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Mobility data and the COVID-19 epidemic
Mobility data and the COVID-19 epidemic
Anonymised and aggregated data from Mobile Network Operators (MNOs) is a key data source to understand mobility patterns of populations and improve decision making and scenario planning during the COVID-19 epidemic. This data can be analysed in near real-time and provide an overview of mobility patterns at local levels and across an entire country.
Large scale mobility changes are both a cause and an effect of the COVID-19 pandemic. On the one hand, mobility of populations affects the speed and patterns of the epidemic. On the other, mobility restrictions, social and economic changes caused by the epidemic, all alter mobility patterns and shift distributions of populations, be it through returns of migrant workers, movements of urban populations to the countryside or mobility restrictions.
Use of mobility data depends on the national strategy
Use of mobility data depends on the national strategy
The use and importance of types of mobility data depends on the national strategy for the response against COVID-19. Countries have different resources, ability, vulnerabilities, values and access to information, which affect their strategies. It is clear that there is no single appropriate strategy for all countries. The role of lockdowns and large-scale mobilty restrictions in particular should be informed by the strategic aims of the country's response.
Possible and actual national strategies can broadly be divided into three groups:
Possible and actual national strategies can broadly be divided into three groups:
Focus on preventive measures that can be implemented at low cost, including intensive promotion of hand washing, self-quarantine of symptomatic persons and importantly protection and isolation of the most vulnerable, especially the elderly and those with other diseases or immunodeficiency (including AIDS), while not instituting lockdowns or other very costly mobility restriction measures, alternatively limiting them only to a period abosolutely necessary to put in place other interventions (read more here about approaches for resource limited settings).
Delay the epidemic substantially without aiming to stop it, with the result that sufficiently many people will eventually be infected to stop the epidemic from continuing. The aim of this approach is to “flatten the curve”, to spread out new cases over time to allow the healthcare system to handle the the large increase of patients over a longer period. At the same time, special protection is offered to the most vulnerable.
Completely or nearly stop transmission through a combination of large scale interventions including testing, self quarantines, restrictions in mobility and social gatherings and closures of places where people gather, such as workplaces, schools, kindergarten, concerts, restaurants etc.
Understanding the mobility and characteristics of populations are important in all three strategies to inform government and individual responses. In the first approach, the role of mobility restrictions is likely also limited as they are extremely costly for society and the health care system is already close to capacity. They may be used during a very brief period if absolutely necessary to put in place protection measures for the most vulnerable. Mobility data in these settings are most useful to understand changes and redistributions of the population that have taken place due to the epidemic, to monitor the return of society back to normal, and to support epidemic modelling.
For the first and second approach, mobility restrictions or voluntary mobility changes will constitute important components of the response, as well as prevention of large gatherings. Here, mobility data can be used to monitor adherence to mobility recommendations, and determine in which areas there is substantial population mixing. With large-scale testing, prediction of the epidemic can be made and, in small outbreaks, mobility restrictions can be implemented in highly targeted fashion.
In all three cases, changes in population distributions are valuable to understand how to equitably distribute resources and to improve the monitoring of health outcomes provided per person.
On this page, you will find out more about data applications, and how to turn mobility indicators into insights useful for decision making.
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