Winners of the 500k Pandemic Response Challenge

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.

You can find the details of the prize here  and of our model here .

Finalists of the 500k Pandemic Challenge Response XPRIZE!!!!

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.

Potential limitations in COVID-19 machine learning due to data source variability

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 . (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.

Success at the ANDI-Challenge

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 en VIA EMPRESA sobre la disponibilitat de dades

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

 

 

Investigadores de la UPV trabajan en la lucha contra la COVID-19 a través de la Ciencia de Datos

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.

Data Science Group COVID-19 (Comunitat Valenciana)

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

Grant concession from BBVA fund SARS-CoV-2 and COVID-19

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.

 

 

Grant funding from CRUE-Santander Fondo Supera CoVID-19

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.

MsC in Bioinformatics and Biostatistics

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.

Machine learning methods for characterizing single-particle trajectories with anomalous diffusion

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.

 

Research paper on Network Science and Machine Learning

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.

Community detection and neural networks.