在COVID-19大流行期间模拟预防行为,使用贝叶斯信念网络和保护动机理论
Brent Vizanko1, Leonid Kadinski2, Christopher Cummings3,4,5
1Civil, Construction, and Environmental Engineering, North Carolina State University, Raleigh, North Carolina, USA.
概括
保护动机理论 (PMT) 有效地预测了COVID-19预防行为. 信任和个人经验对采取保护措施有很大影响,验证了公共卫生策略的理论.
科学领域:
- 公共卫生 公共卫生
- 行为科学 行为科学
- 流行病学 流行病学
背景情况:
- 由于COVID-19的传播需要有效的预防行为.
- 保护动机理论 (PMT) 认为风险和应对感知驱动行为采用.
研究的目的:
- 测试PMT在预测COVID-19预防行为中的适用性.
- 确定在疫情期间采取预防措施的关键预测因素.
主要方法:
- 在调查数据上训练的贝叶斯信念网络 (BBN) 模型.
- 采用数据驱动和基于专家知识 (PMT构造) 的BBN建模方法.
主要成果:
- 信任和之前的COVID-19经验成为预防行为采用的重要预测因素.
- 基于PMT的模型证实了威胁/应对评估和保护行动之间的联系.
- 数据驱动和基于PMT的模型显示了可比的预测性能.
结论:
- 保护动机理论是理解和预测COVID-19预防行为的有效框架.
- 研究结果为流行病期间的公共卫生政策制定提供了见解,特别是在社交距离方面.
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