No abstract

Temporal variations in the activity of arthropod vectors can dramatically affect the epidemiology and evolution of vector‐borne pathogens. Here, we explore the “Hawking hypothesis”, which states that these pathogens may evolve the ability to time investment in transmission to match the activity of their vectors. First, we use a theoretical model to identify the conditions promoting the evolution of time‐varying transmission strategies in pathogens. Second, we experimentally test the “Hawking hypothesis” by monitoring the within‐host dynamics of Plasmodium relictum throughout the acute and the chronic phases of the bird infection. We detect a periodic increase of parasitemia and mosquito infection in the late afternoon that coincides with an increase in the biting activity of its natural vector. We also detect a positive effect of mosquito bites on Plasmodium replication in the birds both in the acute and in the chronic phases of the infection. This study highlights that Plasmodium parasites use two different strategies to increase the match between transmission potential and vector availability. We discuss the adaptive nature of these unconditional and plastic transmission strategies with respect to the time scale and the predictability of the fluctuations in the activity of the vector.

Resources are a core currency of species interactions and ecology in general (e.g., think of food webs or competition). Within parasite-infected hosts, resources are divided among the competing demands of host immunity and growth as well as parasite reproduction and growth. Effects of resources on immune responses are increasingly understood at the cellular level (e.g., metabolic predictors of effector function), but there has been limited consideration of how these effects scale up to affect individual energetic regimes (e.g., allocation trade-offs), susceptibility to infection, and feeding behavior (e.g., responses to local resource quality and quantity). We experimentally rewilded laboratory mice (strain C57BL/6) in semi-natural enclosures to investigate the effects of dietary protein and gastrointestinal nematode (Trichuris muris) infection on individual-level immunity, activity, and behavior. The scale and realism of this field experiment, as well as the multiple physiological assays developed for laboratory mice, enabled us to detect costs, trade-offs, and potential compensatory mechanisms that mice employ to battle infection under different resource conditions. We found that mice on a low-protein diet spent more time feeding, which led to higher body fat stores (i.e., concentration of a satiety hormone, leptin) and altered metabolite profiles, but which did not fully compensate for the effects of poor nutrition on albumin or immune defenses. Specifically, immune defenses measured as interleukin 13 (IL13) (a primary cytokine coordinating defense against T. muris) and as T. muris-specific IgG1 titers were lower in mice on the low-protein diet. However, these reduced defenses did not result in higher worm counts in mice with poorer diets. The lab mice, living outside for the first time in thousands of generations, also consumed at least 26 wild plant species occurring in the enclosures, and DNA metabarcoding revealed that the consumption of different wild foods may be associated with differences in leptin concentrations. When individual foraging behavior was accounted for, worm infection significantly reduced rates of host weight gain. Housing laboratory mice in outdoor enclosures provided new insights into the resource costs of immune defense to helminth infection and how hosts modify their behavior to compensate for those costs.

A standard assumption in the modelling of epidemic dynamics is that the population of interest is well mixed, and that no clusters of metapopulations exist. The well-known and oft-used SIR model, arguably the most important compartmental model in theoretical epidemiology, assumes that the disease being modelled is strongly immunizing, directly transmitted and has a well-defined period of infection, in addition to these population mixing assumptions. Childhood infections, such as measles, are prime examples of diseases that fit the SIR-like mechanism. These infections have been well studied for many systems with large, well-mixed populations with endemic infection. Here, we consider a setting where populations are small and isolated. The dynamics of infection are driven by stochastic extinction–recolonization events, producing large, sudden and short-lived epidemics before rapidly dying out from a lack of susceptible hosts. Using a TSIR model, we fit prevaccination measles incidence and demographic data in Bornholm, the Faroe Islands and four districts of Iceland, between 1901 and 1965. The datasets for each of these countries suffer from different levels of data heterogeneity and sparsity. We explore the potential for prediction of this model: given historical incidence data and up-to-date demographic information, and knowing that a new epidemic has just begun, can we predict how large it will be? We show that, despite a lack of significant seasonality in the incidence of measles cases, and potentially severe heterogeneity at the population level, we are able to estimate the size of upcoming epidemics, conditioned on the first time step, to within reasonable confidence. Our results have potential implications for possible control measures for the early stages of new epidemics in small populations.

Apart from its global health importance, measles is a paradigm for low-dimensional mechanistic understanding of local nonlinear population interactions. A central question for spatio-temporal dynamics is the relative role of hierarchical spread from large cities to small towns and 'metapopulation' transmission among local small population clusters in measles persistence. Quantifying this balance is critical to planning regional elimination and global eradication of measles. Yet, current gravity models do not allow a formal comparison of hierarchical versus metapopulation spread. We address this gap with a competing-risks framework, capturing the relative importance of competing sources of reintroductions of infection. We apply the method to the uniquely spatio-temporally detailed urban incidence data set for measles in England and Wales (available in the Supporting Materials), from 1944 to the infection's vaccine-induced nadir in the 1990s. We find that despite the regional influence of a few large cities (e.g. London and Liverpool) metapopulation aggregation in neighboring towns and cities plays an important role in driving national dynamics in the prevaccination era. As vaccination levels increased in the 1970s and 80s, the signature of spatially predictable spread diminished: increasingly, infection was introduced from unidentifiable random sources possibly outside regional metapopulations. The resulting erratic dynamics highlight the challenges of identifying shifting sources of infection and characterizing patterns of incidence in times of high vaccination coverage. More broadly, the underlying incidence and demographic data, accompanying this paper, will also provide a significant resource for exploring nonlinear spatiotemporal population