Research

I am a quantitative disease ecologist interested in the ways that individual differences in behavior and physiology scale-up to population level outcomes.

I am a quantitative disease ecologist interested in developing and improving mathematical models of disease to assist in prediction and prevention of emerging and zoonotic infectious diseases in the context of rapidly changing, human-impacted environments. The overall objective of my research program is to explore the effects of heterogeneity in behavioral and immune competence on disease modeling predictions within and across populations. I use mathematical modelling approaches, integrated with empirical data, to explore three different types of heterogeneity that can alter individual transmission rates: (i) within-host heterogeneity; (ii) contact heterogeneity and group structure within populations; and (iii) spatial heterogeneity across landscapes. I model these types of heterogeneity in domestic and wild animal populations for specific host-pathogen systems (e.g., influenza A virus in swine herds, feline leukemia virus in Puma concolor) and more generally for theoretical systems. These models differ in the behavioral processes that they encompass and the spatial scale at which they operate. My work also has broader implications for understanding human disease risk within the One Health framework, which includes human, animal, and environmental health.

Within-host heterogeneity

I am interested in understanding how the behavioral and physiological components of transmission interact to determine disease dynamics and how behavioral changes resulting from infection can alter affect epidemic outcomes (Ezenwa et al., 2016). I  developed a dynamic network, individual-based modeling approach to allow for covariation between the behavioral and physiological components of transmission (White, Forester, & Craft, 2018a). My results show that: (a) individual variability in susceptibility or infectiousness, which is typically unaccounted for in disease models, can have profound effects on population-level disease dynamics; (b) when contact rate and susceptibility or infectiousness negatively covary, it takes substantially longer for epidemics to spread throughout the population; and (c) reductions in contact rate resulting from infection-induced behavioral changes can prevent the pathogen from reaching most of the population.              

Contact heterogeneity

Contact heterogeneity is particularly relevant in industrial farm systems with variable movement between different production stages and farms. For example, recent modelling and empirical work on influenza A virus (IAV) suggests that piglets play an important role as an endemic reservoir. I developed a spatially-explicit metapopulation model to test intervention strategies with the goal of reducing the incidence of IAV in piglets and ideally, preventing piglets from becoming exposed in the first place (White, Torremorell, & Craft, 2017). Those interventions included biosecurity measures, vaccination, and management options that swine producers may employ individually or jointly to control IAV in their herds. The Susceptible-Exposed-Infectious-Recovered-Vaccinated (SEIRV) model reflected the spatial organization of a standard breeding herd and accounts for the different classes of pigs therein including gilts, sows, and piglets in various production and immune stages. The findings show that piglets are a high-risk sub-group and that combined biosecurity and vaccination efforts can reduce, but are unlikely to eliminate IAV after it has been introduced into the breeding herd. 

While contact heterogeneity is the norm rather than the exception in wildlife species, contact networks are underutilized in wildlife pathogen studies and methodologies for deriving and analyzing contact networks in these contexts remains inconsistent (White, Forester, & Craft, 2017).

Spatial heterogeneity

Few studies have used mechanistic movement to bridge the gap between disease and movement ecology (White, Forester, & Craft, 2018c).To begin to bridge this gap,I developed a stochastic, individual-based, Susceptible Infectious Recovered (SIR) model where resource selection functions (RSF) governed individual movement choices (White, Forester, & Craft, 2018b). The simulation results support the counterintuitive idea that fragmentation promotes pathogen persistence, but this finding was largely dependent on perceptual range of the host (how far a host could perceive habitat), conspecific density (how many other hosts on the landscape), and recovery rate.

 I am continuing to build on these research questions at my current post-doc fellowship where I am investigating how urbanization alters movement of apex predators like puma (Puma concolor), and how these movement patterns may influence disease transmission. I have developed an individual-based model to simulate disease dynamics in the face of territorial behavior (White, VandeWoude & Craft, in revision). For this theoretical host-pathogen system, hosts modulated their movement choices in response to cues deposited on the landscape by conspecifics (i.e., stigmergy) resulting in dynamic territory formation. As they moved through the landscape, infected hosts could also leave infectious pathogens in their wake. We found that territoriality best reduced maximum prevalence in conditions where we would otherwise expect outbreaks to be most successful: slower recovery rates (i.e., longer infectious periods) and higher conspecific densities. However, at high enough densities, outbreak increased. Our findings therefore support a limited version of the “territoriality benefits” hypothesis—where reduced home range overlap leads to reduced opportunities for pathogen transmission, but with the caveat that reduction in outbreak severity may increase the likelihood of pathogen persistence.

References

Ezenwa, V. O., Archie, E. A., Craft, M. E., Hawley, D. M., Martin, L. B., Moore, J., & White, L. A. (2016). Host behaviour – parasite feedback: an essential link between animal behaviour and disease ecology. Proceedings of the Royal Society B: Biological Sciences, 283, 20153078. https://doi.org/10.1098/rspb.2015.3078

White, L. A., Forester, J. D., & Craft, M. E. (2017). Using contact networks to explore mechanisms of parasite transmission in wildlife. Biological Reviews, 92(1), 389–409. https://doi.org/10.1111/brv.12236

White, L. A., Forester, J. D., & Craft, M. E. (2018a). Covariation between the physiological and behavioral components of pathogen transmission: host heterogeneity determines epidemic outcomes. Oikos, 127(4), 538–552. https://doi.org/10.1111/oik.04527

White, L. A., Forester, J. D., & Craft, M. E. (2018b). Disease outbreak thresholds emerge from interactions between movement behavior, landscape structure, and epidemiology. Proceedings of the National Academy of Sciences, 115(28), 7374–7379. https://doi.org/10.1073/pnas.1801383115

White, L. A., Forester, J. D., & Craft, M. E. (2018c). Dynamic, spatial models of parasite transmission in wildlife: Their structure, applications and remaining challenges. Journal of Animal Ecology, 87(3), 559–580. https://doi.org/10.1111/1365-2656.12761

White, L. A., Torremorell, M., & Craft, M. E. (2017). Influenza A virus in swine breeding herds: Combination of vaccination and biosecurity practices can reduce likelihood of endemic piglet reservoir. Preventive Veterinary Medicine, 138, 55–69. https://doi.org/10.1016/j.prevetmed.2016.12.013

White, L.A., Vandewoude, S. & Craft, M.E. (In press). A mechanistic, stigmergy model of territory formation in an asocial animal: territorial behavior can dampen or drive persistence. PLoS Computational Biology. https://www.biorxiv.org/content/10.1101/796045v1