Hindsight2020:描述COVID-19科学文献中的不确定性
Kinga Dobolyi1, George P Sieniawski2, David Dobolyi3
1George Washington University, Department of Computer Science, Washington, DC, USA.
Disaster medicine and public health preparedness
|July 25, 2023
概括
了解疫情期间的科学进化证据至关重要. 这项研究分析了COVID-19的文献,发现早期的专家证据是稳定的,但特定的答案在2-6个月内稳定,为流行病政策提供了信息.
科学领域:
- 传染病流行病学 传染病流行病学
- 科学沟通科学沟通
- 数据科学数据科学数据科学
背景情况:
- 疫情需要快速科学出版,增加错误信息的风险.
- 在新出现的传染病 (EID) 事件中,方法论的严谨性和不断变化的证据评估至关重要.
- 了解证据演变的时间表对于及时准确的科学传播至关重要.
研究的目的:
- 调查早期科学证据如何以及何时出现和演变,以解决反复出现的病原体相关问题.
- 制定一个框架来描述随时间推移在EID研究中的不确定性.
- 分析COVID-19科学文献中的时间模式,以告知未来的流行病反应.
主要方法:
- 利用深度学习自然语言处理 (NLP) 在COVID-19科学文献中进行索赔匹配.
- 对比NLP衍生索赔与专家策划的证据集.
- 在不同COVID-19主题中分析了问答演变的时间模式.
主要成果:
- 确定了 COVID-19 问题的证据出现和稳定中的独特时间模式.
- 广泛的COVID-19问题得到了可靠的答案,但往往与一般出版趋势不一致.
- 特定的COVID-19主题在最初的2到6个月内显示出不稳定的答案.
结论:
- 对EID爆发的早期专家策划的证据往往是稳定的.
- 针对特定主题的答案不稳定,需要在流行病开始时谨慎制定政策.
- 描述不确定性的框架可以指导公共卫生官员在证据稳定时修订政策.
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