Merge pull request #325 from datapartnership/story-understanding-soci…

…oeconomic-disparities-in-mobility-behavior-during-COVID-in-developing-countries

Please review this pull request to publish the Veraset story

content/authors/lorenzo-lucchini/_index.md

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+name: Lorenzo Lucchini 
+---
+
+Lorenzo Lucchini is a postdoctoral research fellow at Bocconi University and a research fellow at the Center for Health Emergencies (Bruno Kessler Foundation). He also serves as a data science consultant for the World Bank, where he applies advanced data analytics to monitor the impact of public policies, such as COVID-19 responses, using human mobility data and other big data sources.
+His research leverages mathematical modelling and statistical analysis to understand coordination and collective behaviour dynamics, with a focus on issues such as vaccine hesitancy, information diffusion, and public health policy. By utilizing data streams from social media and mobile phones, Lorenzo provides insights into how societies adapt to challenges, forming conventions and norms in response to changing circumstances.
+
+Lorenzo holds a PhD in Computer Science, specializing in complex systems and computational social science, and an MSc in Theoretical Physics. His expertise spans topics critical to global development, from behavioural epidemiology to migration and human mobility trends.

content/authors/nancy-lozano-gracia/_index.md

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+name: Nancy Lozano Gracia
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+
+Nancy Lozano Gracia is the Lead Economist for Sustainable Development in Latin America and the Caribbean, with over 20 years of experience in economic policy and research. She is also the co-lead for the Global Solutions Group on Spatial and Territorial Development. Nancy joined the Bank in 2009 and has worked extensively on designing and delivering major operationally relevant analytical pieces across all regions. Some of her recent work includes analysis at the intersection of pollution and competitiveness in cities with a focus on Africa, analysis on urban inequality in Mexico and Colombia, and ongoing work to help countries reorient their productive capacity to become more environmentally friendly and inclusive.  She has worked extensively on designing and using diagnostic tools to improve the understanding of sustainable development challenges and help identify priorities for action. She holds a doctorate in applied economics from University of Illinois and masters in environmental and agricultural and resource economics from University of Maryland and Universidad de los Andes, Colombia.

content/authors/ollin-langle-chimal/_index.md

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+name: Ollin Langle Chimal 
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+
+Ollin is a Postdoctoral Researcher at the University of California, Berkeley. He earned his PhD in Complex Systems and Data Science from the University of Vermont (UVM). His PhD research included applications of data science and mathematical modeling for social good, focusing particularly on the social and economic inequalities following the COVID-19 pandemic, a topic he explored during his internships at The World Bank. He is deeply interested in using data science, machine learning, and mathematical modeling in decision-making processes to develop preventive tools and mitigate inequalities that affect the most vulnerable populations. This interest has also led Ollin to work as a data scientist for the Mexican Ministry of Social Development and THINKMD, a global health startup dedicated to expanding access to health services in low-income countries.

content/authors/samuel-paul-fraiberger/_index.md

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+name: Samuel Paul Fraiberger 
+---
+
+Samuel is a data scientist and program lead for DIME AI at the World Bank, where he focuses on cutting-edge research and analytical tools to scale development impact using AI and machine learning. He established the first-ever partnership between the World Bank and Google.org, through which DIME AI was selected for the inaugural cohort of grantees in the Google.org Accelerator: Generative AI program. His research has appeared in leading academic journals (Science, Science Advances, The Proceedings of the National Academy of Sciences, The Journal of International Economics) and conferences across disciplines (ACL, EMNLP, KDD, ICWSM, TEDx), as we as in the popular press (The Wall Street Journal, The Economist, The Washington Post, Axios). He is also a visiting researcher at New York University’s Center for Data Science

content/updates/understanding-socioeconomic-disparities-in-mobility-behavior-during-COVID-in-developing-countries/index.md

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+title = "Understanding Socioeconomic Disparities in Mobility Behavior During the COVID-19 Pandemic in Developing Countries"
+authors = ["Samuel Paul Fraiberger", "Lorenzo Lucchini", "Ollin Langle Chimal", "Nancy Lozano Gracia"]
+categories = ["Case Study"]
+partner = ["Veraset"]
+dev_partner = ["World Bank"]
+tags = ["Inequality and Shared Prosperity"]
+links = ["https://arxiv.org/abs/2305.06888"]
+date = 2024-11-06T00:00:00Z
+
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+
+The COVID-19 pandemic profoundly disrupted global mobility, but how did it affect different socioeconomic groups, particularly in developing and middle-income countries? 
+
+By combining high-resolution geolocation data from [Veraset](https://www.veraset.com/) with population census data,the World Bank’s Global Practice for Urban, Disaster Risk Management, Resilience and Land (GPURL) collaborated with the Development Economics Vice Presidency (DEC) to uncover the systematic socioeconomic disparities in mobility behavior during the health crisis in developing countries.
+
+
+
+## Challenge
+
+In response to the COVID-19 pandemic, governments and local authorities worldwide implemented non-pharmaceutical interventions such as stay-at-home orders or workplace closures in an effort to limit the spread of the virus. While these interventions significantly reduced people’s mobility, they also exacerbated existing inequalities. People stayed at home from work or school, avoided large gatherings, and refrained from commuting, thereby changing their mobility behavior. Although there are studies on how the pandemic impacted the most vulnerable, little is known about the socioeconomic disparities in mobility behavior across middle-income economies. The lack of detailed, real-time data on how different groups respond to restrictions has sometimes left researchers and policymakers with an incomplete understanding of the impacts of the enacted interventions on different socioeconomic groups. This is particularly problematic in regions where income inequality is often stark, and access to formal and more secure employment, healthcare, and remote work is limited.
+
+
+<figure align="centre">
+    <img src="understanding-socioeconomic-disparities-in-mobility-behavior-during-COVID-in-developing-countries.png" 
+    <figcaption>
+        <center>
+Figure 1:  World Bank
+  </center>
+    </figcaption>
+</figure>
+
+
+## Solution
+
+By leveraging aggregated and anonymized mobile geolocation data with population census data for 6 middle-income countries across 3 continents –Brazil, Colombia, Indonesia, Mexico, the Philippines, and South Africa– between January 1st and December 31st, 2020 (“observation period”), this analysis uncovered common cross-national disparities in the behavioral response to the pandemic across socioeconomic groups.
+
+The core dataset consisted of anonymized movement trajectories of more than 281 million mobile phone devices and was shared by [Veraset](https://www.veraset.com/) through the Development Data Partnership.
+
+In this study the team used a spatiotemporal clustering technique to accurately infer how people allocated their time between their home, their workplace, and other locations they visited. By assigning to each user a wealth proxy derived from census data on the administrative unit where they live, they then characterized the propensity of mobile phone users of various socioeconomic groups to self-isolate at home, relocate to a rural area, or commute to work.
+
+This study found that when the pandemic hit, urban individuals living in low-wealth neighborhoods were less likely to respond by self-isolating at home, relocating to rural areas, or refraining from commuting to work. Among them, those who used to commute to high-wealth places prior to the pandemic stopped commuting 1.4 times more than those who used to commute to low-wealth places. The gap in the behavioral responses between socioeconomic groups persisted during the entire observation period. In particular, among individuals living in low-wealth neighborhoods, those who used to commute to work in high-wealth neighborhoods pre-pandemic were particularly at risk, facing both the reduction in economic activity in the high-wealth neighborhood and being more likely to be affected by public transport closures.
+
+
+
+## Impact
+
+This World Bank study revealed how non-pharmaceutical interventions during the COVID-19 pandemic affected the mobility options of economically vulnerable groups. The data from [Veraset](https://www.veraset.com/) was essential for quantifying human mobility during the global emergency period and providing key insights into the capacity of mobile-users across different socioeconomic groups to respond to the evolution of the pandemic and mobility restrictions. The gap in mobility behavior uncovered by the team not only showed that individuals living in low-wealth neighborhoods faced a greater exposure to the virus, but also highlighted the need for public health authorities to carefully balance the measures aimed at controlling the epidemic with the economic burden on these more vulnerable communities.
+
+As developing countries often lack the capacity to access up-to-date information on individuals, mobile data could help policymakers and international development organizations target aid to the most vulnerable in a timely fashion and respond to future health crises more effectively.
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