Measuring imported case risk of COVID-19 from inbound international flights --- A case study on China

Abstract

With COVID-19 spreading around the world, many countries are exposed to the imported case risk from inbound international flights. Several governments issued restrictions on inbound flights to mitigate such risk. But with the pandemic controlled in many countries, some decide to reopen the economy by relaxing the international air travel bans. As the virus has still been prevailing in many regions, this relaxation raises the alarm to import overseas cases and results in the revival of local pandemic. This study proposes a risk index to measure one country's imported case risk from inbound international flights. The index combines both daily dynamic international air connectivity data and the updated global COVID-19 data. It can measure the risk at the country, province and even specific route level. The proposed index was applied to China, which is the first country to experience and control COVID-19 pandemic while later becoming exposed to high imported case risk after the epidemic centers switched to Europe and the US afterward. The calculated risk indexes for each Chinese province or region show both spatial and temporal patterns from January to April 2020. It is found that China's strict restriction on inbound flights since March 26 was very effective to cut the imported case risk by half than doing nothing. But the overall index level kept rising because of the deteriorating pandemic conditions around the world. Hong Kong and Taiwan are the regions facing the highest imported case risk due to their superior international air connectivity and looser restriction on inbound flights. Shandong Province had the highest risk in February and early March due to its well-developed air connectivity with South Korea and Japan when the pandemic peaked in these two countries. Since mid-March, the imported case risk from Europe and the US dramatically increased. Last, we discuss policy implications for the relevant stakeholders to use our index to dynamically adjust the international air travel restrictions. This risk index can also be applied to other contexts and countries to relax restrictions on particular low-risk routes while still restricting the high-risk ones. This would balance the essential air travels need and the requirement to minimize the imported case risk.

COVID-19 and transport: Findings from a world-wide expert survey

“…Sokadjo and Atchadé ( 2020 ) found that at the global level, passenger air traffic is positively related to the number of infections. Zhang et al (2020a) showed that infection cases in China were strongly associated with frequencies of flights and high-speed trains, while Zhang et al (2020b) revealed the imported case risk imposed by foreign countries on Chinese provinces and further confirmed the effectiveness of China's strict restriction on inbound flights after March 26 in dramatically reducing the imported case risk. From a different angle, Zhang et al (2020) and Cartenì et al ( 2021 ) observed a strong positive correlation between transport accessibility and the infection cases in Japan and Italy, respectively.…”

Exploring spatiotemporal effects of the driving factors on COVID-19 incidences in the contiguous United States

“…Based on the present research, notably, the overall regression models reveal that population migration, as indicated by domestic migration and the rate of international migration, is highly correlated with the numbers of COVID-19 cases and deaths. The move of people across continents internationally is accompanied with high risk of virus spread, as the air traveling means by its nature increases the likelihood of person-to-person COVID-19 transmissions ( Zhang, Yang et al, 2020 ; Zhang, Wang et al, 2020 ). Given this evidence, air flight restrictions could be effective in undermining the virus spread, which is in line with the conclusion of positive associations between travel restrictions and COVID-19 spread from previous findings ( Christidis & Christodoulou, 2020 ), although this involves trade-offs between air-transporting public health and social-economics risks ( Cotfas, Delcea, Milne, & Salari, 2020 ).…”

“…Random Forest models (Breiman, 2001), fundamentally based on bootstrap aggregating of decision trees, can minimize the unexplained variance of models and thus improve prediction accuracy ( Altmann, Toloşi, Sander, & Lengauer, 2010 ). Random Forest models have been utilized for many domain-specific studies, such as gene expression-based cancer classification (Okun and Priisalu, 2007), biology of ageing ( Fabris, Doherty, Palmer, De Magalhaes, & Freitas, 2018 ), remote sensing land cover mapping ( Ma et al, 2017 ; Zhang, Yang, Wang, Zhan, & Bian, 2020 ; Zhang, Wang et al, 2020 ), screening underlying lead compounds ( Cao et al, 2011 ), Structure damage detection ( Zhou, Zhou, Zhou, Yang, & Luo, 2014 ). In this study, we measured the variable importance based on the overall capacity of the variables to explain the total model variances.…”

COVID-19 pandemic and air transportation: Successfully navigating the paper hurricane

“…Compartmental meta-population models or data science techniques are used to obtain relationships between the number of cases and different regions in the world; leading to predictions about the time and extent of an arrival of COVID-19 in these places. Some studies take a rather global, network perspective ( Coelho et al., 2020 ; Hossain et al., 2020 ; Zhang et al., 2020b ), while others focus on specific regions, e.g., China ( Li et al., 2020b ; Zhang et al., 2020c ), EU/US ( Nikolaou and Dimitriou, 2020 ; Peirlinck et al., 2020 ), Iran ( Tuite et al., 2020 ), Brazil ( Ribeiro et al., 2020 ) or Columbia ( Gomez-Rios et al., 2020 ). Overall, the findings in these studies are largely consistent, given the earlier work on disease spreading (pre-COVID-19).…”