A big-data driven approach to analyzing and modeling human mobility trend under non-pharmaceutical interventions during COVID-19 pandemic

Hu, Songhua; Xiong, Chenfeng; Yang, Mofeng; Younes, Hannah; Luo, Weiyu; Zhang, Lei

doi:10.1016/j.trc.2020.102955

Cited by 145 publications

(118 citation statements)

References 34 publications

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“…The model includes sequential restrictions introduced by the Italian Government broken down by provinces, which resulted in partial or complete lockdown and mobility restrictions. Similar, space-oriented national or regional study results using human mobility data are also available for places such as the USA, Japan, China or Hong-Kong [ 3 – 8 ].…”

Section: Introductionmentioning

confidence: 99%

Big data insight on global mobility during the Covid-19 pandemic lockdown

Sadowski

Galar²,

Walasek

et al. 2021

J Big Data

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The Covid-19 pandemic that began in the city of Wuhan in China has caused a huge number of deaths worldwide. Countries have introduced spatial restrictions on movement and social distancing in response to the rapid rate of SARS-Cov-2 transmission among its populations. Research originality lies in the taken global perspective revealing indication of significant relationships between changes in mobility and the number of Covid-19 cases. The study uncovers a time offset between the two applied databases, Google Mobility and John Hopkins University, influencing correlations between mobility and pandemic development. Analyses reveals a link between the introduction of lockdown and the number of new Covid-19 cases. Types of mobility with the most significant impact on the development of the pandemic are “retail and recreation areas", "transit stations", "workplaces" "groceries and pharmacies”. The difference in the correlation between the lockdown introduced and the number of SARS-COV-2 cases is 81%, when using a 14-day weighted average compared to the 7-day average. Moreover, the study reveals a strong geographical diversity in human mobility and its impact on the number of new Covid-19 cases.

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Section: Introductionmentioning

confidence: 99%

Big data insight on global mobility during the Covid-19 pandemic lockdown

Sadowski

Galar²,

Walasek

et al. 2021

J Big Data

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“…Moreover, introducing restrictions on the mobility of infected patients or on citizens in high-risk regions (such as lockdown) could delay and decrease the peak of the epidemic in the early stage of a COVID-19 outbreak, as examined in Shenzhen (Zhou et al, 2020), Tokyo (Yabe et al, 2020), and Italy and France (Santamaria et al, 2020). However, it was also found that restricting human mobility has only limited value, given that spatial heterogeneity has been evidenced in counties in the US (Hu et al, 2021) and in regions of the UK (Cheng et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Revealing spatiotemporal transmission patterns and stages of COVID-19 in China using individual patients’ trajectory data

Cheng

Liu

et al. 2021

Comput.Urban Sci.

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Gauging viral transmission through human mobility in order to contain the COVID-19 pandemic has been a hot topic in academic studies and evidence-based policy-making. Although it is widely accepted that there is a strong positive correlation between the transmission of the coronavirus and the mobility of the general public, there are limitations to existing studies on this topic. For example, using digital proxies of mobile devices/apps may only partially reflect the movement of individuals; using the mobility of the general public and not COVID-19 patients in particular, or only using places where patients were diagnosed to study the spread of the virus may not be accurate; existing studies have focused on either the regional or national spread of COVID-19, and not the spread at the city level; and there are no systematic approaches for understanding the stages of transmission to facilitate the policy-making to contain the spread.To address these issues, we have developed a new methodological framework for COVID-19 transmission analysis based upon individual patients’ trajectory data. By using innovative space–time analytics, this framework reveals the spatiotemporal patterns of patients’ mobility and the transmission stages of COVID-19 from Wuhan to the rest of China at finer spatial and temporal scales. It can improve our understanding of the interaction of mobility and transmission, identifying the risk of spreading in small and medium-sized cities that have been neglected in existing studies. This demonstrates the effectiveness of the proposed framework and its policy implications to contain the COVID-19 pandemic.

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“…Travel patterns appear to have a huge impact in the propagation of the virus, requiring a combination of sensible public policies and the willing collaboration of the community, as demonstrated by the case of Hong Kong [ 45 ]. Big data extensive studies have found that imposed public policies play a small role in the reduction of mobility [ 46 ]. In fact, the main factor contributing to reductions in mobility appears to be the fear of contagion [ 47 ].…”

Section: Discussionmentioning

confidence: 99%

Non-Intrusive Assessment of COVID-19 Lockdown Follow-Up and Impact Using Credit Card Information: Case Study in Chile

Muñoz-Cancino

Ríos

Goić

et al. 2021

IJERPH

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In this paper, we propose and validate with data extracted from the city of Santiago, capital of Chile, a methodology to assess the actual impact of lockdown measures based on the anonymized and geolocated data from credit card transactions. Using unsupervised Latent Dirichlet Allocation (LDA) semantic topic discovery, we identify temporal patterns in the use of credit cards that allow us to quantitatively assess the changes in the behavior of the people under the lockdown measures because of the COVID-19 pandemic. An unsupervised latent topic analysis uncovers the main patterns of credit card transaction activity that explain the behavior of the inhabitants of Santiago City. The approach is non-intrusive because it does not require the collaboration of people for providing the anonymous data. It does not interfere with the actual behavior of the people in the city; hence, it does not introduce any bias. We identify a strong downturn of the economic activity as measured by credit card transactions (down to 70%), and thus of the economic activity, in city sections (communes) that were subjected to lockdown versus communes without lockdown. This change in behavior is confirmed by independent data from mobile phone connectivity. The reduction of activity emerges before the actual lockdowns were enforced, suggesting that the population was spontaneously implementing the required measures for slowing virus propagation.

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A big-data driven approach to analyzing and modeling human mobility trend under non-pharmaceutical interventions during COVID-19 pandemic

Cited by 145 publications

References 34 publications

Big data insight on global mobility during the Covid-19 pandemic lockdown

Big data insight on global mobility during the Covid-19 pandemic lockdown

Revealing spatiotemporal transmission patterns and stages of COVID-19 in China using individual patients’ trajectory data

Non-Intrusive Assessment of COVID-19 Lockdown Follow-Up and Impact Using Credit Card Information: Case Study in Chile

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