FERRES, LEONARDO ADRIÁN

Preferred name
FERRES, LEONARDO ADRIÁN
Official Name
Ferres, Leonardo Adrian
Main Affiliation
Email
lferres@udd.cl
ORCID
0000-0002-5899-9051
Scopus Author ID
14055701500
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    Dataset - Evaluation of home detection algorithms on mobile phone data using individual-level ground truth
    (GitHub, 2020)
    BRAVO CELEDÓN, MARÍA LORETO  
    ;
    FERRES, LEONARDO ADRIÁN  
    This is the implementation of the 5 algorithms described in Vanhoof, M., Reis, F., Ploetz, T., & Smoreda, Z. (2018). Assessing the quality of home detection from mobile phone data for official statistics. In Journal of Official Statistics (Vol. 34, pp. 935–960). https://doi.org/10.2478/jos-2018-0046 that we used in our paper.
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    Nontraditional Data in Pandemic Preparedness and Response: Identifying and Addressing First- and Last-Mile Challenges
    (JMIR Publications Inc., 2026-04-29)
    Mattia Mazzoli
    ;
    Irma Varela-Lasheras
    ;
    Sónia Namorado
    ;
    Constantino Pereira Caetano
    ;
    Andreia Leite
    <jats:title>Abstract</jats:title> <jats:p>The COVID-19 pandemic served as an important test case of complementing traditional public health data with nontraditional data, such as mobility traces, social media activity, and wearable data, to inform real-time decision-making. Drawing on an expert workshop and a targeted survey of epidemic modelers in Europe, this study assesses the promise and the persistent limitations of such data in pandemic preparedness and response. We distinguish between “first-mile” challenges (obstacles to accessing and harmonizing data) and “last-mile” challenges (difficulties in translating insights into actionable policy interventions). The expert workshop, convened in March 2024 in Brussels, brought together 50 participants, including public health professionals, data scientists, policymakers, and industry leaders, to reflect on lessons learned and define strategies for better integration of nontraditional data into epidemic modeling and policymaking. The accompanying survey, gathering experiences from 29 modelers, offers empirical evidence of the barriers faced by modelers during the COVID-19 pandemic and highlights areas where key data were unavailable or underused. The experiences collected through the survey and workshop resulted in ten key actions and three overarching recommendations for public entities, data providers, and stakeholders. Our findings reveal ongoing issues with data access, quality, and interoperability, as well as institutional and cognitive barriers to evidence-based decision-making. Approximately 66% of all datasets had at least one access problem, with data sharing reluctance for nontraditional sources being double that of traditional data (30% vs 15%). Only 10% of respondents reported that they could use all the data they needed. These limitations included issues related to timeliness and granularity of data, as well as issues with linkage, comparability, and biases. To overcome these hurdles, we propose a set of enabling mechanisms, including data inventories, standardization protocols, simulation exercises, data stewardship roles, and data collaboratives. For first-mile challenges, solutions focus on technical and legal frameworks for data access. For last-mile challenges, we recommend fusion centers, decision accelerator laboratories, and networks of scientific ambassadors to bridge the gap between analysis and action. We argue that realizing the full value of nontraditional data requires a sustained investment in institutional readiness, cross-sectoral collaboration, and a shift toward a culture of data solidarity. Grounded in the lessons of the COVID-19 pandemic, the study can be used to design a roadmap for using nontraditional data to confront a broader array of public health emergencies, from climate shocks to humanitarian crises.</jats:p>
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    Scopus© Citations 38  3
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    Evaluation of home detection algorithms on mobile phone data using individual-level ground truth
    (2021)
    Luca Pappalardo
    ;
    FERRES, LEONARDO ADRIÁN  
    ;
    Manuel Sacasa
    ;
    Ciro Cattuto
    ;
    BRAVO CELEDÓN, MARÍA LORETO  
    Inferring mobile phone users’ home location, i.e., assigning a location in space to a user based on data generated by the mobile phone network, is a central task in leveraging mobile phone data to study social and urban phenomena. Despite its widespread use, home detection relies on assumptions that are difficult to check without ground truth, i.e., where the individual who owns the device resides. In this paper, we present a dataset that comprises the mobile phone activity of sixty-five participants for whom the geographical coordinates of their residence location are known. The mobile phone activity refers to Call Detail Records (CDRs), eXtended Detail Records (XDRs), and Control Plane Records (CPRs), which vary in their temporal granularity and differ in the data generation mechanism. We provide an unprecedented evaluation of the accuracy of home detection algorithms and quantify the amount of data needed for each stream to carry out successful home detection for each stream. Our work is useful for researchers and practitioners to minimize data requests and maximize the accuracy of the home antenna location.
    Scopus© Citations 35  3
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    Predicting city poverty using satellite imagery
    (2019-06-01)
    Piaggesi, Simone
    ;
    Gauvin, Laetitia
    ;
    Tizzoni, Michele
    ;
    Adler, Natalia
    ;
    Verhulst, Stefaan
    Reliable data about socio-economic conditions of individuals, such as health indexes, consumption expenditures and wealth assets, remain scarce for most countries. Traditional methods to collect such data include on site surveys that can be expensive and labour intensive. On the other hand, remote sensing data, such as high-resolution satellite imagery, are becoming largely available. To circumvent the lack of socio-economic data at high granularity, computer vision has already been applied successfully to raw satellite imagery sampled from resource poor countries. In this work we apply a similar approach to the metropolitan areas of five different cities in North and South America, starting from pre-trained convolutional models used for poverty mapping in developing regions. Applying a transfer learning process we estimate household income from visual satellite features. The urban environment we consider is characterized by different features with respect to the resource-poor training environment, such as the high heterogeneity in population density. By leveraging both official and crowd-sourced data at city scale, we show the feasibility of estimating the socio-economic conditions of different neighborhoods from satellite data.
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