Mathematical modeling to inform biological experiments and epidemiological studies

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Guest Editors
Dr. Gesham Magombedze
Center for Infectious Diseases Research and Experimental Therapeutics, Baylor Research Institute, Baylor University Medical Center, Dallas, TX, USA
Email: gesham.magombedze@bswhealth.org

Dr. Tinevimbo Shiri
Livepool School of Tropical Medicine, UK
Email: Tinevimbo.Shiri@lstmed.ac.uk

Dr. Zindoga Mukandavire
Coventry University, School of Computing, Electronics and Mathematics, Coventry, UK
Email: Zindoga.Mukandavire@coventry.ac.uk

Manuscript Topics
The aim of this Special Issue is to publish original research articles that marry theoretical modelling and real life biologicial problems. We call for unique simple, yet robust, mathematical modeling studies that outline fundamental mathematical analysis steps, interpretation of mathematical results that are then applied to real biological problems in microbiology, ecology, epidemiology, public health and clinical trials. We are interested in mathematical models that will then give direct insights on designing better biological studies, improved data collection, and clinical trials. In summary, robust mathematical modeling ought to be fused with biological problems in a way that is useful to biologists, ecologists, epidemiologists, clinicians, clinical trialists and policy makers.

All areas of mathematical biology are welcome, however we are specifically interested in mathematical and computational studies that demonstrate how mathematical analysis (analytical and simulations) is applied to (i) explaining biological data, (ii) designing experiments, (iii) epidemiological studies and (iv) clinical trials, and as well as in data collection.

Paper Submission
All manuscripts will be peer-reviewed before their acceptance for publication.
The deadline for manuscript submission is 31 May 2019.

Instructions for authors 
http://www.aimspress.com/news/295.html
Please submit your manuscript to online submission system
http://oeps.aimspress.com/mbe/ch/author/login.aspx

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