5.4 million snakebite incidents occur worldwide annually, resulting in 125,000 deaths. This study focused on epidemiological surveys and modeling the time series data of snakebites in Shoushtar City from 2017 to 2022. This study recorded data on 396 individuals who experienced snakebite incidents during the research period. Time series analysis in the medical field involves studying and analyzing data collected over time to identify patterns, trends, and relationships. Time series analysis and appropriate models were used to forecast the trend of cases for future months using BOX-Jenkins models. Findings revealed that the majority of snakebites occurred in men and rural areas. The trend remained constant until the end of 2019, and from the last months of 2019 to the end of 2020, it exhibited an increasing trend (during the peak of the COVID-19 pandemic). The data had a seasonal trend, with the highest occurrences in hot seasons and the lowest occurrences in cold seasons. The age groups of 25-44 years and 10-24 years constituted the largest portion of the victims. The most frequently affected body parts were the foot (58.8%) and hand (38.8%). The majority of individuals sought treatment without delay. The best-fitting model for the available data was a seasonal ARIMA model in the form of ARIMA (0,0,0) (1,0,1)12. Forecasting was carried out for the next 6 months using the selected model, suggesting a decrease in snakebite cases compared to previous periods. Overall, time series analysis in the medical field is crucial in improving patient care, enhancing public health strategies, and advancing medical research. It can also aid in resource allocation and healthcare planning.
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