人工智能模型揭示了影响巴西北部子主义的因素
Thais de Andrade Moura1, Andrés A Ojanguren-Affilastro2, Mahmood Sasa3
1Laboratório de Pesquisas Integrativas em Biodiversidade (PIBi-Lab), Departamento de Biologia, Universidade Federal de Sergipe, São Cristóvão, Brazil.
概括
子刺是巴西北部日益严重的公共卫生问题,由气候变化和土地利用驱动. 人工智能模型预测子症的发生率,强调气候和物种分布是预防的关键因素.
科学领域:
- 环境科学 环境科学
- 公共卫生 公共卫生
- 毒理学 毒理学 毒理学
背景情况:
- 子刺是一个重大的公共卫生问题,特别是在热带地区.
- 在过去的十年里,巴西北部的子伤病例和死亡人数显著增加.
- 气候变化和土地利用变化被怀疑会影响医学上重要的子物种的分布和事故发生率.
研究的目的:
- 预测巴西北部子病的发生率.
- 识别和量化子刺发生率的关键环境和人口预测指标的重要性.
- 将物种分布模型 (SDM) 与人工智能 (AI) 整合起来,以提高预测准确度.
主要方法:
- 开发了三种医学上重要的子物种的物种分布模型 (SDMs):Tityus obscurus,T. metuendus和T. silvestris.
- 使用生物气候数据和土地利用信息作为预测变量.
- 采用XGBoost人工智能算法来模拟子症发病率和沙普利方法来评估预测因素的重要性.
主要成果:
- 预测模型表现出强的性能,平均绝对误差 (MAE) 为7.17,根平均平方误差 (RMSE) 为10.62.
- 气候因素被确定为子症发病率的主要决定因素.
- 黑暗的Tityus和T.silvestris的分布,牧场地区和农村人口密度也是重要的因素.
结论:
- 整合SDM和AI技术为预测子症发病率提供了一种有效的方法.
- 这些发现支持制定针对子刺的有针对性的预防和管理策略.
- 了解环境因素和物种分布的相互作用对于减轻与子中毒相关的公共卫生风险至关重要.
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