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Using Modeling to Limit Infectious Disease Transmission at Airports and Train Stations

The model concerns London Heathrow airport. © Unsplash

In crowded places, such as airports and train stations, social distancing is difficult to maintain and the risk of infectious disease transmission is increased. In order to reduce this risk, it is essential that we improve our understanding of the dynamics of disease transmission within such places and the effective mitigation measures that can be implemented at low cost. This is the objective of a mathematical model developed by teams from Inserm and Sorbonne Université at the Pierre Louis Institute of Epidemiology and Public Health with the Spanish Institute CSIC-IFISC. Taking the example of London Heathrow airport and diseases such as H1N1 influenza and COVID-19, this model makes it possible to identify zones with the highest risk of transmission within crowded places. By targeting these hotspots with measures such as air filters or the use of Far-UVC lights[1], the scientists also show that it is possible to significantly reduce contamination. Their full findings have been published in Nature Communications.

Crowds and gatherings, with their prolonged contacts between individuals, are a crucial factor in the spread of infectious diseases. While it is possible to implement certain risk reduction measures such as the wearing of masks, the maintenance of social distancing cannot always be respected, especially in transportation hubs such as airports and train stations. After all, these locations are designed to optimize logistical efficiency rather than reduce crowding. They are characterized by a constant in and outflow of visitors, with a high risk of international disease transmission.

The study by the scientists from Inserm, Sorbonne Université and CSIC-IFISC describes a mathematical model that identifies, within these places, the hotspots for the transmission of infectious diseases. It is essential to know exactly where these hotspots are in order to implement appropriate “spatial immunization” strategies, i.e. specific prevention measures that target these zones and reduce contamination.

“In the hotspots that we have identified with our model, the development of dedicated approaches such as air filtration, systematic surface disinfection, and the use of Far-UVC lights can significantly reduce the risk of pathogen spread beyond the first cases arriving at an airport or train station without having been detected,” explains Mattia Mazzoli, Inserm researcher and first author of the study.


A Model Built From GPS Data

In this article, the scientists studied the example of Europe’s busiest airport: London Heathrow. Their model uses anonymized data concerning the movements of over 200 000 people within the airport, derived from the GPS tracking of cell phones between February and August 2017. Using this data, the researchers were able to visualize movements with a spatial resolution of 10 meters, reconstruct the contact networks between these different people, and thereby detect the zones where contacts were the most intense, with a higher risk of contamination.

In order to provide some practical examples, the scientists fed their mathematical model with data concerning the spread of diseases such as H1N1 influenza and COVID-19 in order to study their dissemination throughout the airport.


A Model That Can Be Applied to the Future

The results of this modeling show that the communal areas such as bars and restaurants are where the highest number of infections occur, as these are where travelers and airport staff are brought into contact for long periods of time.

“The danger of these contagion hotspots is driven by a balance between the number of people that use them and the time they spend there. While these are not always the busiest places in the airport, they do involve more sustained contacts for longer periods of time, enabling the spread of diseases,” emphasizes Mazzoli.

Although the model has only been tested with H1N1 influenza and COVID-19, it could still be used in the future to study any new and as yet uncharacterized pathogen. In addition, the method is immediately generalizable to other modes of transport such as trains, subways, bus stations or other crowded facilities where social distancing is impossible, such as malls and convention centers.

“Using spatial immunization measures reduces the number of infections among airport users and, to a lesser extent, among airport staff. When well-targeted and implemented in zones identified as presenting the highest risk, these measures are helpful in containing and/or delaying the spread of infectious agents to the rest of the world via airports or other crowded centers. Our model could be particularly useful in the early stages of a potential future epidemic, when the first cases imported into airports and train stations have not yet been detected,” concludes Mazzoli.

Researcher Contact

Mattia Mazzoli

Inserm researcher

Pierre Louis Institute of Epidemiology and Public Health (Inserm/Sorbonne Université)

Email: rf.mresni@ilozzam.aittam

Telephone number provided upon request


Press Contact



Spatial immunization to abate disease spreading in transportation hubs

Mattia Mazzoli1, 2, Riccardo Gallotti3, Filippo Privitera4, Pere Colet1, and Jose J. Ramasco1

1 Instituto de Fiica Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB), Campus UIB, 07122 Palma de Mallorca, Spain

2 Inserm, Sorbonne Université, Institut Pierre Louis d’Epid_emiologie et de Sant_e Publique, IPLESP, Paris, France

3 CHuB Lab, Fondazione Bruno Kessler, Via Sommarive 18, 38123, Povo (TN), Trento, Italy

4 Cuebiq Inc., 45 W 27th Street, 3rd oor, 10001, New York, NY, USA

Nature communications, mars 2023

DOI : 10.1038/s41467-023-36985-0