Related Experiment Video
Updated: Jun 14, 2025
16:14
Trajectory Data Analyses for Pedestrian Space-time Activity Study
Published on: February 25, 2013
13.5K
A Big Data and FRAM-Based Model for Epidemic Risk Analysis of Infectious Diseases
Junhua Zhu1, Yue Zhuang1, Wenjing Li1
1School of Safety Science and Emergency Management, Wuhan University of Technology, Wuhan, People's Republic of China.
Risk Management and Healthcare Policy
|September 3, 2024
Summary
A new big data fusion method using the Functional Resonance Analysis Method (FRAM) model aids in predicting infectious disease epidemic risk. This approach provides early identification and rapid assessment for public health events.
Area of Science:
- Epidemiology
- Public Health
- Data Science
Background:
- Predicting epidemic risk is crucial for timely public health interventions.
- Existing methods may not fully leverage multi-source data for comprehensive risk assessment.
Purpose of the Study:
- To develop a comprehensive big data fusion assessment method for predicting epidemic risk levels.
- To enable early and timely identification of infectious disease epidemic risks.
Main Methods:
- Utilized the Functional Resonance Analysis Method (FRAM) model to create a pandemic spreading risk portrait.
- Developed a hierarchical multi-source dataset integrating medical, human behavior, internet, and geo-meteorological data.
- Employed three function module tags: Basic Risk Factors (BRF), Spread of Epidemic Threats (SET), and Risk Influencing Factors (RIF).
Main Results:
- Applied the FRAM portrait model to analyze the 2020 Wuhan pandemic case using a dynamic functional network diagram.
- Demonstrated the model's capability for risk assessment within the functional modules.
Conclusions:
- The developed FRAM portrait model offers a novel approach for early and rapid epidemic risk assessment.
- This method has potential applications for future acute public health events and infectious disease surveillance.
Related Concept Videos
01:18Steps in Outbreak Investigation
114
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:
114
01:25Statistical Methods for Analyzing Epidemiological Data
330
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:
330
01:12Statistical Software for Data Analysis and Clinical Trials
522
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...
522
01:23Mechanistic Models: Compartment Models in Individual and Population Analysis
33
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
33
01:11Hazard Rate
95
The hazard rate, also known as the hazard function or failure rate, is a statistical measure used to describe the instantaneous rate at which an event occurs, given that the event has not yet happened. From a probabilistic perspective, it represents the likelihood that a subject will experience the event in a very small time interval, conditional on surviving up to the beginning of that interval. In terms of frequency, the hazard rate can be viewed as the ratio of the number of events to the...
95
01:14Parametric Survival Analysis: Weibull and Exponential Methods
390
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
390