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Related Concept Videos

Hazard Rate01:11

Hazard Rate

83
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...
83
Applications of GIS: Disaster Management and Emergency Response01:29

Applications of GIS: Disaster Management and Emergency Response

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Geographic Information System (GIS) technology is essential for risk identification, action prioritization, and resource optimization in critical situations like flooding and earthquakes. By integrating spatial and demographic data, GIS provides a comprehensive framework for emergency response.GIS integrates data layers, like rainfall intensity, topography, elevation profiles, and river levels, to model high-risk flood zones. These layers assess areas susceptible to flooding based on their...
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Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

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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:
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Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

84
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
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Survival Tree01:19

Survival Tree

51
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
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Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

324
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.
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Related Experiment Video

Updated: May 25, 2025

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
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A predictive model for household displacement duration after disasters.

Nicole Paul1, Carmine Galasso2, Jack Baker3

  • 1Institute for Risk and Disaster Reduction, University College London, London, UK.

Risk Analysis : an Official Publication of the Society for Risk Analysis
|February 26, 2025
PubMed
Summary

Disaster displacement models predict how long households remain displaced. Integrating physical and socioeconomic factors improves predictions, aiding in understanding and mitigating the human impact of disasters.

Keywords:
disaster displacementdisaster riskdisplacement durationhousehold displacementmachine learningpopulation return

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Area of Science:

  • Disaster risk analysis
  • Environmental sociology
  • Geographic information systems

Background:

  • Recent data shows 1.1% of US households displaced by disasters.
  • 20% of displaced households face over a month of displacement.
  • Protracted displacement causes significant hardship, yet is often overlooked in risk analyses.

Purpose of the Study:

  • To propose predictive models for household displacement duration and return.
  • To enable practical integration of displacement time into disaster risk analyses.
  • To improve the holistic understanding of disaster's human impact.

Main Methods:

  • Developed classification tree models (TreeP, TreeP&S) using physical and socioeconomic factors.
  • Utilized a random forest model (ForestP&S) for enhanced predictive power.
  • Applied models to a hurricane scenario in Atlantic City, NJ.

Main Results:

  • The ForestP&S model effectively predicted displacement duration and return.
  • Key predictors included physical factors (property damage, unsanitary conditions) and socioeconomic factors (tenure status, income).
  • Models demonstrate the feasibility of integrating displacement duration into risk assessments.

Conclusions:

  • Predictive models for household displacement duration and return are proposed.
  • Integrating physical and socioeconomic factors significantly improves prediction accuracy.
  • These models can enhance disaster risk analyses and inform mitigation strategies.