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Spatial and temporal epidemiological analysis in the Big Data era.

Dirk U Pfeiffer1, Kim B Stevens1

  • 1Veterinary Epidemiology, Economics & Public Health Group, Department of Production & Population Health, Royal Veterinary College, London, UK.

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Summary
This summary is machine-generated.

Global connectivity fuels infectious disease spread and generates Big Data. Advanced data science and machine learning offer new tools for managing health risks, but data biases remain a challenge.

Keywords:
Data scienceExploratory analysisInternet of ThingsModellingMulti-criteria decision analysisSpatial analysisVisualisation

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Area of Science:

  • Veterinary Public Health
  • Epidemiology
  • Data Science

Background:

  • Intensified global connectivity has increased infectious disease spread and data generation.
  • The Internet of Things (IoT) further amplifies data availability through interconnected sensors.
  • Effective management of animal and human health risks requires sophisticated data analysis.

Purpose of the Study:

  • To explore the role of Big Data and advanced analytical methods in managing global health risks.
  • To highlight the challenges and opportunities presented by complex, high-volume data in disease surveillance.
  • To discuss the evolution of data analysis from statistical science to data science.

Main Methods:

  • Analysis of Big Data, including spatial and temporal dimensions.
  • Application of data management tools like relational databases, GIS, and cloud storage (e.g., Hadoop).
  • Utilization of advanced analytical methodologies such as machine learning regression and multi-criteria decision analysis.

Main Results:

  • Significant advances in spatial and spatio-temporal data analysis, including visualization, exploratory analysis, and modeling.
  • Increased adoption of data science approaches integrating diverse data sources and analytical methods.
  • Machine learning regression and multi-criteria decision analysis are increasingly used for complex data challenges.

Conclusions:

  • Big Data and advanced analytics offer immense opportunities for improved prevention, detection, and control of animal health threats.
  • The integration of diverse data sources, including expert opinion, is crucial for filling knowledge gaps.
  • Data quality, bias, and the need for robust data management tools remain critical considerations.