Theory of Infectious Disease

We use a combination of mathematical analysis and computer simulation to examine the evolution and co-evolution of hosts and parasites. Themes include:

The evolution of host defense: resistance and tolerance

A large body of theoretical work has been developed to look at how ecological feedbacks affect the evolution of defense against parasites. The approach is a modern evolutionary game theory that allows the ecological dynamics of the interaction to be taken into account in the evolutionary process. This is of crucial importance in the evolution of defense, since the host strategy will affect the parasite prevalence. For example, resistance, as it spreads from rare, will tend to reduce prevalence and be less advantageous. Other defense mechanisms such as mortality tolerance will have different effects on prevalence.

An early paper is:

Boots, M., & Haraguchi, Y. (1999). The Evolution of Costly Resistance in Host‐Parasite Systems. The American Naturalist, 153(4), 359–370 https://doi.org/10.1086/303181

A key more recent paper is:

Best, A., White, A., & Boots, M. (2008). Maintenance of host variation in tolerance to pathogens and parasites. Proceedings of the National Academy of Sciences of the United States of America, 105(52), 20786–20791 https://doi.org/10.1073/pnas.0809558105

Spatial structure and the evolution of hosts and parasites

There is always some form of spatial or social structure within populations: individuals interact with some individuals more often than others. We use a combination of computer simulation and pair approximation to examine the implication of local interactions to the evolution of parasites. The key to this work is examining mixing patterns between the completely local and the completely mixed.

spatial

Boots, M., & Sasaki, A. (1999). “Small worlds” and the evolution of virulence: infection occurs locally and at a distance. Proceedings of the Royal Society of London. Series B, Biological Sciences, 266(1432), 1933–1938 https://doi.org/10.1098/rspb.1999.0869

Boots, M., & Sasaki, A. (2002). Parasite-driven extinction in spatially explicit host-parasite systems. American Naturalist, 159(6), 706–713 https://doi.org/10.1086/339996

This has been followed up with a series of theoretical models and now an empirical test of the theory (see Insect Model Systems section):

Boots, M., & Mealor, M. (2007). Local interactions select for lower pathogen infectivity. Science, 315(5816), 1284–1286 https://doi.org/10.1126/science.1137126

The generation of diversity in hosts and parasites

Understanding the drivers of host and parasite diversity is a key challenge in the lab.

Boots M, A.White, A. Best & R. Bowers (2014). How specificity and epidemiology drive the coevolution of static trait diversity in hosts and parasites. Evolution, 68, 1594-1606.
Boots M, A. White, A. Best & R. Bowers (2012). Diversity in host resistance: The importance of who infects whom. Ecology Letters 15, 1104-1111.

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Coevolutionary Dynamics

How important is coevolution as opposed to evolution to the outcome?

Best, A., A. White & M. Boots (2009). The implications of co-evolutionary dynamics to host-parasite interactions. The American Naturalist 173, 779-791.
Best A, A. White & M. Boots (2014). The co-evolutionary implications of host tolerance. Evolution, 68, 1426-1435.
Ashby B & M. Boots (2015). The coevolution of parasite virulence and host mating strategies. Proceedings of the National Academy of Sciences 112(43) 13290-13295.

Immune priming

What are the implications of immune priming to the ecological and evolutionary outcomes?

Best, A., Tidbury, H., White, A., & Boots, M. (2013). The evolutionary dynamics of within-generation immune priming in invertebrate hosts. Journal of the Royal Society Interface, 10(80) https://doi.org/10.1098/rsif.2012.0887

Tidbury, H. J., Best, A., & Boots, M. (2012). The epidemiological consequences of immune priming. Proceedings of the Royal Society B: Biological Sciences, 279(1746), 4505–4512 https://doi.org/10.1098/rspb.2012.1841

Trade-off shapes

The elephant in the room for many modeling studies is that the outcomes depend critically on the shape of the trade-off curve. We have developed a new method (trade-off invasion plots (TIPs)) that allows the role of the trade-off shape to be studied explicitly.

Hoyle, A., Bowers, R. G., White, A., & Boots, M. (2008). The influence of trade-off shape on evolutionary behaviour in classical ecological scenarios. Journal of Theoretical Biology, 250(3), 498–511 https://doi.org/10.1016/j.jtbi.2007.10.009

Bowers, R. G., Hoyle, A., White, A., & Boots, M. (2005). The geometric theory of adaptive evolution: trade-off and invasion plots. Journal of Theoretical Biology, 233(3), 363–377 https://doi.org/10.1016/j.jtbi.2004.10.017

Boots, M., & Bowers, R. G. (2004). The evolution of resistance through costly acquired immunity. Proceedings of the Royal Society B: Biological Sciences, 271(1540), 715–723 https://doi.org/10.1098/rspb.2003.2655

Ecological theory

We have suggested oligomorphic dynamics as a way to bridge adaptive dynamics and quantitative genetics:

Lion, S., Sasaki, A., & Boots, M. (2023). Extending eco-evolutionary theory with oligomorphic dynamics. Ecology Letters, 26(S1), S22–S46 https://doi.org/10.1111/ele.14183

We have also addressed the general issue of disease dynamics under density and frequency dependence:

Ryder, J. J., Miller, M. R., White, A., Knell, R. J., & Boots, M. (2007). Host‐parasite population dynamics under combined frequency‐ and density‐dependent transmission. Oikos, 116(12), 2017–2026 https://doi.org/10.1111/j.2007.0030-1299.15863.x

And developed resonance approaches to understand disease dynamics:

Greenman, J., Kamo, M., & Boots, M. (2004). External forcing of ecological and epidemiological systems: a resonance approach. Physica D: Nonlinear Phenomena, 190(1–2), 136–151 https://doi.org/10.1016/j.physd.2003.08.008

Human Infectious Disease

We use epidemiological and evolutionary models to address questions in human vector-borne disease.

Dengue

Numerous studies have shown that the majority of DENV infections are inapparent and that the ratio of inapparent to symptomatic infections (I/S) fluctuates substantially year-to-year. However, the mechanisms explaining these large fluctuations are not well understood. We used a mechanistic model to test the hypothesis that in dengue-endemic areas, frequent boosting (i.e., exposures to DENV that do not lead to extensive viremia and result in a <4-fold rise in antibody titers) of the immune response can be protective against symptomatic disease and this can explain fluctuating I/S ratios.

Alexander L. W., R. Ben-Shachar, L. C. Katzelnick, G. Kuan, A. Balmaseda, E. Harris & M. Boots (2021). Boosting can explain patterns of fluctuations of ratios of inapparent to symptomatic dengue virus infections. Proceedings of the National Academy of Sciences 118(14) e2013941118.

The four serotypes of dengue show a characteristic out of phase pattern in Bangkok, while the phylogenetic analysis of the data shows evidence of an immune interaction between the serotypes. We used models to show that partial cross-immunity was sufficient to cause the out of phase dynamics providing evidence for cross-immunity between dengue serotypes.

Adams, B., E. C. Holmes, C. Zhang, M. P. Mammen Jr, S. Nimmannitya, S. Kalayanarooj and M. Boots (2006). Cross-protective immunity can account for the alternating epidemic pattern of dengue virus serotypes circulating in Bangkok. Proceedings of the National Academy of Science. 103, 14234-14239.

We had previously shown that the large difference between the dengue serotypes could be explained by antibody-dependent enhancement on death.

Kawaguchi, I., A. Sasaki & M. Boots (2003). Antibody-dependent enhancement explains coexistence in dengue serotypes. Proceedings of the Royal Society, Series B 270, 2241-2247.

Further models look at the role of mosquito transmission of dengue.

Malaria

We have also looked at the dynamics of Malaria in northern Thailand.

Childs, D.Z, Cattadori I, Wannapa Suwonkerd & Somsak Prajakwong, Boots, M (2006). Long-term patterns of Malaria incidence in Northern Thailand. Transactions of the Royal Society of Tropical Medicine and Hygiene, 100 (7): 623-631.

And built a dynamical seasonally forced model of malaria that looks at reasons for the contrasting epidemic dynamics of malaria in Thailand and Kenya.

Childs, D.Z. & M. Boots (2010). Seasonal Forcing, Immunity and the Dynamics of Malaria. Journal of the Royal Society Interface, 7, 309-319.

Work on mosquitoes has looked at host fidelity in Japanese encephalitis vectors and more applied entomology on Aedes.

Mwandawiro, C.S., M. Boots, N. Tuno, Y. Tsuda & M. Takagi (2000). Host choice in Japanese Encephalitus vectors in Northern Thailand. Transactions of the Royal Society of Tropical Medicine and Hygiene, 94, 238-242.