Optimization strategies of human mobility during the COVID-19 pandemic: A review

TL;DR

This review paper examines various data-driven, machine learning, and modeling techniques used to optimize human mobility during the COVID-19 pandemic, balancing public health measures with economic impacts.

Contribution

It provides a comprehensive overview of optimization strategies for human mobility during COVID-19, highlighting recent approaches and their effects across different scales.

Findings

Various optimization techniques have been applied to control mobility.

Data-driven and machine learning methods are prominent in recent strategies.

These approaches aim to balance health safety and economic activity.

Abstract

The impact of the ongoing COVID-19 pandemic is being felt in all spheres of our lives -- cutting across the boundaries of nation, wealth, religions or race. From the time of the first detection of infection among the public, the virus spread though almost all the countries in the world in a short period of time. With humans as the carrier of the virus, the spreading process necessarily depends on the their mobility after being infected. Not only in the primary spreading process, but also in the subsequent spreading of the mutant variants, human mobility plays a central role in the dynamics. Therefore, on one hand travel restrictions of varying degree were imposed and are still being imposed, by various countries both nationally and internationally. On the other hand, these restrictions have severe fall outs in businesses and livelihood in general. Therefore, it is an optimization process, exercised on a global scale, with multiple changing variables. Here we review the techniques and their effects on optimization or proposed optimizations of human mobility in different scales, carried out by data driven, machine learning and model approaches.