Cover: COVID-19 Air Traffic Visualization

COVID-19 Air Traffic Visualization

Worldwide Spread of COVID-19 Accelerated Starting on February 19, 2020

Published Sep 22, 2020

by Christopher A. Mouton, Adam R. Grissom, John P. Godges, Russell Hanson

Key Finding

Using the CAT-V tool, we estimate a marked increase in the worldwide spread of COVID-19 starting on February 19, 2020—three weeks before the official declaration of a global COVID-19 pandemic. By the end of February, nearly 40 passengers per week were spreading the disease worldwide via international air travel. The United States, in particular, was facing importation risks from every continent except Antarctica.

In this report—one of several from a RAND Corporation team examining the role of commercial air travel in the coronavirus disease 2019 (COVID-19) pandemic—we use our COVID-19 Air Traffic Visualization (CAT-V) tool to estimate when COVID-19 transmission via commercial air travel began to rapidly accelerate throughout the world. The tool combines daily COVID-19 case data from Johns Hopkins University with detailed air travel data, including travelers' country of origin and country of destination, from the International Air Transport Association (IATA).

Using the CAT-V tool, we estimate the global spread of COVID-19 by international air passengers prior to the March 11, 2020, declaration by the World Health Organization (WHO) that the world was officially experiencing a pandemic—defined as "the worldwide spread of a new disease." Our estimates indicate that "worldwide spread" of the disease had occurred weeks before the WHO declaration.

In particular, we estimate that the worldwide exports of COVID-19 cases began increasing at an accelerating rate on February 19, 2020, exactly three weeks before the WHO declaration. By the end of February, about two weeks before the WHO declaration, more than five cases of COVID-19 per day—or nearly 40 per week—were already being exported around the globe via air travel.

In the first figure below, which uses a linear scale of the number of cases, we show the estimated daily global exports of COVID-19 between January 22 and March 31, 2020. However, the increase in daily exports in late February exceeded the bounds of our linear chart. For this reason, we rely on the second figure, which has a logarithmic scale, instead. Both figures contain the same data.

Estimated Daily Global Exports of COVID-19 via Air Travel (Log Scale)

  1. January 22: 0.1 estimated daily global exported cases
  2. January 29: Around 1 estimated daily global exported case
  3. February 2: With about 1.5 estimated daily global exported cases, travel restrictions from China take effect
  4. February 19: increased rate of worldwide COVID-19 spread starts
  5. March 2: With about 7 estimated daily global exported cases, travel restrictions from Iran take effect
  6. March 11: With around 42 estimated daily global exported cases, the WHO formally declares a global pandemic
  7. March 14: With around 76 estimated daily global exported cases, travel restrictions from Schengen Area take effect
  8. March 18: At around 130 estimated daily global exported cases, increased rate of worldwide COVID-19 spread ends
  9. March 31: Around 406 estimated daily global exported cases

NOTES: The curves represent CAT-V daily export data. The shaded areas around the curves represent the error bands when using IATA monthly data versus daily data. The portions of the curves shown in red highlight the convex portions, or the accelerating daily increases in COVID-19 exports.

Fifteen countries were importing more than one case per week, mostly from outside China, as of February 27, 2020. The countries with the largest numbers of likely infected inbound passengers by that date spanned Asia, North America, and Europe. The top nine countries, in descending order of likely inbound importation risk, were Japan, Thailand, the United States, Vietnam, South Korea, the United Kingdom, the Philippines, Malaysia, and Germany.

Outside China, the largest number of active cases in Asia at the time was in South Korea. With more than 1,700 confirmed cases, South Korea was likely exporting more than two cases abroad per day via air travel. Italy, with nearly 600 active cases, was likely exporting more than one case abroad per day via air travel. The map below shows that South Korea represented the highest risk of exportation to the United States on February 27, 2020. Overall, however, the United States faced inbound infection risks from more than 50 countries and from every continent except Antarctica as early as that date.

Risk of COVID-19 Importation to the United States on February 27, 2020

A global heat map of inbound COVID-19 risk is overlaid with red lines showing air travel routes to the United States. Described by the paragraph preceding this figure.

NOTE: The width of each line in this map is proportional to the number of imported cases. The line from Japan to the United States represents about one case every other month; the line from South Korea to the United States represents almost five cases every month.

These findings underscore the importance of developing a responsive, forward-looking analytical basis for policies related to pandemic risks. Formal public health declarations may occur too late to be effective triggers for key decisions. As COVID-19 enters a phase in which infection "hot spots" emerge and recede across the world, forward-looking tools, such as CAT-V, can play an important role in helping policymakers, particularly at the U.S. Departments of Defense and Homeland Security, keep ahead of the risks. By combining projections of COVID-19 infection rates and air travel patterns, policymakers can first identify countries that would be expected to pose serious infection risks in the future and then devise travel restriction plans to mitigate the risks.


In accordance with RAND's quality assurance standards, this analysis is based on the best available data. However, COVID-19 is an evolving threat, and even the best available data being used by government agencies and research institutes have very significant limitations. In the first report in this series, we outline several caveats about using country-level data, assuming equal passenger risk profiles, drawing on inaccurate country caseload reports, and being restricted by other data limitations.

For this report, it is important to note that the CAT-V tool uses monthly IATA data without higher-fidelity daily corrections. Higher-fidelity data are particularly important during times of rapid changes in the level and pattern of air travel. The yellow-shaded areas in the first and second figures above show estimates of such error. Additionally, we reiterate that the tool, consistent with most academic research and public policy tools, uses confirmed COVID-19 cases, as reported by individual countries. The true number of infected individuals in any given country is certainly higher than reported. The CAT-V tool does not correct for inaccurate or misleading case reporting.


The COVID-19 Air Traffic Visualization tool combines confirmed COVID-19 case data from the Johns Hopkins University Center for Systems Science and Engineering's COVID-19 Dashboard with detailed air passenger data from the International Air Transport Association's Nationality Traffic Report program. Together, these data sets allow the researchers to visualize and analyze the estimated transmission of the novel coronavirus via air travel, outline the resulting implications, and offer suggestions for minimizing the most-dangerous potential vectors.

This research was sponsored by the Office of the Secretary of Defense and the U.S. Air Force and conducted jointly within the Acquisition and Technology Policy Center of the RAND National Security Research Division and the Strategy and Doctrine Program of RAND Project AIR FORCE.

This report is part of the RAND research report series. RAND reports present research findings and objective analysis that address the challenges facing the public and private sectors. All RAND reports undergo rigorous peer review to ensure high standards for research quality and objectivity.

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