COVID-19 · Mathematical models and data management in public health emergencies

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Several UPC research groups and centers are working on large data management and mathematical modeling projects to advance research in the health emergency caused by the COVID-19 epidemic.

A mathematical model to monitor the epidemic

The  Computational Biology and Complex Systems Group (BIOCOM-SC), which is specialised in mathematical epidemiology, and the Germans Trias i Pujol Research Institute (IGTP) are working to analyse data on the incidence of COVID-19. They have developed a mathematical model to quantify the situation and predict what will happen in the following three days.

The centre provides an informative document to understand the epidemiological dynamics, daily reports that assess the situation and make short-term predictions in Catalonia, Spain and the European Union, a daily summary of the situation in the other autonomous communities and other EU countries, and predictions from previous days.

The UPC promotes the platform GoData and supports the WHO

The UPC is providing technical support, with the collaboration of the Department of Network Engineering and the Department of Computer Architecture, to the World Health Organisation (WHO) to apply GoData software in Catalonia. GoData is a tool for researching outbreaks that can gather field data during COVID-19 public health emergencies.

The UPC and the spin-off Alteraid, which emerged from the UPC, are promoting the platform, which supports the installation of WHO software and provides a testing server to test and train on the application.

This is a multilingual, flexible tool with many functions for importing and exporting data. It enables information to be obtained to monitor the incidence of COVID-19 and adapt the response of health institutions to various scenarios. 

A model to estimate the real number of COVID-19 cases in each autonomous community


A mathematics and statistics team from the UPC, the Humboldt-Universität zu Berlin and the UAB is developing a model to estimate the real number of daily new cases of people infected by COVID-19, given the impossibility of gaining this data directly from the entire population as many cases are mild or asymptomatic. The official data are based on numbers that reflect the most serious cases and therefore the other cases that also contribute to the expansion of the pandemic are not known.

Researchers from the UAB Department of Mathematics developed in 2016 a method for the analysis of data that are underrepresented in statistics (Under‐reported data analysis with INAR‐hidden Markov chains). The method can be used to make an accurate estimation of the number of cases that are not officially registered. It has various public health applications, such as monitoring the real number of cases of infections with human papillomavirus (HPV), botulism and real cases of abused women.

Now, the UAB researchers, with the collaboration of the UPC Department of Computer Science, the Humboldt-Universität zu Berlin and the Mathematical Research Centre (CRM), are using this method to update the situation of COVID-19 daily and, in particular, quantify the cases that are not reported in the official records of the illness in Spain. The results provide a more realistic image of the pandemic in real time, and help to estimate fundamental data accurately, such as the real mortality rates or the basic number of infections so that professionals and politicians can make decisions. The analysis is designed to be easily reproduced with the data from other countries.

On behalf of the UPC, the researcher Argimiro Arratia, from the  Relational Algorithmics, Complexity and Learnability Laboratory (LARCA), is involved in the design and programming of epidemic models to which are entered as input a series of cases of infection reconstructed according to the mathematical model developed by the team at the UAB. In addition, the researcher is collaborating to collect data. Data processing is automatized in the entire process of executing the simulations and data analysis. 






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