Spatial Tessellation of Infectious Disease Spread for Epidemic Decision Support

Area of Science:

• Epidemiology
• Public Health
• Spatial Analysis

Background:

• Infectious diseases like COVID-19 significantly impact global economy and public health.
• Heterogeneous virus spread leads to spatial variations in demand for critical medical resources (PPE, tests, vaccines).
• Effective epidemic control relies heavily on the availability and strategic allocation of these resources.

Purpose of the Study:

• To develop novel tessellation algorithms for decision support in epidemic resource allocation and management.
• To estimate optimal resource locations and coverage based on spatial analysis of infection distribution.
• To address the gap in research concerning tessellation methods for epidemic resource management.

Main Methods:

• Initialization of spatial tessellation centroids using greedy or cluster-centric approaches.
• Calibration of centroid locations via a gradient learning algorithm.
• Computation of spread tessellation to estimate resource coverage under heterogeneous infection distributions.

Main Results:

• The proposed methodology effectively tessellates infectious disease spread.
• Validation using a COVID-19 case study in Pennsylvania demonstrates practical application.
• The algorithms provide estimations of resource coverage aligned with infection patterns.

Conclusions:

• The developed tessellation algorithms offer a robust approach for epidemic decision support.
• These methods have strong potential for enhancing infection modeling and optimizing resource allocation strategies.
• Improved spatial analysis of disease spread can lead to more efficient medical resource management during public health crises.