Epidemiology and Time Series Analysis of Snakebite Incidence in Southwestern Iran (Shoushtar) 2017-2022

2017-2022年伊朗西南部(舒什塔尔)蛇咬伤发病率的流行病学和时间序列分析

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Abstract

Snakebite incidents represent a significant public health concern. On an annual basis, approximately 5.4 million snakebite incidents occur worldwide, resulting in 125,000 deaths. The present study centered on epidemiological surveys and the modeling of time series data pertaining to snakebites in Shoushtar City from 2017 to 2022. The present study documented data on 396 individuals who experienced snakebite incidents during the research period. In the medical field, time series analysis entails the study and analysis of data collected over time to identify patterns, trends, and relationships. The forecasting of future trends in case counts was accomplished through the implementation of time series analysis and the employment of suitable models, with the utilization of Box-Jenkins models being a key element of this approach. The findings indicated that the majority of snake bite incidents occurred among males and in 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 pandemic). The data exhibited a seasonal trend, with the highest occurrences in hot seasons and the lowest occurrences in cold seasons. The demographic groups with the highest number of casualties were those between the ages of 25 and 44 and between 10 and 24 years of age. The body parts most frequently affected by the condition were the foot (58.8%) and the hand (38.8%). The majority of individuals sought treatment without delay. The most suitable model for the available data was determined to be a seasonal ARIMA model in the form of ARIMA (0,0,0) (1,0,1)12. The forecasting exercise, conducted over the span of the subsequent six months, employed the selected model. The model's projections indicated a decline in snakebite cases when compared to earlier periods. In general, the application of time series analysis in the medical field is of paramount importance in the context of improving patient care, enhancing public health strategies, and advancing medical research. The utilization of these tools can facilitate effective resource allocation and healthcare planning.

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