Market area delineation for airports to predict the spread of infectious disease

Abstract

Air travel facilitates the international spread of infectious disease. While global air travel data represent the volume of travel between airports, identifying which airport an infected individual might use, or where a disease might spread after an infected passenger deplanes, remains a largely unexplored area of research and public health practice. This gap can be addressed by estimating airport catchment areas. This research aims to determine how existing market area delineation techniques estimate airport catchments differently, and which techniques are best suited to anticipate where infectious diseases may spread. Multiple techniques were tested for airports in the Province of Ontario, Canada: circular buffers, drive-time buffers, Thiessen polygons, and the Huff model, with multiple variations tested for some techniques. The results were compared qualitatively and quantitatively based on spatial patterns as well as area and population of each catchment area. There were notable differences, specifically between deterministic and probabilistic approaches. Deterministic techniques may only be suitable if all airports in a study area are similar in terms of attractiveness. The probabilistic Huff model appeared to produce more realistic results because it accounted for variation in airport attractiveness. Additionally, the Huff model requires few inputs and therefore would be efficient to execute in situations where time, resources, and data are limited.