使用修改后的SIS模型对累积感染病例的跳动调整预测
Rashi Mohta1, Sravya Prathapani1, Palash Ghosh1,2,3
1Department of Mathematics, Indian Institute of Technology Guwahati, Guwahati, Assam India.
Annals of data science
|April 16, 2024
概括
本研究介绍了一种算法,用于识别和调整印度异常的COVID-19病例数据. 这提高了流行病学模型的准确性,以更好地管理医疗保健资源.
科学领域:
- 流行病学 流行病学
- 数据科学数据科学数据科学
- 公共卫生 公共卫生
背景情况:
- 准确的COVID-19病例预测对于印度的医疗保健资源管理至关重要.
- 流行病学模型对于疫情控制至关重要,但依赖于准确的历史数据.
- 不稳定的每日病例数量可以显著降低这些模型的预测准确性.
研究的目的:
- 开发一种自动化算法,用于识别异常的每日COVID-19病例数据 (跳跃和下降).
- 调整这些异常数据点以提高流行病学模型的准确性.
- 为了提高印度累积COVID-19感染病例的预测.
主要方法:
- 提出了一种算法,可以在日常COVID-19病例数据中自动检测异常的"跳跃"和"掉落"日.
- 根据总体趋势,根据异常天的每日感染病例数量进行调整.
- 修改了培训数据以这些调整后的观察结果.
- 使用修改后的易受感染易受感染 (SIS) 模型应用算法.
主要成果:
- 该算法成功识别了COVID-19病例报告中的异常数据点.
- 通过纠正异常计数来调整训练数据,从而提高了预测准确度.
- 在数据修改后,修改后的SIS模型显示了更好的预测性能.
结论:
- 自动识别和调整异常的COVID-19病例数据显著提高了流行病学模型的准确性.
- 这种方法为更可靠地预测传染病趋势提供了有价值的工具.
- 提高预测准确度有助于在流行病期间更好地规划和管理医疗保健资源.
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
01:11Residuals 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
01:12Statistical Software for Data Analysis and Clinical Trials
546
Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
546
01:09Censoring Survival Data
88
Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
88
01:25Interpreting Run Charts
100
Run charts, essentially line graphs plotted over time, serve as fundamental yet effective tools for process analysis. They chronicle data sequentially, facilitating the identification of trends, shifts, or cyclical movements. This graphical representation is instrumental in determining whether a process is stable or exhibits signs of potential instability indicative of special cause variation. In the healthcare domain, run charts depict infection rates over time, enabling hospitals to monitor...
100