Welcome to POSTCOVID-AI!

This research project was born from the question, which factors influence the well-being of the population during the pandemic, and the following period? In order to find these parameters, we set 4 GOALS:

Base de datos

Create a public, large, anonymised DATASET of longitudinal social, behavioural and emotional data in the post-COVID19 context.

Base de datos

Implement an INTELLIGENT FRAMEWORK based on advanced machine learning and artificial intelligence algorithms to map individual and group conduct onto adverse outcomes and better understand COVID-related resilience factors.

Base de datos

Contribute to the development of new GUIDELINES, recommendations and policies informed by the new evidence generated via the methodological framework.

Base de datos

ADVANCE the scientific field of computational psychological, behavioural and social sciences via the intelligent monitoring and analysis of daily life context.

Read more

Latest news

Exploring the potential of the POSTCOVID-AI tool at local level

The POSTCOVID-AI project was born with a double intention: to help society and to advance the scientific field. We are almost at the end of this journey, which has turned out to be more enriching than we could have ever imagined. The generosity of our colleagues at the City Council of Montilla (Cordoba, Spain), with whom we started a collaboration a few months ago, has become an example of what inspired this project.

At our last meeting, we took a step further. In addition to finalising outstanding issues about the use of the project tool in the programme promoting physical activity among retired people, we explored the possibility of promoting the general participation of the residents of Montilla. This idea would fulfil the dual intention of the project. In terms of helping society, the tool would provide objective information on population well-being, allowing stakeholders to make decisions based on this data and for the neighbours' benefit. From a scientific point of view, this opportunity would allow the results obtained at the national level in the two previous studies carried out within the project to be replicated at the local level. In addition, by obtaining more evidence on the functioning of the tool, it would become more powerful for future use cases.

This has only just begun, but we are very excited to carry out this project together with the City Council of Montilla. We would like to thank Carmen, Marta, Alicia and Manolo for making this possible.

Read more

Second study completed!

A few weeks ago, we announced our intention to conduct a second study. Well, after an intense month of data collection, we are pleased to report that the second study has concluded successfully!

Similar to what we did for the first study, once the data was collected, we began the data curation process. In a previous post, we explained what this process involves, which will be very similar this time and will enable us to clean and prepare the extensive dataset recorded so that it can be used by both the POSTCOVID-AI platform and any other researchers or organizations interested.

We would like to take this opportunity to once again express our gratitude for the collaboration of all the study participants. Without their dedication, projects like these would be unfeasible.

Read more

POSTCOVID-AI second study

It has been over a year since we conducted the first of two planned studies in POSTCOVID-AI. After several intense months of data exploration in the initial study, along with the incorporation of certain technical improvements in the app and data collection system, we are now prepared to commence the second study of POSTCOVID-AI, scheduled to begin in the upcoming weeks. This new study poses an even greater challenge than the previous one. While the goal remains to collect the same type of data over a similar time frame (approximately one month), this time around, we are working with a significantly larger participant group. In fact, the objective is to replicate the first study with a sample of approximately 400 people, quadrupling the number of participants from the previous study, thus representing one of the largest mobile longitudinal datasets to date. This increase primarily aims to provide stronger support for the knowledge gained from the initial study up to this point and to yield additional data for the formulation of new hypotheses.

Read more