Mobility data as a proxy for epidemic measures

Kishore, Nishant

doi:10.1038/s43588-021-00127-7

Cited by 5 publications

(3 citation statements)

References 5 publications

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“…Finally, the use of novel digital sources requires a careful understanding of their limitations and their scope. For instance, mobile phone-derived mobility metrics have been proven useful to understand the dynamics of COVID-19 in the early phase of the outbreak, however, the relationship between mobility indicators and epidemic outcomes is not straightforward (Kishore, 2021 ). While mobile phone data are clearly useful to measure changes in human behavior and link them with epidemic dynamics, such link often varies over time, and understanding this varying relationship poses significant challenges to scholars and policymakers who may want to use mobile phone data to evaluate the effectiveness of interventions or forecast future epidemic trajectories.…”

Section: Public Healthmentioning

confidence: 99%

Crime, inequality and public health: a survey of emerging trends in urban data science

et al. 2023

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Urban agglomerations are constantly and rapidly evolving ecosystems, with globalization and increasing urbanization posing new challenges in sustainable urban development well summarized in the United Nations' Sustainable Development Goals (SDGs). The advent of the digital age generated by modern alternative data sources provides new tools to tackle these challenges with spatio-temporal scales that were previously unavailable with census statistics. In this review, we present how new digital data sources are employed to provide data-driven insights to study and track (i) urban crime and public safety; (ii) socioeconomic inequalities and segregation; and (iii) public health, with a particular focus on the city scale.

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Section: Public Healthmentioning

confidence: 99%

Crime, inequality and public health: a survey of emerging trends in urban data science

et al. 2023

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“…Several reasons have been proposed to explain the varying relationship between mobility metrics and epidemic indicators ( 29 ). Mobility metrics are generally derived from raw mobile positioning data through complex and customized processing pipelines that can significantly vary across data providers ( 36 ). How raw data are processed, and the specific definitions of mobility metrics can significantly impact their interpretation with respect to epidemic variables ( 37 ).…”

Section: Introductionmentioning

confidence: 99%

The limits of human mobility traces to predict the spread of COVID-19: A transfer entropy approach

Delussu,

Tizzoni,

Gauvin

2023

PNAS Nexus

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Mobile phone data have been widely used to model the spread of COVID-19, however, quantifying and comparing their predictive value across different settings is challenging. Their quality is affected by various factors and their relationship with epidemiological indicators varies over time. Here, we adopt a model-free approach based on transfer entropy to quantify the relationship between mobile phone-derived mobility metrics and COVID-19 cases and deaths in more than 200 European subnational regions. Using multiple data sources over a one-year period, we found that past knowledge of mobility does not systematically provide statistically significant information on COVID-19 spread. Our approach allows us to determine the best metric for predicting disease incidence in a particular location, at different spatial scales. Additionally, we identify geographic and demographic factors, such as users’ coverage and commuting patterns, that explain the (non)observed relationship between mobility and epidemic patterns. Our work provides epidemiologists and public health officials with a general – not limited to COVID-19 – framework to evaluate the usefulness of human mobility data in responding to epidemics.

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“…The effective communication of research and real-time data is also essential for public education (Röddiger et al, 2021), which affects participation in mitigation interventions and efforts. Mobility data has further been adapted as a proxy to understand individual behavior and geographic movement during the pandemic, also highlighting best practices regarding ethical data re-use (Ågren et al, 2021; Kishore, 2021). However, using data to create public value requires a unified data infrastructure and governance principles.…”

Section: Introductionmentioning

confidence: 99%

Responding to the coronavirus disease-2019 pandemic with innovative data use: The role of data challenges

Danemayer

Young

Green³

et al. 2023

Data & Policy

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Innovative, responsible data use is a critical need in the global response to the coronavirus disease-2019 (COVID-19) pandemic. Yet potentially impactful data are often unavailable to those who could utilize it, particularly in data-poor settings, posing a serious barrier to effective pandemic mitigation. Data challenges, a public call-to-action for innovative data use projects, can identify and address these specific barriers. To understand gaps and progress relevant to effective data use in this context, this study thematically analyses three sets of qualitative data focused on/based in low/middle-income countries: (a) a survey of innovators responding to a data challenge, (b) a survey of organizers of data challenges, and (c) a focus group discussion with professionals using COVID-19 data for evidence-based decision-making. Data quality and accessibility and human resources/institutional capacity were frequently reported limitations to effective data use among innovators. New fit-for-purpose tools and the expansion of partnerships were the most frequently noted areas of progress. Discussion participants identified building capacity for external/national actors to understand the needs of local communities can address a lack of partnerships while de-siloing information. A synthesis of themes demonstrated that gaps, progress, and needs commonly identified by these groups are relevant beyond COVID-19, highlighting the importance of a healthy data ecosystem to address emerging threats. This is supported by data holders prioritizing the availability and accessibility of their data without causing harm; funders and policymakers committed to integrating innovations with existing physical, data, and policy infrastructure; and innovators designing sustainable, multi-use solutions based on principles of good data governance.

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Mobility data as a proxy for epidemic measures

Cited by 5 publications

References 5 publications

Crime, inequality and public health: a survey of emerging trends in urban data science

Crime, inequality and public health: a survey of emerging trends in urban data science

The limits of human mobility traces to predict the spread of COVID-19: A transfer entropy approach

Responding to the coronavirus disease-2019 pandemic with innovative data use: The role of data challenges

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