Comparing Methods used to Observe Mobility during the COVID-19 Pandemic

     In 2020, an outbreak of the novel coronavirus, SARS-CoV-2 (COVID-19), led to a pandemic that spread rapidly across the globe. In order to control the spread of infectious diseases like COVID-19, public mobility–the amount of a population present in a public space–must be restricted and controlled. By developing methods to track human mobility, researchers are able to quantify data which is useful in ultimately preventing the transmission of infections such as COVID-19.

    The research article titled “Observing Human Mobility Internationally During COVID-19” by Shane Allcroft et al. presents a method to quantify mobility by utilizing computer vision from network cameras and detecting the number of people and vehicles present with the footage. This method uses only public network cameras and data which is available to anyone with Internet access who seeks it out, ensuring that the privacy and protection of individuals remains a priority. The data is gathered from five countries and three U.S. states, then plotted in alignment with a timeline of the area’s policy changes. A leniency index value is determined by taking the Oxford Stringency Index (in which a larger value means more restrictions) from each region and subtracting it from the maximum possible stringency value (in which a larger value means fewer restrictions).

    Results of this study found that mobility in four regions–France, Germany, Austria, and Italy–aligns closely with the leniency index, while in the remaining four regions–Australia, Hawaii, Georgia, and Oregon–the correlations are more varied and do not align as closely with the leniency index. These findings can be explained by observing the different policies and restrictions in each region. The results indicate that the regions with less restrictive policies showed flatter leniency curves and vice versa. The overall findings indicate that visual data may be a useful tool for quantifying how people respond to lockdown policies in different regions.

    A similar study led by Ying Zhou et al., titled “Effects of human mobility restrictions on the spread of COVID-19 in Shenzhen, China: a modelling study using mobile phone data,” develops a model intended to quantify the effects of restricting human mobility in comparison with controlling the spread of COVID-19 in Shenzhen, China. The method used in this study differs from the study by Shane Allcroft et al.; it uses aggregated and anonymous mobile phone data from service providers, following the strict rules and regulations in order to protect the privacy and identities of the mobile phone users. In Shenzhen, nearly 100% of individuals ages 15-65 use mobile phones, which is why the study considered this data to be representative of the mobility patterns of the population as a whole.

    The figures presented in the study show that mobility reduction flattened the peak number of cases and ultimately delayed the forward trajectory of the epidemic. The transmission variables analyzed include the initial number of cases, the duration of the infectious periods, and the R0 value, which represents the basic reproductive number. The overall results of the study indicate that mobility reduction in combination with transmissibility reduction measures would be most effective in controlling COVID-19 outbreaks. 

    In both research articles, a method was developed to quantify human mobility and analyze the data in relation to other variables in order to track and control the progression of the spread of COVID-19 across populations of specific regions. Although different methods were utilized, both studies concluded that there was a relationship between human mobility and the measures taken to reduce transmission, including lockdown policies. Future research on quantifying human mobility may be useful for future pandemics, especially for policymakers, epidemiologists, and scientists working to decrease the transmission of diseases. 

References:

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. (2023). https://doi.org/10.1109/MC.2022.3175751.  

Zhou, Ying; Xu, Renzhe; Hu, Dongsheng; Yue, Yang; Li, Qingquan; Xia, Jizhe. Effects of human mobility restrictions on the spread of COVID-19 in Shenzhen, China: a modelling study using mobile phone data. (2020). https://doi.org/10.1016/S2589-7500(20)30165-5.