人类判断预测COVID-19在英国的预测
Nikos I Bosse1,2, Sam Abbott1, Johannes Bracher3,4
1Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK.
Wellcome open research
|April 15, 2024
概括
人类对COVID-19病例和死亡的预测显示了与计算模型可比的预测性能. 将人类判断与流行病学建模相结合,为传染病预测提供了潜力,但面临着实际挑战.
科学领域:
- 流行病学 流行病学
- 计算建模计算建模
- 公共卫生 公共卫生
背景情况:
- 以前的研究表明,对COVID-19的人类判断预测与计算模型可比.
- 这项研究建立在德国和波兰的先前工作之上,探索一种新的综合方法.
研究的目的:
- 研究一种结合人类判断和流行病学建模用于COVID-19预测的联合方法.
- 将人类判断预测与计算模型的性能进行比较.
主要方法:
- 从人类预测者获得英国COVID-19病例和死亡的每周预测.
- 使用了两个接口:直接预测分布和有效复制数 (R) 的预测.
- 使用EpiNow2 R包进行基于模拟的预测,并使用加权区间得分得分.
主要成果:
- 在病例和死亡方面,人类预测人员的整体表现与欧洲预测中心的整体表现相似.
- 生殖数 (R) 预测与病例的直接预测相似,但对于死亡来说不那么准确.
- 专家对病例预测的校准比非专家更好,但对死亡预测没有.
结论:
- 人类判断和计算模型可以为COVID-19等传染病提供类似的质量预测.
- 预测评估指标和消费者需求影响预测质量的感知.
- 整合人类和计算预测为现实世界的应用提供了机遇和挑战.
相关概念视频
01:18Steps in Outbreak Investigation
125
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
125
01:03Prediction Intervals
2.3K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
2.3K
01:04Random Error
880
Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
880
01:11Residuals and Least-Squares Property
7.4K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
7.4K
01:08The Availability Heuristic
6.0K
A heuristic is a general problem-solving framework (Tversky & Kahneman, 1974). You can think of these as mental shortcuts that are used to solve problems. Different types of heuristics are used in different types of situations, and the impulse to use a heuristic occurs when one of five conditions is met (Pratkanis, 1989):
6.0K
01:25Statistical Methods for Analyzing Epidemiological Data
364
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
364