Global airline networks play a key role in the global importation of emerging infectious diseases. Detailed information on air traffic between international airports has been demonstrated to be useful in retrospectively validating and prospectively predicting case emergence in other countries. In this paper, we use a well-established metric known as effective distance on the global air traffic data from IATA to predict COVID-19 times of arrival (ToA) for different countries as a consequence of direct importation from China. Using this model trained on official first reports from WHO, we provide estimated ToA for all other countries. By combining effective distance with a measure for the country's vulnerability (Infectious Disease Vulnerability Index (IDVI)), we propose a metric to rank vulnerable countries at immediate risk of case emergence. We then incorporate data on airline suspensions to recompute the effective distance and assess the effect of such cancellations in delaying the estimated arrival time for all other countries.
### Competing Interest Statement
The authors have declared no competing interest.
### Funding Statement
The authors would like to thank members of the Network Systems Science and Advanced Computing (NSSAC) Division for interesting discussion and suggestions related to epidemic science and machine learning. This work was partially supported by National Institutes of Health (NIH) Grant 1R01GM109718, NSF BIG DATA Grant IIS-1633028, NSF DIBBS Grant ACI-1443054, DTRA subcontract/ARA S-D00189-15-TO-01-UVA, NSF Grant No.: OAC-1916805 and US Centers for Disease Control and Prevention 75D30119C05935.
### Author Declarations
All relevant ethical guidelines have been followed; any necessary IRB and/or ethics committee approvals have been obtained and details of the IRB/oversight body are included in the manuscript.
All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.
I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).
I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.
All data except from proprietary sources used in the analysis are made available as Supplemental material.
Adiga Aniruddha, Venkatramanan Srinivasan, Peddireddy Akhil, Telionis Alex, Dickerman Allan, Wilson Amanda, Bura Andrei, Warren Andrew, Vullikanti Anil, Klahn Brian D, Mao Chunhong, Xie Dawen, Machi Dustin, Raymond Erin, Meng Fanchao, Barrow Golda, Baek Hannah, Mortveit Henning, Schlitt James, Chen Jiangzhuo, Walke Jim, Goldstein Joshua, Orr Mark, Porebski Przemyslaw, Beckman Richard, Kenyon Ron, Swarup Samarth, Hoops Stefan, Eubank Stephen, Lewis Bryan, Marathe Madhav, Barrett Chris. (2020). Evaluating the impact of international airline suspensions on COVID-19 direct importation risk. Cold Spring Harbor Laboratory Press