Who are we?


We are a team of Spanish scientists who have been working since March 2020 in collaboration with the Valencian Government of Spain on using Data Science to help fight the SARS-CoV-2 pandemic. We have focused on 4 large areas of work: large-scale human mobility modeling via the analysis of aggregated, anonymized data derived from the mobile network infrastructure; computational epidemiological models; predictive models and citizen science by means of a large-scale citizen survey called COVID19impactsurvey which, with over 375,000 answers in Spain and around 150,000 answers from other countries is one of the largest COVID-19 citizen surveys to date. Our work has been awarded two competitive research grants.

Since March, we have been developing two types of traditional computational epidemiological models: a metapopulation compartmental SEIR model and an agent-based model. However, for this challenge, we opted for a deep learning-based approach, inspired by the model suggested by the challenge organizers. Such an approach would enable us to build a model within the time frame of the competition with two key properties: be applicable to a large number of regions and be able to automatically learn the impact of the Non-Pharmaceutical Interventions (NPIs) on the transmission rate of the disease.

Meet The Team

  • Kristina Polotskaya

    Òscar Garibo

    Nuria Oliver

  • Manuel G. Portolés

    Miguel Angel García

    Victor de Elena

    J. Alberto Conejero

  • Miguel Rebollo

    Francisco Escolano

    Elisa Espín

    Eloy Piñol Jimenez

  • Aurora Mula Leal

    Adolfo Lopez

    Miguel Angel Lozano

Our Multidisciplinary Team


This group is made up of more than twenty experts from the Universities and research centers of the Valencian Community (Spain) and led by Dr. Nuria Oliver. 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.

Affiliated with: Ellis Alicante, Universitat Jaume I, Universidad de Alicante, Universidad Miguel Hernández, Universitat Politècnica de València, Universidad Cardenal Herrera CEU.

Visualizations of applied NPIs

Computational epidemiological models

We have developed machine learning-based predictive models of the number of hospitalizations and intensive care hospitalizations overall and for SARS-CoV-2 patients. We have also developed a model to infer the prevalence of the disease based on a few of the answers to our citizen survey https://covid19impactsurvey.org

We have developed 3 types of computational epidemiological models: (1) a metapopulation compartmental SEIR-type model; (2) an agent-based model and (3) a deep learning-based model

Prescriptor Models

Our goal in the Prescription phase of the competition is to develop an interpretable, data-driven and flexible prescription framework that would be usable by non machine-learning experts, such as citizens and policy makers in the Valencian Government. Our design principles are therefore driven by developing interpretable and transparent models.

Given the intervention costs, it automatically generates up to 10 Pareto-optimal intervention plans. For each plan, it shows the resulting number of cases and overall stringency, its position on the Pareto front and the activation regime of each of the 12 types of interventions that are part of the plan.

Press Impacts

- International Press -
- National Press -

Social Media Impacts