Data Collection
In the present study, to calculate the importation risk of 2019-nCoV
infection we collected the number of passengers for given airports of an
origin country travelling to various international airports in India. We
also accounted the total number of 2019-nCoV infected cases in the
country of origin. Information about the total number of infected cases
in the country of origin was obtained by extensive online search of more
than five different sources, which mainly included the government
sources and were rigorously verified and validated for any
inconsistencies. To obtain the count of air passengers from a origin
airport in given country, we relied on a web-based analytical tool
called FLIRT (Huff, Allen, Whiting, Breit, & Arnold, 2016). The major
application of FLIRT is to predict the air travel network flow between
origin and destination airports throughout the world, where user can
choose the origin and destination pair. Note that the FLIRT does not
take an account of transit passenger is making between origin to
destination and would always assign the last boarding airport as an
origin, once entered in final destination. This web application allows
user perform passenger simulations based on the air traffic between
source and destination. To simulate the results, it uses a database of
flight schedules of more than 800 airlines (Andrew Huff et al., 2016; A.
Huff et al., 2016). Researchers in the past (Haider et al., 2020; Andrew
Huff et al., 2016; A. Huff et al., 2016) have used this web interface to
estimate the number of infected travelers originating from different
countries to the respective destinations. For India, in this study, the
countries selected for importation risk index calculation were China,
France, Germany, Iran, Italy, Kuwait, Oman, Qatar, Singapore, Spain,
Thailand, United Arab Emirates (UAE), United Kingdom (UK), and United
States of America (USA), which constituted ~70% of the
total infected cases. For simplicity in analysis, the countries Iran,
Kuwait, Oman, Qatar, and UAE were grouped together as Middle Eastern
countries. The major international airports from all the above-mentioned
countries were selected to simulate the air traffic flow. The IATA codes
for all the selected airports are listed in Table 1.
The FLIRT web application allows two different types of flight data
simulation. The first mode includes the simulation of total seats
offered by airlines between origin and destination airports, and the
other mode simulates the total passengers travelled between airports.
The second mode, however, has a significant disadvantage that it allows
only 20,000 passengers to be simulated at a time, which is an upper
bound on the total number of passengers that can be simulated. Such an
upper bound can have a strong bias in the analysis, particularly for
this kind of studies. Therefore, the first simulation mode was preferred
here. This selection would not affect the analysis, as FLIRT assumes
that the number of seats between two airports is directly proportional
to the number of passengers between them (Andrew Huff et al., 2016).
Thus, for each airport mentioned in Table 1, the simulation was
performed using FLIRT as per the first criteria mentioned above. This
simulation was carried out for the period of 4th March
2020 till 24th March 2020. This period was selected
because the number of 2019-nCoV cases in India started to grow
significantly from 4th of March, and India imposed an international
travel ban from 24th of March. During this period
India has observed a screening at all international airports across the
country. After each simulation, the result was filtered for all the
India bound flights for the respect chosen airports, and thus the total
number of passenger seats and the number of Indian bound passenger seats
were estimated from each airport of the origin. This data along with the
number of infected persons in the origin airport country, from the
various authenticated sources was then used to estimate an importation
risk index.