Mathematical and Computational Methods against the COVID-19 Pandemics

Dear Colleagues,

At the beginning of 2020, the world was shocked by the scale of the SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) crisis, causing social and economic collapse throughout the world, as no vaccine or treatment was available at the time. Moreover, in most countries, the existing number of ICU units could not cope with the vast number of people infected by the virus that leads to CoVID-19 (coronavirus disease).

The vast number of asymptomatic cases makes it harder to control the spread of the epidemics. In many cases, the limited amount of data, make it even harder to set precise models to predict the evolution of the disease. The well-known SIR type models have been useful in modeling the evolution of the pandemics. Although they were already very well-known for nearly one hundred years, a new interest in them has attracted researchers to continue working on them. 

Efforts in monitoring infected people from the presumed moment in which they were infected have shown to be effective in controlling the virus expansion. In this line, mathematics can contribute to the data management and development of app tools for monitoring human mobility.

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 
• Data analysis of social media

Prof. Dr. J. Conejero
Guest Editor

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• SIR model
• Epidemiological modelling
• Network medicine
• Human dynamics
• Data quality
• COVID-19
• SARS-CoV-2