Special Issue "Numerical Simulation and Control for Disease"
Deadline for manuscript submissions: 31 December 2020.
Interests: mathematical modeling and numerical simulations of epidemiological systems with an emphasis on the use of optimal control theory, and computational stochastic modeling using agent-based models and network models in infectious diseases transmission dynamics
Interests: Mathematical modeling and computation in Biology, in particular, epidemiology and medical problems. Mathematical models include differential equations and related stochastic processes.
Emerging and re-emerging infectious diseases are posing serious problems. The immediate and effective implementation of control measures is of concern for public health officials all around the world. Many critical factors increase the potential risks of emerging and re-emerging infectious diseases. These critical factors include climate change related to global warming and people’s mobility, which facilitate the expansion of many infectious diseases and persistence in human populations. Due to the extremely complex nature of disease transmission dynamics, developing more accurate models has become challenging.
Mathematical modeling is a useful tool for studying the transmission dynamics and control of communicable human diseases. Various infectious diseases, such as influenza, measles, tuberculosis, Ebola, Zika, dengue, malaria, SARS-CoV, MERS-CoV, COVID 19, etc., have been analyzed using statistical and mathematical modeling and numerical simulations. In particular, the role of early interventions using statistical/mathematical modeling is critical for mitigating the spread of emerging and re-emerging infectious diseases. In addition, the assessment of the rapid prevention and efficient countermeasures is increasingly important for mitigating the morbidity and mortality impact of emerging or re-emerging infectious diseases, particularly on those individuals at the highest risk of developing severe disease.
This Special Issue focuses on innovative mathematical modeling and numerical simulations for control of emerging and re-emerging infectious diseases and welcomes research articles dealing with all such aspects.
Dr. Sunmi Lee
Prof. Dr. Yongkuk Kim
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1500 CHF (Swiss Francs). Please note that for papers submitted after 31 December 2020 an APC of 2000 CHF applies. Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- Emerging or re-emerging infectious diseases modeling;
- Mathematical and statistical modeling;
- Numerical methods and simulations;
- Ordinary differential equations, partial differential equations, integro-differential equations;
- Stochastic processes, network models, agent-based models;
- Advanced and conventional optimal control methods;
- Cost-effectiveness analysis of countermeasures and interventions.