About this Research Topic
The current Covid-19 pandemic has shown the world that mathematical models are key tools for predicting the evolution of the infections and guiding public health measures. However, mathematical models combined with statistical analysis of datasets are not new; they have been used for the past century to study the spread of various epidemics: from influenza, to smallpox, AIDS, tuberculosis, Ebola, ..., and even to healthcare-associated infections caused by antibiotic-resistant pathogens. There is a wide range of such models ranging from agent-based models to models described by continuum ordinary and partial differential equations. Many of these models incorporate also stochastic events. In addition, models can focus on population scale (i.e., spread of disease between individual hosts), or on individuals scale (i.e., infection of a host and the immune response against the pathogen).
At the heart of modelling infectious diseases is epidemiological data (and immunological data – for in-host dynamics), which is necessary to enable prediction and forecasting. Data is used not only to parametrise various mathematical model, but also to train and validate machine learning and statistical models.
This Research Topic aims to provide new insights into the mathematical and statistical modelling approaches used to investigate various epidemic infections, either acquired in the community or in the hospital. It also aims to identify challenges related to epidemiological data, model parametrisation with data, as well as analytical and numerical investigations of such mathematical models (deterministic or stochastic, discrete or continuum), etc.
The topics may include (but not are limited to) the following aspects:
- Covid-19 epidemics and interventions
- Spread and control of hospital-acquired infections
- Spread and control of community-acquired infections
- In-host immune responses to various infections
- New mathematical/statistical tools to predict epidemics spread
- Machine learning techniques
- Epidemiological data acquisition and analysis
- The impact of epidemics on economy, society in general
- Short-term and long-term forecasting of epidemics
- Challenges related to data
A variety of manuscripts will be considered, including Original Research, Methods, Review, Mini Review and Perspective.
At the heart of modelling infectious diseases is epidemiological data (and immunological data – for in-host dynamics), which is necessary to enable prediction and forecasting. Data is used not only to parametrise various mathematical model, but also to train and validate machine learning and statistical models.
This Research Topic aims to provide new insights into the mathematical and statistical modelling approaches used to investigate various epidemic infections, either acquired in the community or in the hospital. It also aims to identify challenges related to epidemiological data, model parametrisation with data, as well as analytical and numerical investigations of such mathematical models (deterministic or stochastic, discrete or continuum), etc.
The topics may include (but not are limited to) the following aspects:
- Covid-19 epidemics and interventions
- Spread and control of hospital-acquired infections
- Spread and control of community-acquired infections
- In-host immune responses to various infections
- New mathematical/statistical tools to predict epidemics spread
- Machine learning techniques
- Epidemiological data acquisition and analysis
- The impact of epidemics on economy, society in general
- Short-term and long-term forecasting of epidemics
- Challenges related to data
A variety of manuscripts will be considered, including Original Research, Methods, Review, Mini Review and Perspective.
Keywords: statistics, mathematical models, immunology, epidemiology, Covid-19, community-acquired infections, hospital-acquired infections
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
