Human Mobility During COVID-19

           Human mobility is a very hard metric to measure because it incorporates so many different factors from societal, and environmental, to emotional. But they all stem from important cognitive approaches in the brain that we can better understand to use for our own benefit. Specific to a pandemic such as the outbreak of COVID-19, understanding the implications of human mobility can make the difference in saving lives and containing a spread as best as possible. But how can human mobility be monitored when there are so many outlying factors between communities of people? Namely, human people are very variable in how they react to circumstances and policies; in the context of an outbreak, understanding these reactions can be very effective for controlling these reactions in the future. Research using computer vision has helped mitigate these concerns and has given us an eye into the lens of human mobility and how we can use the information to impact such things as legislation and policy making. 

To exemplify this, the article "Observing Human Mobility Internationally During COVID-19” employs visual data that is analyzed consisting of changes in human mobility during the time frame to observe the effectiveness of lockdown procedures concerning the spread of disease (COVID-19). Human variability poses many concerns for collecting data in this format and for this end. At the same time, there are many difficulties with collecting data for something like this, namely how large the sample size is and the risk for observers. The researchers devised the idea to use computer vision (public network cameras) to observe on a grand scale. They observed trends such as the relation of the leniency index concerning mobility(where mobility rose and decreased when laws were enforced). Analyzing the data showed much correlation between how restrictive a country was and how mobility was affected, except for certain outliers that did not follow the index as closely, possibly with much lower restrictions. The overall conclusion of the research showed that many different cultures had their own distinctions when it came to how much mobility was suppressed despite the heightened amount of policymaking and law enforcement. We saw this in our own experience in typically more American areas that had much higher mobility when compared to areas abroad in European and Asian sectors. This information can be very useful to account for future policymaking if say another pandemic were to occur. How do cultures vary in their mobilities, and how do we mitigate concerns of public safety by addressing the social dynamics of people in the way they think from culture to culture?

From the mobility study utilizing computer vision much can be garnered about cultural and societal differences humans display in terms of operation during an outbreak. But there is also a lot to be said about individual movement, urban movement in particular, and how to monitor behavior that is more individualist in nature instead of observing movement as a collective. Another article, “Mobility in pandemic times: Exploring changes and long-term effects of COVID-19 on urban mobility behavior”, focuses on urban mobility behavior during the pandemic and how this can play into the long term. Being that mobility is such an implicating factor in the spread of a virus, many individuals were urged to reconsider their personal behaviors, however as we observed from the first article this was not always the case. This article, on a smaller scale, examined Berlin residents over a 20-month-long period to see how travel patterns changed from before the pandemic to during the pandemic period. An average of 20% of distances were observed, and an 11% reduction in frequency, while on the public transportation end, a long-term reduction was seen of 50%. Most interestingly, bicycle trips were seen increasing in frequency by an overwhelming 53% in the long term, and distances increased by 117%. This information greatly benefits us as it tells us how the public reacts to mobility in terms of an urban landscape. It can suggest to us that some cultures and landscapes may be more reliant on alternate forms of transport such as cycling so lawmaking could be geared towards opening that avenue to decrease overall risk. Through the synthesis of both articles, much can be gathered specifically on how to understand differences between cultures and urban landscapes.

The use of computer vision is very useful because it allows for very large-scale monitoring but it does not address how individual participants in a society operate and through what means. By combining methods from both articles lawmaking and policies could be addressed in a most appropriate manner to mitigate risks in public safety and health. Between the two articles, the number one implication that should be monitored is a difference in culture and how that affects a person’s ability to interact with his/her environment. The second article specifically discusses longitudinal developments and that long-term assessment is most important in monitoring a disease such as a pandemic being that it is an “enduring and dynamic phenomenon”. The first article also touches base on this concept by understanding that the differences in mobility were not continuous between all areas tested and were very prone to alteration based on cultural attitudes that coincided with policy-making at the time of proportionate change. Concluding the thoughts that both articles present, human mobility is a very interesting phenomenon, and understanding the way an individual operates within their culture and society can serve great importance towards further research, specifically research that will benefit public health and safety.

Allcroft, Shane; Metwaly, Mohammed; Berg, Zachery; Ghodgaonkar, Isha; Bordwell, Fischer; Zhao, XinXin; Liu, Xinglei; Xu, Jiahao; Chakraborty, Subhankar; Banna, Vishnu; Chinnakotla, Akhil; Goel, Abhinav; Tung, Caleb; Kao, Gore; Zakharov, Wei; Shoham, David A.; Thiruvathukal, George K.; and Lu, Yung-Hsiang. "Observing Human Mobility Internationally During COVID-19". IEEE Computer, 2023. Retrieved from Loyola eCommons, Computer Science: Faculty Publications and Other Works

http://10.1109/MC.2022.3175751

Kellermann R, Sivizaca Conde D, Rößler D, Kliewer N, Dienel HL. Mobility in pandemic times: Exploring changes and long-term effects of COVID-19 on urban mobility behavior. Transp Res Interdiscip Perspect. 2022 Sep;15:100668. doi: 10.1016/j.trip.2022.100668. Epub 2022 Aug 11. PMID: 35971332; PMCID: PMC9365868.

https://doi.org/10.1016/j.trip.2022.100668