Modeling the Propagation of Infectious Diseases across the Air Transport Network: A Bayesian Approach

Pablo Quirós Corte, Javier Cano (), Eduardo Sánchez Ayra, Chaitanya Joshi and Víctor Fernando Gómez Comendador
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Pablo Quirós Corte: Department of Aerospace Systems, Air Transport and Airports, Universidad Politécnica de Madrid, 28040 Madrid, Spain
Javier Cano: Department of Computer Science and Statistics, Rey Juan Carlos University, 28933 Madrid, Spain
Eduardo Sánchez Ayra: Department of Aerospace Systems, Air Transport and Airports, Universidad Politécnica de Madrid, 28040 Madrid, Spain
Chaitanya Joshi: Department of Statistics, University of Auckland, Auckland 1010, New Zealand
Víctor Fernando Gómez Comendador: Department of Aerospace Systems, Air Transport and Airports, Universidad Politécnica de Madrid, 28040 Madrid, Spain

Mathematics, 2024, vol. 12, issue 8, 1-25

Abstract: The COVID-19 pandemic, caused by the SARS-CoV-2 virus, continues to impact the world even three years after its outbreak. International border closures and health alerts severely affected the air transport industry, resulting in substantial financial losses. This study analyzes the global data on infected individuals alongside aircraft types, flight durations, and passenger flows. Using a Bayesian framework, we forecast the risk of infection during commercial flights and its potential spread across an air transport network. Our model allows us to explore the effect of mitigation measures, such as closing individual routes or airports, reducing aircraft occupancy, or restricting access for infected passengers, on disease propagation, while allowing the air industry to operate at near-normal levels. Our novel approach combines dynamic network modeling with discrete event simulation. A real-case study at major European hubs illustrates our methodology.

Keywords: air transport networks; Bayesian analysis; discrete event simulation; imported risk (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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