Carmen Huber

Air travel facilitates the international spread of infectious disease, allowing pathogens to spread of new areas. While global air travel data represents 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. This gap can be addressed by estimating catchment areas. This research aims to answer two research questions: (1) How do various catchment area delineation techniques estimate airport catchment areas differently? and (2) Which techniques are best suited to the use case of analysis to anticipate where infectious diseases may spread internationally?

Multiple catchment area techniques were tested for airports in Ontario, Canada: circular buffers, drive-time buffers, Thiessen polygons, and the Huff model, with multiple variations tested for some techniques. Results of each technique were compared qualitatively, and results were quantitatively compared based on total area and population within each catchment area. Results revealed notable differences in airport catchment area estimates between techniques, specifically between deterministic and probabilistic approaches. Deterministic techniques (circular buffers, drive-time buffers, and Thiessen polygons) may only be suitable if all airports in a study area are similar in terms of attractiveness. The Huff model appeared to produce more realistic results because it accounted for variation in airport attractiveness and produced probabilistic estimates instead of binary ones. Additionally, the Huff model requires few inputs and therefore would be efficient to execute in situations where time, resources, and data is limited.