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Mathematical modelling of parasitic infections: from data and parameter estimation to evolutionary implications

As the discipline of parasitology has become increasingly quantitative, mathematical models have become widely accepted as useful tools with which to formalise our current understanding of the population biology of parasites, their transmission dynamics, and to explore the impact on such dynamics of control interventions. The field of mathematical modelling of parasitic infections has moved on a great deal from the pioneering work of Ross and Macdonald, followed by that of Tallis and Leyton, Bailey, Nåssell, Dietz, and Anderson and May to mention a few, yet relatively simple, single parasite and host models continue to offer profound qualitative insights and stimulate much needed research to test the hypotheses that are implicit or explicitly formulated in such models. In earlier stages of development are models to tackle the issue of multiple infections of a host, multiple hosts of a pathogen, or those merging principles of population dynamics and population genetics to investigate the consequences of control interventions both in epidemiological and evolutionary scales. This special issue of Parasitology was conceived as a result of two sessions on modelling parasitic infections at the 11th International Congress of Parasitology (ICOPA XI), held in Glasgow during the summer of 2006, and which I had the pleasure to chair with Ian Hastings from the University of Liverpool. The sessions were very successful and withmost speakers willing to contribute their papers, my co-editor Les Chappell and I felt brave enough to embark in the preparation of this volume. The selection of articles we now present contains papers from the ICOPA sessions and others that we commissioned subsequently. We start the volume as we intend to end it, with papers that take into account possible evolutionary implications of control interventions. Exactly 100 years ago Sir Ronald Ross initiated, in his report on Mauritius in 1908, the development of mathematical models for the study of malaria epidemiology, so we see it fit to begin this special issue with a section on models for malaria control. Thus, the article by Jacob Koella and Lamia Zaghloul examines the prospects of using evolutionary costs to try and enhance the efficacy of controlling this infection through the use of genetically modified mosquitoes, and concludes that even when combining evolutionary costs (decreased fitness of resistant mosquitoes) with epidemiological costs (decreased survival of infected mosquitoes), the fraction of resistant mosquitoes that has to be achieved to effect a meaningful impact is still very high. The paper by Alan Saul continues this theme by exploring models for mosquito stage transmission blocking vaccines and concludes that we may be closer to an effective transmission blocking vaccine than is widely believed. Yet, it is not obvious how decreased Plasmodium oocyst intensities in mosquitoes translate into given levels of vaccine efficacy. The final paper of this ‘malaria models’ section describes a comprehensive micro-simulation model by Tom Smith and co-workers, with the potential of providing a platform for testing, under different epidemiological scenarios, the likely impact of the control measures explored in the previous two papers alone or in combination with other interventions. The contribution of Olivier Restif and coauthors challenges the commonly made assumption 1487