In recent years, the words “infection” and “outbreak” have not been far from anyone’s mind as we’ve faced the emergence of a new variant of coronavirus, COVID-19, Malaria, Dengue fever, HIV/AIDS, Tuberculosis, Cholera, Zika virus, Chickenpox, Influenza, Pneumonia, and so on. Across the globe, efforts are underway to control and limit viral spread, and to find ways to treat those infected. The vast number of asymptomatic cases makes it harder to control the spread of these 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 modelling pandemic evolution. 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.
As we watch these events unfurl, it is evident that there is still a lot that we, as a global community, do not yet understand about the dynamics of infectious diseases. The ways in which diseases spread are a concern that we all have a stake in research that helps further our understanding of infectious diseases and can influence each of our lives. Efforts in monitoring infected people from the presumed moment in which they were infected have shown to be effective in controlling virus expansion. In this line, mathematics can contribute to the data management and development of app tools for monitoring human mobility.
Mathematics and computational science may sound like an unlikely hero to help us overcome a global epidemic, but the insights we gain from studying the dynamics of infectious diseases by using equations describing fundamental variables are not to be underestimated. By approaching infectious diseases from a mathematical perspective, we can identify patterns and common systems in disease function, and enable discovery of some of the underlying structures that govern outbreaks and epidemics. Mathematical modellers make use of available data from current and previous outbreaks to predict who may get infected, where vaccination efforts will be most effective, and how to limit the spread of disease.
We welcome any and all the contributions and developments within the scope of this collection. Topics include, but are not limited to, the following:
- Epidemiological dynamics
- Diffusion modelling
- Compartmental and SIR type models
- Human dynamics
- Agent modelling of structured populations
- Network medicine
- Data quality
- Artificial intelligence and deep learning models
- Data analysis of social media
Applied Mathematics in Science and Engineering accepts original research articles. When submitting your article, please select the Article Collection "New Advances in Computational and Applied Mathematics Methods against Infectious Disease Dynamics" from the drop-down menu in the submission system.
