open access publication

Article, 2024

Inferring country-specific import risk of diseases from the world air transportation network.

PLOS Computational Biology, ISSN 1553-7358, 1553-734X, Volume 20, 1, Page e1011775, 10.1371/journal.pcbi.1011775

Contributors

Klamser, Pascal P 0000-0003-2208-4391 [1] [2] Zachariae, Adrian 0000-0001-9780-4651 [1] [2] Maier, Benjamin Frank 0000-0001-7414-8823 [1] [2] [3] [4] Baranov, Olga [5] [6] [7] Jongen, Clara [1] [2] Schlosser, Frank 0000-0003-1649-4300 [1] [2] Brockmann, Dirk 0000-0001-5708-2922 [1] [2] [8]

Affiliations

  1. [1] Humboldt-Universität zu Berlin
  2. [NORA names: Germany; Europe, EU; OECD];
  3. [2] Robert Koch Institute
  4. [NORA names: Germany; Europe, EU; OECD];
  5. [3] Technical University of Denmark
  6. [NORA names: DTU Technical University of Denmark; University; Denmark; Europe, EU; Nordic; OECD];
  7. [4] University of Copenhagen
  8. [NORA names: KU University of Copenhagen; University; Denmark; Europe, EU; Nordic; OECD];
  9. [5] German Center for Infection Research
  10. [NORA names: Germany; Europe, EU; OECD];

Abstract

Disease propagation between countries strongly depends on their effective distance, a measure derived from the world air transportation network (WAN). It reduces the complex spreading patterns of a pandemic to a wave-like propagation from the outbreak country, establishing a linear relationship to the arrival time of the unmitigated spread of a disease. However, in the early stages of an outbreak, what concerns decision-makers in countries is understanding the relative risk of active cases arriving in their country-essentially, the likelihood that an active case boarding an airplane at the outbreak location will reach them. While there are data-fitted models available to estimate these risks, accurate mechanistic, parameter-free models are still lacking. Therefore, we introduce the 'import risk' model in this study, which defines import probabilities using the effective-distance framework. The model assumes that airline passengers are distributed along the shortest path tree that starts at the outbreak's origin. In combination with a random walk, we account for all possible paths, thus inferring predominant connecting flights. Our model outperforms other mobility models, such as the radiation and gravity model with varying distance types, and it improves further if additional geographic information is included. The import risk model's precision increases for countries with stronger connections within the WAN, and it reveals a geographic distance dependence that implies a pull- rather than a push-dynamic in the distribution process.

Keywords

active cases, air transportation network, airline, airline passengers, airplane, arrival, arrival time, cases, combination, complex spreading pattern, connection, countries, decision-making, dependence, disease, disease propagation, distance, distance dependence, distance types, distribution, distribution process, early stages, effective distance, flight, framework, geographic information, gravity, gravity model, importance, importation probability, importation risk, information, likelihood, location, measurements, mobility, mobility model, model, model precision, network, origin, outbreak, outbreak countries, outbreak locations, outbreak origin, pandemic, parameter-free model, passenger, path, path tree, patterns, precision, probability, process, propagation, pushing dynamics, radiation, random walk, relationship, relative risk, risk, risk of disease, shortest path tree, spread, spread pattern, stage, stronger connections, study, time, transportation network, trees, type, walking, wave-like propagation, world, world air transportation network

Funders

  • Carlsberg Foundation
  • Federal Ministry of Health

Data Provider: Digital Science