Hostname: page-component-848d4c4894-x24gv Total loading time: 0 Render date: 2024-06-09T08:20:03.242Z Has data issue: false hasContentIssue false
  • English
  • Français

Editorial: Mathematical modelling of infectious diseases

Published online by Cambridge University Press:  30 March 2016

ANDY FENTON
ANDY FENTON*
Affiliation:
Institute of Integrative Biology, University of Liverpool, Crown Street, Liverpool L69 7ZB, UK
*
*Corresponding author. Institute of Integrative Biology, University of Liverpool, Crown Street, Liverpool L69 7ZB, UK. E-mail: a.fenton@liverpool.ac.uk
Get access Rights & Permissions [Opens in a new window]

Extract

The field of disease ecology – the study of the spread and impact of parasites and pathogens within their host populations and communities – has a long history of using mathematical models. Dating back over 100 years, researchers have used mathematics to describe the spread of disease-causing agents, understand the relationship between host density and transmission and plan control strategies. The use of mathematical modelling in disease ecology exploded in the late 1970s and early 1980s through the work of Anderson and May (Anderson and May, 1978, 1981, 1992; May and Anderson, 1978), who developed the fundamental frameworks for studying microparasite (e.g. viruses, bacteria and protozoa) and macroparasite (e.g. helminth) dynamics, emphasizing the importance of understanding features such as the parasite's basic reproduction number (R0) and critical community size that form the basis of disease ecology research to this day. Since the initial models of disease population dynamics, which primarily focused on human diseases, theoretical disease research has expanded hugely to encompass livestock and wildlife disease systems, and also to explore evolutionary questions such as the evolution of parasite virulence or drug resistance. More recently there have been efforts to broaden the field still further, to move beyond the standard ‘one-host-one-parasite’ paradigm of the original models, to incorporate many aspects of complexity of natural systems, including multiple potential host species and interactions among multiple parasite species.

Keywords

Ecologyevolutiondata integrationmodel testingpopulation dynamicstheorymodel complexity
Type
Special Issue Review
Information
Parasitology , Volume 143 , Special Issue 7: Mathematical modelling of infectious diseases , June 2016 , pp. 801 - 804
Check for updates
Copyright
Copyright © Cambridge University Press 2016 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Anderson, R. M. and May, R. M. (1978). Regulation and stability of host-parasite population interactions. I. Regulatory processes. Journal of Animal Ecology 47, 219–247.Google Scholar
Anderson, R. M. and May, R. M. (1981). The population dynamics of microparasites and their invertebrate hosts. Philosophical Transactions of the Royal Society of London, Series B 291, 451–524.Google Scholar
Anderson, R. M. and May, R. M. (1992). Infectious Diseases of Humans: Dynamics and Control. Oxford University Press, Oxford.Google Scholar
Cressler, C., McLeod, D., Rozins, C., van den Hoogen, J. and Day, T. (2016). The adaptive evolution of virulence: a review of theoretical predictions and empirical tests. Parasitology. doi:10.1017/S003118201500092X.Google Scholar
de Leo, G., Dobson, A. and Gatto, M. (2016). Body size and meta-community structure: the allometric scaling of parasitic worm communities in their mammalian hosts. Parasitology. doi:10.1017/S0031182015001444.Google Scholar
Garnier, R., Grenfell, B., Nisbet, A., Matthews, J. and Graham, A. (2016). Integrating immune mechanisms to model nematode worm burden: an example in sheep. Parasitology. doi:10.1017/S0031182015000992.Google Scholar
Gilbert, L., Norman, R., Laurenson, K. M., Reid, H. W. and Hudson, P. J. (2001). Disease persistence and apparent competition in a three-host community: an empirical and analytical study of large scale wild populations. Journal of Animal Ecology 70, 1053–1061.Google Scholar
Greischar, M., Reece, S. and Mideo, N. (2016). The role of models in translating within-host dynamics to parasite evolution. Parasitology. doi:10.1017/S0031182015000815.Google Scholar
Ionides, E. L., Dao, N., Atchade, Y., Stoev, S. and King, A. A. (2015). Inference for dynamic and latent variable models via iterated, perturbed Bayes maps. Proceedings of the National Academy of Sciences of the United States of America 112, 719–724.Google Scholar
King, A. A., Ionides, E. L., Breto, C. M., Ellner, S., Kendall, B., Wearing, H., Ferrari, M. J., Lavine, M. and Reuman, D. C. (2015). Pomp: Statistical inference for partially observed Markov processes (R package). http://pomp.r-forge.r-project.org.Google Scholar
Magpantay, F. M., Domenech de Celles, M., Rohani, P. and King, A. (2016). Pertussis immunity and epidemiology: mode and duration of vaccine-induced immunity. Parasitology. doi:10.1017/S0031182015000979.Google Scholar
May, R. M. and Anderson, R. M. (1978). Regulation and stability of host-parasite population interactions. II. Destabilizing processes. Journal of Animal Ecology 47, 249–267.Google Scholar
McCallum, H. (2016). Models for managing wildlife disease. Parasitology. doi:10.1017/S0031182015000980.Google Scholar
Norman, R., Worton, A. and Gilbert, L. (2016). Past and future perspectives on mathematical models of tick-borne pathogens. Parasitology. doi:10.1017/S0031182015001523.Google Scholar
Park, A., Cleavland, C., Dallas, T. and Corn, J. (2016). Vector species richness increases haemorrhagic disease prevalence through functional diversity modulating the duration of seasonal transmission. Parasitology. doi:10.1017/S0031182015000578.Google Scholar
Restif, O., Hayman, D. T. S., Pulliam, J. R. C., Plowright, R. K., George, D. B., Luis, A. D., Cunningham, A. A., Bowen, R. A., Fooks, A. R., O'Shea, T. J., Wood, J. L. N. and Webb, C. T. (2012). Model-guided fieldwork: practical guidelines for multidisciplinary research on wildlife ecological and epidemiological dynamics. Ecology Letters 15, 1083–1094.Google Scholar
Viana, M., Shirima, G., Kunda, J., Fitzpatrick, J., Kazwala, R., Buza, J., Cleaveland, S., Haydon, D. and Halliday, J. (2016). Integrating serological and genetic data to quantify cross-species transmission: brucellosis as a case study. Parasitology. doi:10.1017/S0031182016000044.Google Scholar
Wearing, H., Robert, M. and Chrsitofferson, R. (2016). Dengue and chikungunya: modeling the expansion of mosquito-borne viruses into naïve populations. Parasitology. doi:10.1017/S0031182016000421.Google Scholar