Using urban landscape pattern to understand and evaluate infectious disease risk.

Area of Science:

• Environmental Science
• Epidemiology
• Urban Planning

Background:

• COVID-19 transmission is influenced by complex spatial factors.
• Understanding the relationship between urban landscape patterns and infectious disease spread is crucial for public health.
• Previous studies have not fully integrated landscape metrics with epidemiological data at a fine spatial scale.

Purpose of the Study:

• To investigate the association between landscape patterns and COVID-19 infection numbers in Wuhan sub-districts.
• To identify key landscape metrics that predict infectious disease risk.
• To develop a framework for evaluating and managing urban infectious disease risk based on landscape epidemiology.

Main Methods:

• Utilized landscape epidemiology to analyze COVID-19 case data in 161 Wuhan sub-districts.
• Calculated landscape metrics based on land use/land cover (LULC) data.
• Employed mediation models, geographically weighted regression (GWR), and principal component analysis (PCA).

Main Results:

• Central urban sub-districts exhibited higher infection risk (Adjusted Incidence Rate: 25.82–63.56 ‱).
• GWR models demonstrated strong predictive power (adjusted R² 0.668–0.835) for infection risk using landscape metrics.
• Principal components influencing risk included development intensity (urban, unused land), landscape connectivity, and blue-green spaces (water, woodland).

Conclusions:

• Urban landscape patterns, including development intensity, connectivity, and blue-green spaces, significantly predict infectious disease risk.
• The findings support a landscape epidemiology approach for urban infectious disease risk assessment and policy-making.
• This research provides a basis for community management and urban planning to mitigate infectious disease outbreaks globally.