How Spatial Epidemiology Helps Understand Infectious Human Disease Transmission

Introduction

  • Infectious diseases can be transmitted:
    • Directly (e.g., influenza)
    • Indirectly (e.g., through water, soil)
  • Spatial dimensions are crucial for understanding and managing diseases.
  • Advancements in computing and statistical methods enhance disease visualization and analysis.

Introduction to Spatial Epidemiology Figure 1: Visualization of spatial dimensions in disease spread.

Spatial Data Types

  • Point Data:
    • Raw data such as incidences, deaths, physical locations (e.g., hospitals)
  • Aggregate Data:
    • Summaries of individual points like incidence rates by area
  • Point Data provides detailed and precise information, allowing for pinpointing specific outbreak locations.
  • Aggregate Data helps in understanding broader trends but can obscure local variations.

Disease Mapping Techniques

Location Mapping

  • Historical examples:
    • Yellow fever in New York (1798)
    • Cholera in London (1854)
  • Advantages: Quick observation of disease spread.
  • Limitations: Privacy issues, difficulty in reflecting population distributions.

Surface Mapping

  • Choropleth Maps:
    • Example: Filariasis in India, Q fever in the Netherlands.
  • Advantages: Visualize incidence rates clearly.
  • Limitations: May hide demographic factors, sensitive to spatial scales.

Choropleth Map Example Figure 2: Example of a choropleth map showing disease incidence.

Advanced Mapping Techniques

Bayesian Smoothing

  • Reduces bias in local risk estimation.
  • Example: Applied in studies of dengue, influenza, and tuberculosis.

Kernel Density Estimation (KDE)

  • Identifies dense points and visualizes as smooth surfaces.
  • Example: Dengue cases in Delhi.

Bayesian Smoothing Figure 3: Illustration of Bayesian Smoothing applied to disease data.

Kernel Density Estimation Figure 4: KDE showing the density of dengue cases.

Overall Spatial Patterns

Clustering Methods

  • Nearest Neighbour Ratio:
    • Assesses clustering degree (e.g., rabies in China).
  • Ripley’s K Function:
    • Measures spatial clustering over distance (e.g., leishmaniasis in Brazil).

Clustering Methods Figure 5: Visualization of clustering methods.

  • Clustering methods help identify hotspots and inform targeted interventions.

Statistical Tests for Clustering

Point Data

  • Nearest Neighbour Ratio
  • Cuzick and Edwards’ test
  • Ripley’s K function

Aggregate Data

  • Spatial autocorrelation statistics (e.g., Moran’s I)
  • These tests are crucial for confirming whether observed patterns are statistically significant.

Hot Spot Detection

Local Indicators of Spatial Association (LISA)

  • Identifies clusters of high or low values.

Spatial Scan Statistics

  • Detects clusters in space and time (e.g., leishmaniasis in Brazil).

Hot Spot Detection Figure 6: Example of hot spot detection using LISA and spatial scan statistics.

Identifying Risk Factors

Neighbourhood Effect

  • Disease risk influenced by nearby cases.

Spatial Heterogeneity

  • Variation in disease risk across different areas.

Spatial Regression Methods

  • Adjust for neighbourhood effects and spatial heterogeneity.

Risk Factors Figure 7: Illustration of neighbourhood effect and spatial heterogeneity.

Conclusion

  • Spatial epidemiology provides insights into the spread and control of infectious diseases.
  • Future Directions: Enhancing GPS methods, optimizing Bayesian estimations.
  • Key Impact: Improved disease surveillance, hypothesis generation, and strategic control measures.

Conclusion Figure 8: The impact of spatial epidemiology on disease control.

References

  • Lin, C.-H., & Wen, T.-H. (2022). How spatial epidemiology helps understand infectious human disease transmission.