影响食物传播疾病的因素 用户生成的数据自我识别 - 明尼苏达州,2024年
Lynette L Krampf1, Craig W Hedberg2, Brett Hauber3
1Concordia University - Wisconsin, School of Education, 12800 N. Lake Shore Drive, Mequon, WI 53097, USA.
Journal of food protection
|September 4, 2025
概括
当有更多的人受到影响或列出症状时,人们更有可能在公开的食物传播事件中报告疾病. 这影响了使用公开数据的疫情早期检测.
科学领域:
- 公共卫生监督
- 行为科学
- 数据分析
背景情况:
- 食物传播疾病的爆发对公众健康构成重大风险.
- 快速发现疫情对于实施控制措施至关重要.
- 新兴的数据流,包括用户生成的内容和人工智能,为早期发现疫情提供了潜力.
研究的目的:
- 调查影响在食品传播事件中自我报告疾病的因素.
- 了解个体对不同爆发信息的反应.
- 评估用户生成的食品传播疾病监测数据的可靠性.
主要方法:
- 进行了基于图片的离散选择实验调查.
- 参与者提供了假设的食物传播疾病爆发场景.
- 情况在报告病人的数量,包括症状,FDA调查状况和呼吁采取行动方面有所不同.
主要成果:
- 人们更容易认为自己患有食物传播疾病.
- 当受影响人数较多 (8,500人) 或明确提到症状时,自我报告的几率显著增加.
- 所有测试属性 (患病人数,症状,FDA调查,呼吁采取行动) 都对自我报告产生了积极影响.
结论:
- 食品传播疾病的事件可能会引发自我报告,即使没有确定的爆发.
- 疾病程度和症状细节等因素对自我报告行为有很大影响.
- 这突显了在没有公共卫生当局确认的情况下使用新型数据流的潜在限制.
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