The objective of this study was to assess the feasibility of using a user-centered chatbotfor collecting linked data to study overweight and obesity causes ina target population. In total 980 people participated in the feasibility study organized in three studies: (1) within a group of university students (88 participants), (2) in a small town (422 participants), and (3) within a university community (470 participants). We gathered self-reported data through the Wakamola chatbot regarding participants diet, physical activity, social network, living area, obesity-associated diseases, and sociodemographic data. For each study, we calculated the mean Body Mass Index (BMI) and number of people in each BMI level. Also, we defined and calculated scores (1–100 scale) regarding global health, BMI, alimentation, physical activity and social network. Moreover, we graphically represented obesity risk for living areas and the social network with nodes colored by BMI.
For those of you who have missed it, here is the interview of Lo que AI que oir (What AI has to hear) (The Spain AI Podcast) that they did to Oscar Garibo and me. Thanks to Spain AI, Alba Pérez Fernández, Karen Troiano and Alberto Julián.
In this interview, we speak about our participation in the XPRIZE Challenge, but we also had time to speak about Mathematics, AI, and life. You can hear it in Spanish here:
Obesity and overweight are a serious health problem worldwide with multiple and connected causes. Simultaneously, chatbots are becoming increasingly popular as a way to interact with users in mobile health apps. Our study reports the user-centered design and feasibility study of a chatbot to collect linked data to support the study of individual and social overweight and obesity causes in populations. We first studied the users’ needs and gathered users’ graphical preferences through an open survey on 52 wireframes designed by 150 design students; it also included questions about sociodemographics, diet and activity habits, the need for overweight and obesity apps, and desired functionality. We also interviewed an expert panel. We then designed and developed a chatbot. Finally, we conducted a pilot study to test feasibility. With this work, we have shown that the Telegram chatbot Wakamola is a feasible tool to collect data from a population about sociodemographics, diet patterns, physical activity, BMI, and specific diseases. Besides, the chatbot allows the connection of users in a social network to study overweight and obesity causes from both individual and social perspectives. You can access to this work here S. Asensio-Cuesta et al.
A User-Centered Chatbot (Wakamola) to Collect Linked Data in Population Networks to Support Studies of Overweight and Obesity Causes: Design and Pilot Study. JMIR Med Inform 2021;9(4):e17503
Our team VALENCIA IA4COVID, coleaded by Nuria Oliver and me, has won 500k Pandemic Response Challenge, organized by XPRIZE Foundation and supported by Cognizant. The $500K Pandemic Response Challenge, required teams to build effective data-driven AI systems capable of accurately predicting COVID-19 transmission rates and prescribing intervention and mitigation measures that, with testing in “what-if” scenarios, were shown to minimize infection rates as well as negative economic impacts.
Our group is made up of fourteen experts from the Universities and research centers of the Valencian Community. Our model successfully forecasted epidemiological evolution through their use of AI and data science and provided decision makers with the best prescriptor models to produce non-pharmaceutical intervention plans that minimize the number of infections while minimizing the stringency of the interventions.
Our team VALENCIA IA4COVID has progressed to the 2nd phase of the Pandemic Response Challenge, organized by XPRIZE Foundation and supported by Cognizant. This is a $500K, a four-month challenge that focuses on the development of data-driven AI systems to predict COVID-19 infection rates and prescribe Intervention Plans (IPs) that regional governments, communities, and organizations can implement to minimize harm when reopening their economies. Our group is made up of fourteen experts from the Universities and research centers of the Valencian Community and it is leaded by Nuria Oliver and me. We have all been working intensively since the beginning of the pandemic, altruistically and using the resources available to us in our respective institutions and with the occasional philanthropic collaboration of some companies.
Our model is among the three best models in the competition in MAE Mean Rank, leading in ASIA and in the top 5 of EUROPE in MAE per 100k habitants.
You can see our predictions here. The model has not been updated since its release on December 22nd.
Our paper Reversible Self-Replication of Spatio-Temporal Kerr Cavity Patterns has been recently accepted for publication in Physical Review Letters (PRL), with IF = 8.385, jointly with Salim B. Ivars, Yaroslav V. Kartashov, Lluis Torner, and Carles Milián. In this work, we uncover a novel and robust phenomenon that causes the gradual self-replication of spatiotemporal Kerr cavity patterns in cylindrical microresonators. These patterns are inherently synchronized multi-frequency combs. Under proper conditions, the axially-localized nature of the patterns leads to a fundamental drift instability that induces transitions amongst patterns with a different number of rows. Self-replications, thus, result in the stepwise addition or removal of individual combs along the cylinder’s axis. Transitions occur in a fully reversible and, consequently, deterministic way. The phenomenon puts forward a novel paradigm for Kerr frequency comb formation and reveals important insights into the physics of multi-dimensional nonlinear patterns.
Our recent paper Potential limitations in COVID-19 machine learning due to data source variability: A case study in the nCov2019 dataset has been accepted for publication in J Am Med Inform Assoc. (JAMIA, IF 4.112). We study whether the lack of representative coronavirus disease 2019 (COVID-19) data is a bottleneck for reliable and generalizable machine learning. Data sharing is insufficient without data quality, in which source variability plays an important role. We showcase and discuss potential biases from data source variability for COVID-19 machine learning. Our results are based in the publicly available nCov2019 dataset, including patient-level data from several countries. We aimed to the discovery and classification of severity subgroups using symptoms and comorbidities. We show that cases from the 2 countries with the highest prevalence were divided into separate subgroups with distinct severity manifestations. This variability can reduce the representativeness of training data with respect the model target populations and increase model complexity at risk of overfitting.
During the last months, I have been participating in the ANDI Challenge, together with my Ph.D. student Óscar Garibo. Since Albert Einstein provided a theoretical foundation for Robert Brown’s observation of the movement of particles within pollen grains suspended in water, significant deviations from the laws of Brownian motion have been uncovered in a variety of animate and inanimate systems, from biology to the stock market. Anomalous diffusion, as it has come to be called, is connected to non-equilibrium phenomena, flows of energy and information, and transport in living systems.
The challenge consists of three main tasks, each of them on 3 Dimensions:
- Task 1 – Inference of the anomalous diffusion exponent α.
- Task 2 – Classification of the diffusion model.
- Task 3 – Segmentation of trajectories.
We got the first position in Task 1 (1D) and the second position in Task 2 (1D). We also get the 3rd position in Task 2 (3d) and the 4th position in Task 2 (2D).
Article d’opinió al voltant de la disponibilitat de dades al voltant de la evolució de la pandèmia del COVID-19. Després de portar uns mesos analitzant dades sobre l’evolució de la COVID-19 hem comprovat que algunes coses han millorat i que ha augmentat la disponibilitat de les dades i l’accés a aquests.
Comptar cada vegada amb ciutadans amb una major cultura de l’ús de les dades ens permet ser més corresponsables en la presa de decisions individuals i poder recolzar amb major criteri les decisions dels organismes oficials.
Pots trobar l’article sencer en https://www.viaempresa.cat/opinio/conejero-upv-dades-dades-dades_2142814_102.html
On November 12th, I got the position of Full Professor at the Dept. of Applied Mathematics of UPV. It has been a long and winding trip of almost 22 years since I started my PhD. I did not think about this when I started. I did not dream of it. I neither consider it as the measure of academic success.
After getting the tenure position in 2009, I did not think about it for some time. However, 8 years ago I simply thought one day: Maybe I can get it, why not? Essentially, it is to keep on working day after day and getting more results. And I started to keep on working even at night or before dawn. Later, three years ago in the lobby of a hotel in Boston, reading the new criteria for becoming a full professor I thought, I absolutely will get it.
Many people have asked me if I am happier or if I have celebrated. Truth be told that happiness only lasted some days, later you return to your daily tasks and almost forget it. Besides, celebrations are very limited in these strange times, so almost anything has changed in my life after it.
Does it worth all the time invested? I honestly do not know. I enjoyed quite a lot during the path, but for the things you meet along the way, not because of the position, and to be focused on the path you missed many things. You can never have it all and you have to decide.
We Alberto Conejero (Instituto Universitario de Matemática Pura y Aplicada, IUMPA) and Miguel Rebollo (of the Valencian Research Institute for Artificial Intelligence, VRAIN) of UPV are part of the Data Science Working Group in the Fight against COVID-19 , of the Commissioner for the Presidency of the Generalitat Valenciana on Strategy for Artificial Intelligence and Data Sciences against COVID-19. News appeared in UPV, in El Periodic newspaper and in RUVID website.
Since April 2020, I have been involved in the Data Science Group COVID-19 of the València Region under the supervision of Nuria Oliver. This is a multidisciplinary team of volunteers that work side by side with the General Director of Analysis and Public Policies of the Presidency of the RValencia Region Government. The analysis for COVID-19 is coordinated with the Ministry of Health and the rest of the Councils involved. This working group is led by Nuria Oliver, Commissioner of the Presidency of the Generalitat for the Valencian Strategy for Artificial Intelligence and, especially, for the coordination of data intelligence before the COVID-19 epidemic in the Valencia Region.
They are part of the group of experts from the Jaume I University, the University of Valencia, the Polytechnic University of Valencia, the Miguel Hernández University, the University of Alacant, the CEU Cardenal Herrera University, Fisabio, and Microsoft, with the collaboration of Esri, the INE, the Secretary of State for Artificial Intelligence and the three most important mobile phone companies in the country.
This group is divided into three priority areas with their respective work coordinators: (1) analysis, visualization, and modeling of mobility data, (2) epidemiological models and (3) data science applied to COVID-19. There, I work in epidemiological models with Antonio Falcó, Miguel Rebollo, Miguel A. Lozano, Emilio Sansano, Xavier Barber, and Francisco Escolano.
This is the web page of our data research group http://infocoronavirus.gva.es/es/grup-de-ciencies-de-dades-del-covid-19-de-la-comunitat-valenciana
Within the context of the collaborative university project «Data Literacy in Context» (DaLiCo), the first summer school with international students as participants and lecturers from all partner countries of the project took place in the last week of September. It was organized by the University of Applied Sciences Hamburg (HAW Hamburg), in a team led by Prof. Christine Gläser (project coordinator, Department Information) with contributions from the partner universities Stichting Hogeschool Utrecht (Netherlands), University of Debrecen (Hungary), and Universitat Politècnica de València (Spain).
During the summer school with the topic of Open (Governmental) Data, the participants studied intensively in lectures and workshops the infrastructures of open data sources, data analysis and visualization as well as data ethical questions and concepts, and finally the aspects of data sharing in the research context.
In data projects, the international teams of PhD, Master and Bachelor students deepened their newly acquired knowledge and put it to practical use. Using the World Happiness Report, I mentored some groups that developed their own research questions and examined whether aspects such as gender equality or unemployment have an influence on the happiness index in different countries. The research results of these projects can be viewed on the project website projects.dalico.info
On September 30th, 2020 we have received funding for the project Ciencias de Datos e Inteligencia Artificial contra el COVID-19, IA4COVID19, from Fundación BBVA among more than 150 proposals presented to the call in the category: Big Data e Inteligencia Artificial (“Data-IA-COVID-19”). This proposal has been lead by the data research scientist Nuria Oliver from Ellis Alicante. The initiative, is linked to the Valencian Strategy in Artificial Intelligence.
Our project is a collaborative work that we have developed voluntarily and altruistically since the beginning of the crisis caused by the pandemic, professors from Valencian universities. The research entitled «Data science against Covid-19» brings together the participation of civil society (through a citizen survey), experts from the academic-research environment, and public administration, with the aim of providing information so that the those responsible for public crisis management can make informed decisions based on scientific evidence obtained from data analysis. In particular, I collaborate in the epidemiological models part and as head of the UPV node together with Miguel Rebollo from the VRAIN Institute. The initiative, linked to the Valencian Strategy in Artificial Intelligence through the commissioner of the presidency occupied by the researcher Nuria Oliver.
Last July we received funding for the project «Data Sciences against Covid-19» (CD4COVID), from the Supera Covid-19 fund that Banco Santander launched in April, together with CRUE Spanish Universities and the Higher Council for Scientific Research ( CSIC). The fund, endowed with 8.5 million euros to finance programs, projects and support measures, aims to minimize the impact of the crisis generated by the pandemic and focuses on three lines of action: research, impact projects social and strengthening the technological capacity of Spanish universities.
Our project is a collaborative work that we have developed voluntarily and altruistically since the beginning of the crisis caused by the pandemic, professors from Valencian universities. The research entitled «Data science against Covid-19» brings together the participation of civil society (through a citizen survey), experts from the academic-research environment and public administration, with the aim of providing information so that the those responsible for public crisis management can make informed decisions based on scientific evidence obtained from data analysis. In particular, I collaborate in the epidemiological models part and as head of the UPV node together with Miguel Rebollo from the VRAIN Institute. The initiative, linked to the Valencian Strategy in Artificial Intelligence through the commissioner of the presidency occupied by the researcher Nuria Oliver.
I have recently been elected as member of the Editorial Board of AIMS Mathematics. This is an international bimonthly publication devoted to publishing peer-reviewed, high quality, research articles in all major fields of mathematics. Its impact factor in 2019 is 0.882.
I recently obtained a Msc in Bioinformatics and Biostatitsics from Universitat Oberta de Catalunya and Universitat de Barcelona. My capstone project was entitled «Machine learning methods for characterizing single-particle trajectories with anomalous diffusion» under the supervision of Ferran Reverter Comes.
Single-Particle Tracking (SPT) appears to be a potential approach to studying different dynamic processes in the life sciences with recent advances in light microscopy. The physics of life molecules has an inherent instability due to the heterogeneity of free energy states that different types of molecules can show, far from thermal equilibrium and ranging at different scales, from the nanoscale of a single molecule up to the cellular or even organism level. The classification of trajectories is a relevant topic, not only in the biological field at the molecular level but also at the level of animal and human behavior. Besides, this topic combines physical principles with some degree of uncertainty, which habilitates us for exploring the use of ML techniques.
I have recently presented a contribution in the with Francisco Pedroche on rankings. We have particularized to Spotify. While people around the world stopped, music didn’t: we have shown that during the worst months of the pandemic, the list of the Top-200 hits on Spotify, produced from the streams registered on the popular app, had 18% more songs. The most played songs worldwide, during the months studied, were Dance Monkey (Tones and I), Blinding Lights (The Weeknd), and The Box (Roddy Ricch).
This means that, regardless of whether record companies published new material or not, listeners changed their preferences more often during the pandemic, thus causing an increase in the number of songs that made the lists. Thus, the first quarter of 2019 registered 474 songs on the Top 200 list, whereas in the same period of this year, the number of hits was 557, which represents an 18% increase. This figure reveals a significant increase when compared to the 1% decrease that took place during the same period from 2018 to 2019, or the 9% increase from 2017 to 2018.
It has already been published our paper «Community detection based deep neural network (CD-DNN) architectures: a fully automated framework for Likert scales» in the mathematical journal Mathematical Methods in Applied Science, where we apply network community detection in order a suitable infrastructure for an Artificial Neural Network. This permits to efficiently use raw data from psychological questionnaires based on Likert scales.
Mathematics can also contribute to the modeling of side problems to the pandemics. We point out some of them, such as data quality analysis, network medicine, healthcare services management, the physical spreading of the virus in the environment, the subsequent impact on the economy, and the social response to confinement government measures.
In any case, all the contributions and developments will be beneficial in many ways aside from coping with pandemics. Some potential topics that this Special Issue will cover, but is not limited to, are as follows: Epidemiological dynamics, diffusion modeling, compartmental and SIR type models, human dynamics, agent modeling of structured populations, network medicine, data quality, artificial intelligence and deep learning models, and data analysis of social media
Further information in https://www.mdpi.com/journal/mathematics/special_issues/Mathematics_COVID-19 Your work is welcome!!!