使用自组织地图来预测和控制自然灾害和流行病
Raymond Moodley1, Francisco Chiclana1,2, Fabio Caraffini1
1Institute of Artificial Itelligence, School of Computer Science and Informatics De Montfort University Leicester UK.
概括
这项研究引入了一种新的流行病和自然灾害的预测和制模型. 该模型使用选择性锁定和防护带来降低感染率的峰值,同时保持不受影响地区的经济活动.
科学领域:
- 流行病学 流行病学
- 公共卫生 公共卫生
- 数据科学数据科学数据科学
背景情况:
- 随着COVID-19的流行,人们越来越需要有效的预测和制策略.
- 目前的应对措施主要集中在疾病传播缓解和疫苗接种上,未来的准备工作至关重要.
- 现有的模型可能缺乏适应各种场景的灵活性,例如流行病和自然灾害.
研究的目的:
- 为未来的流行病和自然灾害提出一个新的预测和制模型.
- 开发一个灵活,用户友好的数据分析工具,用于政府和组织的决策.
- 为了证明该模型在降低感染率的有效性,同时保持经济活动.
主要方法:
- 开发一个预测和制模型,整合选择性锁定和保护.
- 基于自组织地图 (SOMs) 的数据分析模型的实施,用于决策支持.
- 使用大不列颠 (GB) 的公开数据进行比较测试.
主要成果:
- 拟议的战略有效地降低了感染率的峰值.
- 它使得很大一部分地区 (高达25%的英国议会选区) 能够保持经济活动.
- 该模型有助于快速封闭,同时允许不受影响的区域正常运行.
结论:
- 拟议的模型为管理未来的流行病和自然灾害提供了一个可行的策略.
- 选择性制措施与数据驱动的决策相结合,可以平衡公共卫生和经济稳定.
- 基于自组织地图的方法为有效的危机管理提供了一个可访问的工具.
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