پیش‌بینی فضایی-زمانی شیوع تب کریمه-کنگو در اردبیل با استفاده از مدل جنگل تصادفی و تحلیل تأثیر تغییرات اقلیمی (۲۰۱۵-۲۰۳۰)
کد: G-1875
نویسندگان: Azam Nasrollahi *, Robab Hosseinpour ℗
زمان بندی: زمان بندی نشده!
برچسب: سیاست گذاری، قانون گذاری و مدیریت سلامت در حوزه هوش مصنوعی
دانلود: دانلود پوستر
خلاصه مقاله:
خلاصه مقاله
Background and Aims: Infectious diseases, including Crimean-Congo Hemorrhagic Fever (CCHF), pose significant challenges to global public health. Climate change may influence the dynamics of these diseases, necessitating accurate computational tools for outbreak prediction. This study aims to develop a spatiotemporal machine learning framework to predict CCHF incidence at the county level in Ardabil, Iran, from 2015 to 2030, while assessing the impact of climate change on outbreak patterns. Method: The proposed research will employ the Random Forest model to capture nonlinear relationships and temporal lags (1 to 12 weeks). Epidemiological data (2019–2023), meteorological data (temperature, relative humidity, precipitation), and environmental data (normalized difference vegetation index, land cover) will be collected from local and global sources. Climate change scenarios (RCP4.5 and RCP8.5) will be analyzed to forecast changes in high-risk areas. Results: It is anticipated that variables such as temperature and relative humidity will be identified as key drivers of CCHF outbreaks. Hotspot analysis is expected to highlight areas like Meshginshahr and Parsabad as high-risk regions. The model is likely to provide high predictive accuracy, enabling the development of an early warning system for targeted interventions. Conclusion: This study will offer a scalable and effective framework for predicting climate-driven zoonotic diseases, with potential applications for public health policy in Ardabil and similar regions.
کلمات کلیدی
Crimean-Congo Hemorrhagic Fever, Random Forest,Climate Change
