Theoretical Biology and Medical Modelling

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Aims and scope

Theoretical Biology and Medical Modelling is an open access peer-reviewed journal adopting a broad definition of "biology" and focusing on theoretical ideas and models associated with developments in biology and medicine. Mathematicians, biologists and clinicians of various specialisms, philosophers and historians of science are all contributing to the emergence of novel concepts in an age of systems biology, bioinformatics and computer modelling. This is the field in which Theoretical Biology and Medical Modelling operates. We welcome submissions that are technically sound and offering either improved understanding in biology and medicine or progress in theory or method.

Featured article: Assessing potential countermeasures against the dengue epidemic in non-tropical urban cities

Masui et al. model the spread of dengue in an urban environment, and find that reducing movement between high-risk and low risk areas, as in quarantine, may actually promote the spread of the disease, contrary to expectations.

The authors of this article have also written an entry on the BioMed Central blog, On Biology:

Dengue and quarantine – not as simple as you think

Editor-in-Chief

Professor Hiroshi Nishiura

Professor Nishiura is an Associate Professor at the Graduate School of Medicine, The University of Tokyo. He is cosmopolitan-minded, having worked for 10 years for different infectious disease modeling groups at Imperial College London, University of Tuebingen (Germany), University of Utrecht (The Netherlands) and the University of Hong Kong, before returning back to Japan in 2013. His research interests span the areas of statistical epidemiology of infectious diseases, epidemiological modeling and biomathematical formulation of the transmission dynamics of infectious diseases. He aims to answer policy-relevant questions by integrating various mathematical models with empirically observed data. A common thread in his research is an understanding of the epidemiological dynamics underlying empirically observed data.