Niche theory for within-host parasite dynamics: Analogies to food web modules via feedback loops

Why do parasites exhibit a wide dynamical range within their hosts? For instance, why can a parasite only sometimes successfully infect its host? Why do some parasites exhibit large fluctuations? Why do two parasites coinfect, exclude each other, or win only sometimes over another (via priority effects)? For insights, we turn to food webs. An omnivory model (IGP) blueprints one parasite competing with immune cells for host energy (PIE), and a competition model (keystone predation, KP) mirrors a new coinfection model (2PIE). We then draw analogies between models using feedback loops. We translate those loops into the intraspecific direct (DE) and indirect effects (IE) that create various dynamics. Three points arise. First, a prey or parasite can flip between stable and oscillatory coexistence with their enemy with weakening IE and strengthening DE. Second, even with comparable loop structure, a parasite cannot exhibit priority effects seen in IGP due to constraints imposed by production of immune cells. Third, despite simpler loop structure, KP predicts parallel outcomes in the two-parasite model due to comparable structure of interactions between competing victims and their resources and enemies. Hence, food web models offer powerful if imperfect analogies to feedbacks underlying the dynamical repertoire of parasites within hosts.

Avian vampire fly (Philornis downsi) mortality differs across Darwin’s finch host species

In invasive parasites, generalism is considered advantageous during the initial phase of introduction. Thereafter, fitness costs to parasites, such as host-specific mortality, can drive parasites towards specialism to avoid costly hosts. It is important to determine changes in host specificity of invasive populations to understand host-parasite dynamics and their effects on vulnerable host populations. We examined changes in mortality in the introduced avian vampire fly (Philornis downsi) (Diptera: Muscidae), a generalist myasis-causing ectoparasite, between 2004 and 2020 on Floreana Island (Galápagos). Mortality was measured as the proportion of immature larvae found upon host nest termination. Over the time period, the avian vampire fly was most abundant and had low mortality in nests of the critically endangered medium tree finch (Camarhynchus pauper) and had the highest mortality in nests of hybrid tree finches (Camarhynchus spp.). Low larval mortality was also found in small tree (Camarhynchus parvulus) and small ground finch (Geospiza fuliginosa) nests. Selection could favour avian vampire flies that select medium tree finch nests and/or avoid hybrid nests. Overall, the finding of differences in avian vampire fly survival across host species is parsimonious with the idea that the introduced fly may be evolving towards host specialisation.

Mechanistic within-host models of the asexual Plasmodium falciparum infection: a review and analytical assessment

Background Malaria blood-stage infection length and intensity are important drivers of disease and transmission; however, the underlying mechanisms of parasite growth and the host’s immune response during infection remain largely unknown. Over the last 30 years, several mechanistic mathematical models of malaria parasite within-host dynamics have been published and used in malaria transmission models. Methods Mechanistic within-host models of parasite dynamics were identified through a review of published literature. For a subset of these, model code was reproduced and descriptive statistics compared between the models using fitted data. Through simulation and model analysis, key features of the models were compared, including assumptions on growth, immune response components, variant switching mechanisms, and inter-individual variability. Results The assessed within-host malaria models generally replicate infection dynamics in malaria-naïve individuals. However, there are substantial differences between the model dynamics after disease onset, and models do not always reproduce late infection parasitaemia data used for calibration of the within host infections. Models have attempted to capture the considerable variability in parasite dynamics between individuals by including stochastic parasite multiplication rates; variant switching dynamics leading to immune escape; variable effects of the host immune responses; or via probabilistic events. For models that capture realistic length of infections, model representations of innate immunity explain early peaks in infection density that cause clinical symptoms, and model representations of antibody immune responses control the length of infection. Models differed in their assumptions concerning variant switching dynamics, reflecting uncertainty in the underlying mechanisms of variant switching revealed by recent clinical data during early infection. Overall, given the scarce availability of the biological evidence there is limited support for complex models. Conclusions This study suggests that much of the inter-individual variability observed in clinical malaria infections has traditionally been attributed in models to random variability, rather than mechanistic disease dynamics. Thus, it is proposed that newly developed models should assume simple immune dynamics that minimally capture mechanistic understandings and avoid over-parameterization and large stochasticity which inaccurately represent unknown disease mechanisms.

How host phenology impacts multi-season epidemic cycles

Parasite-host interactions can result in periodic population dynamics when parasites over-exploit host populations. The timing of host seasonal activity, or host phenology, determines the frequency and demographic impact of parasite-host interactions which may govern if the parasite can sufficiently over-exploit their hosts to drive population cycles. We describe a mathematical model of a monocyclic, obligate-killer parasite system with seasonal host activity to investigate the consequences of host phenology on host-parasite dynamics. The results suggest that parasites can reach the densities necessary to destabilize host dynamics and drive cycling in only some phenological scenarios, such as environments with short seasons and synchronous host emergence. Further, only parasite lineages that are sufficiently adapted to phenological scenarios with short seasons and synchronous host emergence can achieve the densities necessary to over-exploit hosts and produce population cycles. Host-parasite cycles can also generate an eco-evolutionary feedback that slows parasite adaptation to the phenological environment as rare advantageous phenotypes are driven to extinction when introduced in phases of the cycle where host populations are small and parasite populations are large. The results demonstrate that seasonal environments can drive population cycling in a restricted set of phenological patterns and provides further evidence that the rate of adaptive evolution depends on underlying ecological dynamics.

Mechanistic within-host models of the asexual Plasmodium falciparum infection: a review and analytical assessment

Background: Malaria blood-stage infection length and intensity are important drivers of disease and transmission; however, the underlying mechanisms of parasite growth and the host's immune response during infection remain largely unknown. Over the last 30 years, several mechanistic mathematical models of malaria parasite within-host dynamics have been published and used in malaria transmission models. Methods: We identified mechanistic within-host models of parasite dynamics through a review of published literature. For a subset of these, we reproduced model code and compared descriptive statistics between the models using fitted data. Through simulation and model analysis, we compare and discuss key features of the models, including assumptions on growth, immune response components, variant switching mechanisms, and inter-individual variability. Results: The assessed within-host malaria models generally replicate infection dynamics in malaria-na&iumlve individuals. However, there are substantial differences between the model dynamics after disease onset, and models do not always reproduce late infection parasitemia data used for calibration of the within host infections. Models have attempted to capture the considerable variability in parasite dynamics between individuals by including stochastic parasite multiplication rates; variant switching dynamics leading to immune escape; variable effects of the host immune responses; or via probabilistic events. For models that capture realistic length of infections, model representations of innate immunity explain early peaks in infection density that cause clinical symptoms, and model representations of antibody immune responses control the length of infection. Models differed in their assumptions concerning variant switching dynamics, reflecting uncertainty in the underlying mechanisms of variant switching revealed by recent clinical data during early infection. Overall, given the scarce availability of the biological evidence there is limited support for complex models. Conclusions: Our study suggests that much of the inter-individual variability observed in clinical malaria infections has traditionally been attributed in models to random variability, rather than mechanistic disease dynamics. Thus, we propose that newly developed models should assume simple immune dynamics that minimally capture mechanistic understandings and avoid over-parameterisation and large stochasticity which inaccurately represent unknown disease mechanisms.

Semi-mechanistic pharmacokinetic and pharmacodynamic modelling of piperaquine in a volunteer infection study with Plasmodium falciparum blood-stage malaria

Dihydroartemisinin-piperaquine is a recommended first-line artemisinin combination therapy for falciparum malaria. Piperaquine is also under consideration for other antimalarial combination therapies. The aim of this study was to develop a pharmacokinetic-pharmacodynamic model that could be used to optimize the use of piperaquine in new antimalarial combination therapies. The pharmacokinetic-pharmacodynamic model was developed using data from a previously reported dose-ranging study where 24 healthy volunteers were inoculated 1,800 blood-stage Plasmodium falciparum parasites. All volunteers received a single oral dose of piperaquine (960 mg, 640 mg, or 480 mg) on day 7 or day 8 after parasite inoculation in separate cohorts. Parasite densities were measured by qPCR, and piperaquine levels were measured in plasma samples. We used nonlinear mixed-effect modelling to characterize the pharmacokinetic properties of piperaquine and the parasite dynamics associated with piperaquine exposure. Pharmacokinetics of piperaquine was described by a three-compartment disposition model. A semi-mechanistic parasite dynamics model was developed to explain maturation of parasites, sequestration of mature parasites, synchronicity of infections, and multiplication of parasites, as seen in natural clinical infections with falciparum malaria. Piperaquine-associated parasite killing was estimated using a maximum effect (Emax) function. Treatment simulations (i.e. 3-day oral dosing of dihydroartemisinin-piperaquine) indicated that to be able to combat multidrug resistant infections, an ideal additional drug in a new antimalarial triple-combination therapy should have a parasite reduction ratio of ≥102 per life cycle (38.8 h) with a duration of action of ≥ 2 weeks. The semi-mechanistic pharmacokinetic-pharmacodynamic model described here offers the potential to be a valuable tool to assess and optimize current and new antimalarial drug combinations therapies containing piperaquine, and the impact of these therapies on killing multidrug resistant infections.

Effects of climate change on parasites and disease in estuarine and nearshore environments

Information on parasites and disease in marine ecosystems lags behind terrestrial systems, increasing the challenge of predicting responses of marine host–parasite systems to climate change. However, here I examine several generalizable aspects and research priorities. First, I advocate that quantification and comparison of host and parasite thermal performance curves is a smart approach to improve predictions of temperature effects on disease. Marine invertebrate species are ectothermic and should be highly conducive to this approach given their generally short generation times. Second, in marine systems, shallow subtidal and intertidal areas will experience the biggest temperature swings and thus likely see the most changes to host–parasite dynamics. Third, for some responses like parasite intensity, as long as the lethal limit of the parasite is not crossed, on average, there may be a biological basis to expect temperature-dependent intensification of impacts on hosts. Fourth, because secondary mortality effects and indirect effects of parasites can be very important, we need to study temperature effects on host–parasite dynamics in a community context to truly know their bottom line effects. This includes examining climate-influenced effects of parasites on ecosystem engineers given their pivotal role in communities. Finally, other global change factors, especially hypoxia, salinity, and ocean acidity, covary with temperature change and need to be considered and evaluated when possible for their contributing effects on host–parasite systems. Climate change–disease interactions in nearshore marine environments are complex; however, generalities are possible and continued research, especially in the areas outlined here, will improve our understanding.