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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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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:
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Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
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Disease surveillance is the systematic collection, analysis, and interpretation of health data essential to the planning, implementation, and evaluation of public health practice. This process integrates data dissemination to entities responsible for preventing and controlling disease, injury, and disability. Surveillance systems provide crucial information for action, helping public health authorities make informed decisions to manage and prevent outbreaks, ensure public safety, optimize...
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Calculating subtransient fault currents for three-phase faults in an N-bus power system involves using the positive-sequence network. When a three-phase short circuit occurs at a specific bus, the analysis uses the superposition method to evaluate two separate circuits.
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Related Experiment Video

Updated: Jul 21, 2025

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
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Dataset for developing optimal headway-based bus dispatching strategy during epidemic outbreaks.

Yan Huang1, Zongzhi Li2, Shengrui Zhang1

  • 1College of Transportation Engineering, Chang'an University, Xi'an, Shaanxi 710064, China.

Data in Brief
|July 28, 2023
PubMed
Summary
This summary is machine-generated.

This study presents data for optimizing bus dispatching strategies during epidemics in Xi'an's CBD. The dataset covers network details, passenger flow, and travel times for enhanced urban transit planning.

Keywords:
Boarding and alighting countBus transit serviceDispatching headwayEpidemicNetwork

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Area of Science:

  • Urban planning and transportation science.
  • Epidemiological impact analysis on public transit.
  • Data science and geospatial analysis.

Background:

  • Public transportation systems face challenges during epidemics, necessitating optimized operations.
  • Effective urban transit planning requires comprehensive data on network infrastructure and passenger behavior.
  • The Xi'an Xiaozhai central business district (CBD) serves as a case study for urban mass transit analysis.

Purpose of the Study:

  • To present a dataset for identifying optimal bus dispatching strategies under epidemic conditions.
  • To provide a foundation for analyzing integrated urban passenger services.
  • To support research on the resilience of public transit systems.

Main Methods:

  • Data collection from government records, Amap platform, and field surveys.
  • Analysis of geospatial data for intersections and bus stops.
  • Inclusion of signal timing, operational properties, and passenger O-D data.

Main Results:

  • The dataset details 33 signalized intersections, 112 bus stops, and 12 bus routes.
  • Includes passenger counts and origin-destination data across peak and off-peak periods.
  • Captures intersection design, signal timing, and bus operational parameters.

Conclusions:

  • The dataset is crucial for studying integrated urban transit operations during normal and epidemic scenarios.
  • Enables research into multimodal passenger services including transit, ridesharing, and active transport.
  • Provides a resource for enhancing bus dispatching strategies and urban mobility resilience.