Unintended consequences: holiday travel during the COVID-19 pandemic
COVID-19 related travel restrictions were imposed in China around the same time as major annual migrations. We show that restructuring of the travel network in response to restrictions was temporary, whilst holiday-related travel increased pressure on healthcare services with lower capacity.
Holiday travel is often associated with fun and quality times with family and friends. In China, the largest holiday season is around the Lunar New Year, also called Chunyun (“spring migration”). Not only are there more travellers during this time, travel destinations are also different from the rest of the year, as those who work in large cities return home. The Chunyun of 2020 was projected to incur nearly three billion person-trips over a forty-day period. Unfortunately, this year’s Chunyun coincided with the beginning of the COVID-19 pandemic, and may have contributed to spreading the novel pathogen.
To study the role of human mobility in COVID-19 transmission, we used travel data provided by Baidu (a Chinese technology company) which aggregates user locations to describe travel flows between prefectures. Human mobility is a suitable metric to study infectious disease dynamics as it is a proxy for how members from different communities interact with each other. In our recent study, we found that Chunyun may have contributed to early COVID-19 introduction in large cities like Beijing and Shanghai. The unique travel patterns during Chunyun also shifted COVID-19 disease burden to smaller cities, where the healthcare systems may be less able to support a temporary surge in local population in terms of caring or testing.
Human mobility, under normal circumstances, is modulated by weekends and holidays. During the COVID-19 pandemic, non-pharmaceutical interventions designed to mitigate or suppress community transmissions have also affected human mobility in China and around the world. In London, the COVID-19 related remote working policies have increased activities around residential areas while decreasing activities around workplaces. Although COVID-19 policies have been easing over the past few months, changes in mobility have not yet recovered. We need to start considering the possibility that the COVID-19 pandemic has changed our lives in such a way that our behaviours may never return to the previous “normal”.
The mobility data we used is only one of a wide range of movement data sources that allow researchers to study COVID-19. Data with even finer resolution is also made available by tech companies (e.g., Tencent, Facebook) and by telecommunication service providers. How mobility has contributed to disease transmission is only one of the many questions that we can answer with this data. Mobility data can also be used to assess the rate of compliance with non-pharmaceutical interventions (e.g., to what degree people stopped traveling after travel restrictions), and to understand if these interventions are effective (e.g., have travel restrictions effectively stop introducing infections to susceptible populations). New data availability has allowed us to answer questions in ways that were not possible previously. Combining multiple sources of spatial data (e.g., cases, testing, phylogenetics, genomics, contacts) will increase this potential to answer challenging questions even further.
Photo: Wuhan City. Credit: Junping Fan, MD/ PUMC Hospital Dept of Pulmonary and Critical Care Medicine.