An earlier mathematical model exploring the use of genetically manipulated mosquitoes for malaria control suggested that the prevalence of malaria is reduced significantly only if almost all mosquitoes become completely resistant to malaria. Central to the model was the ‘cost of resistance’: the reduction of a resistant mosquito's evolutionary fitness in comparison with a sensitive one's. Here, we consider the possibility of obtaining more optimistic outcomes by taking into account the epidemiological (in addition to the evolutionary) consequences of a cost of resistance that decreases the life-span of adult mosquitoes (the most relevant parameter for the parasite's epidemiology). There are two main results. First, if despite its cost, resistance is fixed in the population, increasing the cost of resistance decreases the intensity of transmission. However, this epidemiological effect is weak if resistance is effective enough to be considered relevant for control. Second, if the cost of resistance prevents its fixation, increasing it intensifies transmission. Thus, the epidemiological effect of the cost of resistance cannot compensate for the lower frequency of resistant mosquitoes in the population. Overall, our conclusion remains pessimistic: so that genetic manipulation can become a promising method of malaria control, we need techniques that enable almost all mosquitoes to be almost completely resistant to infection.

Vaccines that target antigens found on the mosquito stages of Plasmodium falciparum and Plasmodium vivax parasites are under development as transmission blocking vaccines. Antisera from vaccinated animals and humans are able to block oocyst development in artificially fed mosquitoes but it is not clear from these data what level of antibody response would be required for a useful vaccine in a field setting. This paper describes a mathematical model that takes into account the relationship between antibody levels and blocking of oocyst levels in artificial feeds, the distribution of antibody responses seen in human populations and the distribution of oocyst densities in infected mosquitoes in the field to calculate the levels of antibody in the host population that would be required to achieve a level of herd immunity in a vaccinated human population that would give an operationally useful level of transmission blocking. The model predicts that current formulations of Pfs25 are likely to achieve useful reductions in transmission when tested in human field trials.

Planning of the control of Plasmodium falciparum malaria leads to a need for models of malaria epidemiology that provide realistic quantitative prediction of likely epidemiological outcomes of a wide range of control strategies. Predictions of the effects of control often ignore medium- and long-term dynamics. The complexities of the Plasmodium life-cycle, and of within-host dynamics, limit the applicability of conventional deterministic malaria models. We use individual-based stochastic simulations of malaria epidemiology to predict the impacts of interventions on infection, morbidity, mortality, health services use and costs. Individual infections are simulated by stochastic series of parasite densities, and naturally acquired immunity acts by reducing densities. Morbidity and mortality risks, and infectiousness to vectors, depend on parasite densities. The simulated infections are nested within simulations of individuals in human populations, and linked to models of interventions and health systems. We use numerous field datasets to optimise parameter estimates. By using a volunteer computing system we obtain the enormous computational power required for model fitting, sensitivity analysis, and exploration of many different intervention strategies. The project thus provides a general platform for comparing, fitting, and evaluating different model structures, and for quantitative prediction of effects of different interventions and integrated control programmes.

In a recent experiment, we found that mice previously infected with Bordetella pertussis were not protected against a later infection with Bordetella parapertussis, while primary infection with B. parapertussis conferred cross-protection. This challenges the common assumption made in most mathematical models for pathogenic strain dynamics that cross-immunity between strains is symmetric. Here we investigate the potential consequences of this pattern on the circulation of the two pathogens in human populations. To match the empirical dominance of B. pertussis, we made the additional assumption that B. parapertussis pays a cost in terms of reduced fitness. We begin by exploring the range of parameter values that allow the coexistence of the two pathogens, with or without vaccination. We then track the dynamics of the system following the introduction of anti-pertussis vaccination. Our results suggest that (1) in order for B. pertussis to be more prevalent than B. parapertussis, the former must have a strong competitive advantage, possibly in the form of higher infectivity, and (2) because of asymmetric cross-immunity, the introduction of anti-pertussis vaccination should have little effect on the absolute prevalence of B. parapertussis. We discuss the evidence supporting these predictions, and the potential relevance of this model for other pathogens.

Deer are important blood hosts for feeding Ixodes ricinus ticks but they do not support transmission of many tick-borne pathogens, so acting as dead-end transmission hosts. Mathematical models show their role as tick amplifiers, but also suggest that they dilute pathogen transmission, thus reducing infection prevalence. Empirical evidence for this is conflicting: experimental plots with deer removal (i.e. deer exclosures) show that the effect depends on the size of the exclosure. Here we present simulations of dynamic models that take into account different tick stages, and several host species (e.g. rodents) that may move to and from deer exclosures; models were calibrated with respect to Ixodes ricinus ticks and tick-borne encephalitis (TBE) in Trentino (northern Italy). Results show that in small exclosures, the density of rodent-feeding ticks may be higher inside than outside, whereas in large exclosures, a reduction of such tick density may be reached. Similarly, TBE prevalence in rodents decreases in large exclosures and may be slightly higher in small exclosures than outside them. The density of infected questing nymphs inside small exclosures can be much higher, in our numerical example almost twice as large as that outside, leading to potential TBE infection risk hotspots.

Hosts are typically simultaneously co-infected by a variety of microparasites (e.g. viruses and bacteria) and macroparasites (e.g. parasitic helminths). However, the population dynamical consequences of such co-infections and the implications for the effectiveness of imposed control programmes have yet to be fully realised. Mathematical models may provide an important framework for exploring such issues and have proved invaluable in helping to understand the factors affecting the epidemiology of single parasitic infections. Here the first population dynamic model of microparasite-macroparasite co-infection is presented and used to explore how co-infection alters the predictions of the existing single-species models. It is shown that incorporating an additional parasite species into existing models can greatly stabilise them, due to the combined density-dependent impacts on the host population, but co-infection can also restrict the region of parameter space where each species could persist alone. Overall it is concluded that the dynamic feedback between host, microparasite and macroparasite means that it is difficult to appreciate the factors affecting parasite persistence and predict the effectiveness of control by just studying one component in isolation.

Mathematical models of disease dynamics tend to assume that individuals within a population mix at random and so transmission is random, and yet, in reality social structure creates heterogeneous contact patterns. We investigated the effect of heterogeneity in host contact patterns on potential macroparasite transmission by first quantifying the level of assortativity in a socially structured wild rodent population (Apodemus flavicollis) with respect to the directly-transmitted macroparasitic helminth, Heligmosomoides polygyrus. We found the population to be disassortatively mixed (i.e. male mice mixing with female mice more often than same sex mixing) at a constant level over time. The macroparasite H. polygyrus has previously been shown to exhibit male-biased transmission so we used a Susceptible-Infected (SI) mathematical model to simulate the effect of increasing strengths of male-biased transmission on the prevalence of the macroparasite using empirically-derived transmission networks. When transmission was equal between the sexes the model predicted macroparasite prevalence to be 73% and infection was male biased (82% of infection in the male mice). With a male-bias in transmission ten times that of the females, the expected macroparasite prevalence was 50% and was equally prevalent in both sexes, results that both most closely resembled empirical dynamics. As such, disassortative mixing alone did not produce macroparasite dynamics analogous to those from empirical observations; a strong male-bias in transmission was also required. We discuss the relevance of our results in the context of network models for transmission dynamics and control.

Regular distribution of ivermectin reduces onchocerciasis transmission and morbidity by killing, within humans, the microfilarial stage of the parasite (microfilaricidal effect). In addition, ivermectin exerts a so-called embryostatic effect by which microfilarial production by the adult female worm becomes suppressed during a number of weeks after treatment. To assess the overall effect of ivermectin on onchocerciasis transmission and evaluate the likelihood of local elimination of the infection it is important to estimate the magnitude of the anti-fertility effect over the course of a treatment programme. Estimates of the effect of repeated drug treatments on the production of microfilariae by adult Onchocerca volvulus were obtained by developing a model that was fitted to data collected from three hyperendemic communities in Guatemala, where eligible residents received ivermectin twice per year for two and a half years. The data consist of microfilarial load measurements in the skin, collected just before each six-monthly treatment during the programme. The model that is developed describes the dynamics of an individual host's expected microfilarial load over the 30-month study period. We adopt a Bayesian approach and use Markov chain Monte Carlo (McMC) techniques to fit the model to the data. Combining estimates from the three villages, average microfilarial production in the first six months post-treatment was reduced by ~64% of its pre-treatment level, regardless of values chosen for the pre-ivermectin fertility rate within plausible ranges. Increased adult worm death rate after treatment (to mimic removal of macrofilariae via nodulectomy during the programme) resulted in a smaller estimated magnitude of the embryostatic effect (rate of microfilarial production was reduced by ~58% of pre-ivermectin value). After subsequent treatments, the rate of microfilarial production appeared to be similarly decreased. The data and analyses therefore do not support the hypothesis of a cumulative effect of multiple ivermectin treatments on microfilarial production by female worms.

LYMFASIM is a simulation model for lymphatic filariasis transmission and control. We quantified its parameters to simulate Wuchereria bancrofti transmission by Anopheles mosquitoes in African villages, using a wide variety of reported data. The developed model captures the general epidemiological patterns, but also the differences between communities. It was calibrated to represent the relationship between mosquito biting rate and the prevalence of microfilariae (mf) in the human population, the age-pattern in mf prevalence, and the relation between mf prevalence and geometric mean mf intensity. Explorative simulations suggest that the impact of mass treatment depends strongly on the mosquito biting rate and on the assumed coverage, compliance and efficacy. Our sensitivity analysis showed that some biological parameters strongly influence the predicted equilibrium pre-treatment mf prevalence (e.g. the lifespan of adult worms and mf). Other parameters primarily affect the post-treatment trends (e.g. severity of density dependence in the mosquito uptake of infection from the human blood, between-person variability in exposure to mosquito bites). The longitudinal data, which are being collected for evaluation of ongoing elimination programmes, can help to further validate the model. The model can help to assess when ongoing elimination activities in African populations can be stopped and to design surveillance schemes. It can be a valuable tool for decision making in the Global Programme to Eliminate Lymphatic Filariasis.

Infection caused by parasitic nematodes of humans and livestock can have significant health and economic costs. Treatments aimed at alleviating these costs, such as chemotherapy and vaccination, alter parasite survival and reproduction, the main selective pressures shaping life-history traits such as age to maturity, size and fecundity. Most authors have argued that the life-history evolution prompted by animal and public health programmes would be clinically beneficial, generating smaller, less fecund worms, and several mathematical models support this view. However, using mathematical models of long-lasting interventions, such as vaccination, and regularly repeated short interventions, such as drenching, we show here that the expected outcome actually depends on how mortality rates vary as a function of worm size and developmental status. Interventions which change mortality functions can exert selection pressure to either shorten or extend the time to maturity, and thus increase or decrease worm fecundity and size. The evolutionary trajectory depends critically on the details of the mortality functions with and without the intervention. Earlier optimism that health interventions would always prompt the evolution of smaller, less fecund and hence clinically less damaging worms is premature.