Human Mobility and the COVID-19 Pandemic
Ronaldo Menezes is a Professor of Data and Network Science, Head of the Computer Science department at the University of Exeter, and Director of the BioComplex Laboratory. His research interests include Network Science, Human Dynamics and Mobility, Complex Systems, and Urban Systems. He has spent 18 years in academia in the USA before moving to the University of Exeter in late 2018. His lab has received funding from the National Science Foundation (USA), Army Research Office (USA), CAPES (Brazil), FUNCAP (Brazil), to name a few.
He is deeply involved with the research community in Human Mobility and Network Science as a founding member of CompleNet and a board member of NetSci Society and the Latin American Conference on Complex Networks (LANET). He is also the co-Editor-in-Chief of one of the main journals in the field - Applied Network Science published by Springer Nature. He has more than 200 peer-reviewed publications in these fields and has collaborated with more than 40 researchers worldwide.
Human Mobility and the COVID-19 Pandemic
It is evident that the consequences of the COVID-19 pandemic are yet to be understood and to say that the world will never be the same is probably an understatement regardless of how we recover from this situation. As I write this post (mid-2020), we are seeing signs of a second peak in the USA lead mostly by outbreaks in the south of the USA, and nations such as Brazil and India yet to reach the peak of the first outbreak. This is occurring whilst the world has more than 10 million confirmed cases and more than 500,000 people have lost their lives before their time due to this disease. Indubitably, the situation was made worse because countries were ill-prepared for outbreaks of this magnitude. Amongst the many reasons that brought us here (e.g. lack of intensive care units, governments not educating their citizens about the dangers of such disease, and misinformation spreading faster than the disease itself), the understanding of population dynamics has entered the world lexicon; terms such as social distancing, social bubbles, social isolation, and contact tracing became commonplace and spoken in every part of the world. After some initial hesitation, it became clear that in the absence of a vaccine, the most effective way of containing the spread of the disease was to implement restrictions to people’s movements to minimise contagion.
At the centre of the lockdown debate, it is a scientific field generally referred to as human mobility modelling which aims primarily at understanding our spatial and temporal dynamics (mostly in urban environments), but also at creating predictive models of population and individual movements. Humans are mobile creatures constantly searching for new opportunities, defining us since early humans left Africa to explore the new world. Nowadays, we continue to move solely due to food scarcity, climate change but mostly due to socio-economic factors such as pay imbalances, home-work daily rhythms, differences in living conditions, diverse pressures exerted to individuals based on their age and gender, to name a few.
The field of human mobility has attracted scientists from diverse backgrounds including mathematicians, computer scientists, statisticians, physicists, social scientists, and behaviour analysts, among others. Arguably, however, it was geography the first discipline to look at mobility data to describe travel patterns in the early 50s. Since then, we have seen a dramatic increase in published works describing regularities in human mobility at different scales (from daily routines such as commuting to international patterns) and models being used in several applications such as traffic forecasting, urban planning, and epidemic modelling. Furthermore, there are now several hypotheses that such models of human movement could be linked to other human dynamics behaviours such as customer purchasing and online activity.
The availability of human traces from phones, local travel surveys, GPS, and location-based social networks enabled scientists to characterise various facets of human mobility including the length of trips, displacements, the radius of activity, and spatial motifs at individual and population basis. The more recent trend, however, has been to disaggregate these regularities by socio-economic groups bringing new clarity to group differences and more important mobility inequalities.
Putting all together, the COVID-19 pandemic demonstrated to the general population how the science of human mobility can be used to deal with a practical problem. Mobility models can predict social mixing, the effect of lockdown policies to different socio-economic groups and consequently its effectiveness to general populations based on their specific socio-economic composition. Additionally, as countries attempt to return to a normal life, human mobility can be of help by predicting the consequences of various policies of loosing restrictions. There is however a lot more challenges to be addressed including data collection while maintaining privacy, predictability at scale, and the effect of social context (social networks) to mobility patterns. COVID-19 has showed the importance of investing in the field. An interested reader can check our extensive survey Human Mobility: Models and Applications as well as several papers published recently in some of the Springer Nature journals such as Applied Network Science and EPJ Data Science.
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