Country-wide mobility changes observed using mobile phone data during COVID-19 pandemic

Heiler, Georg; Reisch, Tobias; Hurt, Jan; Forghani, Maryam; Omani, Aida; Hanbury, Allan; Karimipour, Farid

doi:10.48550/arxiv.2008.10064

Cited by 7 publications

(14 citation statements)

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“…Studies on human mobility analysis have used such data to model disease dynamics [5,6,7,8]. During the COVID-19 crisis, researchers, industry, and government agencies have utilized large-scale mobility datasets to evaluate the effects of NPIs in various countries, including the United States [15,9,14], the United Kingdom [17], Italy [13,12,18,26], China [10,11], Sweden [16], Germany [19], Spain [27], Austria [28], and Japan [20,29]. None of these studies have attempted to integrate the analysis of web search queries to predict COVID-19 outbreak hotspots.…”

Section: Mobility Analysis During Covid-19mentioning

confidence: 99%

Early warning of COVID-19 hotspots using human mobility and web search query data

Yabe

Tsubouchi²,

Sekimoto

et al. 2022

Computers, Environment and Urban Systems

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COVID-19 has disrupted the global economy and well-being of people at an unprecedented scale and magnitude. To contain the disease, an effective early warning system that predicts the locations of outbreaks is of crucial importance. Studies have shown the effectiveness of using large-scale mobility data to monitor the impacts of non-pharmaceutical interventions (e.g., lockdowns) through population density analysis. However, predicting the locations of potential outbreak occurrence is difficult using mobility data alone. Meanwhile, web search queries have been shown to be good predictors of the disease spread. In this study, we utilize a unique dataset of human mobility trajectories (GPS traces) and web search queries with common user identifiers (> 450 K users), to predict COVID-19 hotspot locations beforehand. More specifically, web search query analysis is conducted to identify users with high risk of COVID-19 contraction, and social contact analysis was further performed on the mobility patterns of these users to quantify the risk of an outbreak. Our approach is empirically tested using data collected from users in Tokyo, Japan. We show that by integrating COVID-19 related web search query analytics with social contact networks, we are able to predict COVID-19 hotspot locations 1–2 weeks beforehand, compared to just using social contact indexes or web search data analysis. This study proposes a novel method that can be used in early warning systems for disease outbreak hotspots, which can assist government agencies to prepare effective strategies to prevent further disease spread. Human mobility data and web search query data linked with common IDs are used to predict COVID-19 outbreaks. High risk social contact index captures both the contact density and COVID-19 contraction risks of individuals. Real world data was collected from 200 K individual users in Tokyo during the COVID-19 pandemic. Experiments showed that the index can be used for microscopic outbreak early warning.

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Section: Mobility Analysis During Covid-19mentioning

confidence: 99%

Early warning of COVID-19 hotspots using human mobility and web search query data

Yabe

Tsubouchi²,

Sekimoto

et al. 2022

Computers, Environment and Urban Systems

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“…and it thus represents a distance to the center of all the places of stay during that day t weighted by the lengths of the stay duration at the different places. Details are available and especially a description of how the large quantity of data was handled is available in [28].…”

Section: Mobile-phone Datamentioning

confidence: 99%

“…We have defined an aggregation method to understand the overall aggregated mobility of the whole population. Our data set as well as the aggregation, anonymization approach and the various phases of the lock-down are outlined in detail in [28].…”

Section: Introductionmentioning

confidence: 99%

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The impact of COVID-19 on relative changes in aggregated mobility using mobile-phone data

Heiler¹,

Hanbury²,

Filzmoser³

2020

Preprint

Self Cite

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Evaluating relative changes leads to additional insights that would remain hidden when only evaluating absolute changes. We analyze a dataset describing the mobility of mobile phones in Austria before, during COVID-19 lock-down measures until recently.By applying compositional data analysis we show that formerly hidden information becomes available: we see that the elderly population groups increase relative mobility and that the younger groups, especially on weekends, also do not decrease their mobility as much as the others.

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“…Santamaria et al (2020) investigated several mobility indices such as internal and inward demand based on the origin-destination matrix obtained using mobile phone data. Heiler et al (2020) analyzed the influence of the lockdown in Austria using mobile phone data and various measures such as a clustering index. However, no one has investigated how travel patterns changed over time and whether they returned to those prior to the implementation of measures such as lockdowns.…”

Section: Introductionmentioning

confidence: 99%

Understanding Changes in Travel Patterns during the COVID-19 Outbreak in the Three Major Metropolitan Areas of Japan

Dantsuji¹,

Sugishita²,

Fukuda³

2020

Preprint

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Unlike the lockdown measures taken in some countries or cities, the Japanese government declared a "State of Emergency" (SOE) under which people were only requested to reduce their contact with other people by at least 70 %, while some local governments also implemented their own mobility-reduction measures that had no legal basis. The effects of these measures are still unclear. Thus, in this study, we investigate changes in travel patterns in response to the COVID-19 outbreak and related policy measures in Japan using longitudinal aggregated mobile phone data. Specifically, we consider daily travel patterns as networks and analyze their structural changes by applying a framework for analyzing temporal networks used in network science. The cluster analysis with the network similarity measures across different dates showed that there are six main types of mobility patterns in the three major metropolitan areas of Japan: (I) weekends and holidays prior to the COVID-19 outbreak, (II) weekdays prior to the COVID-19 outbreak, (III) weekends and holidays before and after the SOE, (IV) weekdays before and after the SOE, (V) weekends and holidays during the SOE, and (VI) weekdays during the SOE. It was also found that travel patterns might have started to change from March 2020, when most schools were closed, and that the mobility patterns after the SOE returned to those prior to the SOE. Interestingly, we found that after the lifting of the SOE, travel patterns remained similar to those during the SOE for a few days, suggesting the possibility that self-restraint continued after the lifting of the SOE. Moreover, in the case of the Nagoya metropolitan area, we found that people voluntarily changed their travel patterns when the number of cases increased.

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Country-wide mobility changes observed using mobile phone data during COVID-19 pandemic

Cited by 7 publications

References 0 publications

Early warning of COVID-19 hotspots using human mobility and web search query data

Early warning of COVID-19 hotspots using human mobility and web search query data

The impact of COVID-19 on relative changes in aggregated mobility using mobile-phone data

Understanding Changes in Travel Patterns during the COVID-19 Outbreak in the Three Major Metropolitan Areas of Japan

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