A combined within-host and between-hosts modelling framework for the evolution of resistance to antimalarial drugs

The spread of drug resistance represents a significant challenge to many disease control efforts. The evolution of resistance is a complex process influenced by transmission dynamics between hosts as well as infection dynamics within these hosts. This study aims to investigate how these two processes combine to impact the evolution of resistance in malaria parasites. We introduce a stochastic modelling framework combining an epidemiological model of Plasmodium transmission and an explicit within-human infection model for two competing strains. Immunity, treatment and resistance costs are included in the within-host model. We show that the spread of resistance is generally less likely in areas of intense transmission, and therefore of increased competition between strains, an effect exacerbated when costs of resistance are higher. We also illustrate how treatment influences the spread of resistance, with a trade-off between slowing resistance and curbing disease incidence. We show that treatment coverage has a stronger impact on disease prevalence, whereas treatment efficacy primarily affects resistance spread, suggesting that coverage should constitute the primary focus of control efforts. Finally, we illustrate the importance of feedbacks between modelling scales. Overall, our results underline the importance of concomitantly modelling the evolution of resistance within and between hosts.

Can Horton hear the whos? The importance of scale in mosquito-borne disease

The epidemiology of vector-borne pathogens is determined by mechanisms and interactions at different scales of biological organization, from individual-level cellular processes to community interactions between species and with the environment. Most research, however, focuses on one scale or level with little integration between scales or levels within scales. Understanding the interactions between levels and how they influence our perception of vector-borne pathogens is critical. Here two examples of biological scales (pathogen transmission and mosquito mortality) are presented to illustrate some of the issues of scale and to explore how processes on different levels may interact to influence mosquito-borne pathogen transmission cycles. Individual variation in survival, vector competence, and other traits affect population abundance, transmission potential, and community structure. Community structure affects interactions between individuals such as competition and predation, and thus influences the individual-level dynamics and transmission potential. Modeling is a valuable tool to assess interactions between scales and how processes at different levels can affect transmission dynamics. We expand an existing model to illustrate the types of studies needed, showing that individual-level variation in viral dose acquired or needed for infection can influence the number of infectious vectors. It is critical that interactions within and among biological scales and levels of biological organization are understood for greater understanding of pathogen transmission with the ultimate goal of improving control of vector-borne pathogens.

Implications of within-farm transmission for network dynamics: consequences for the spread of avian influenza

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Cross-scale dynamics were investigated for avian influenza in British poultry.

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Transmission risk is dependent on the assumed within-flock transmission mode.

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Transmission risk may not scale with transmissibility or flock size.

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Transmission risk corresponds with between-farm impact for 28% of farms.

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These results have implications for targeted disease control at the farm-level.

The importance of considering coupled interactions across multiple population scales has not previously been studied for highly pathogenic avian influenza (HPAI) in the British commercial poultry industry. By simulating the within-flock transmission of HPAI using a deterministic S-E-I-R model, and by incorporating an additional environmental class representing infectious faeces, we tracked the build-up of infectious faeces within a poultry house over time. A measure of the transmission risk (TR) was computed for each farm by linking the amount of infectious faeces present each day of an outbreak with data describing the daily on-farm visit schedules for a major British catching company. Larger flocks tended to have greater levels of these catching-team visits. However, where density-dependent contact was assumed, faster outbreak detection (according to an assumed mortality threshold) led to a decreased opportunity for catching-team visits to coincide with an outbreak. For this reason, maximum TR-levels were found for mid-range flock sizes (~25,000–35,000 birds). When assessing all factors simultaneously using multivariable linear regression on the simulated outputs, those related to the pattern of catching-team visits had the largest effect on TR, with the most important movement-related factor depending on the mode of transmission. Using social network analysis on a further database to inform a measure of between-farm connectivity, we identified a large fraction of farms (28%) that had both a high TR and a high potential impact at the between farm level. Our results have counter-intuitive implications for between-farm spread that could not be predicted based on flock size alone, and together with further knowledge of the relative importance of transmission risk and impact, could have implications for improved targeting of control measures.

Landscape epidemiology of emerging infectious diseases in natural and human-altered ecosystems

A central challenge to studying emerging infectious diseases (EIDs) is a landscape dilemma: Our best empirical understanding of disease dynamics occurs at local scales, whereas pathogen invasions and management occur over broad spatial extents. The burgeoning field of landscape epidemiology integrates concepts and approaches from disease ecology with the macroscale lens of landscape ecology, enabling examination of disease across spatiotemporal scales in complex environmental settings. We review the state of the field and describe analytical frontiers that show promise for advancement, focusing on natural and human-altered ecosystems. Concepts fundamental to practicing landscape epidemiology are discussed, including spatial scale, static versus dynamic modeling, spatially implicit versus explicit approaches, selection of ecologically meaningful variables, and inference versus prediction. We highlight studies that have advanced the field by incorporating multiscale analyses, landscape connectivity, and dynamic modeling. Future research directions include understanding disease as a component of interacting ecological disturbances, scaling up the ecological impacts of disease, and examining disease dynamics as a coupled human-natural system.

The evolution of plant pathogens in response to host resistance: factors affecting the gain from deployment of qualitative and quantitative resistance

Disease resistance genes are valuable natural resources which should be deployed in a way which maximises the gain to crop productivity before they lose efficacy. Here we present a general epidemiological model for plant diseases, formulated to study the evolution of phenotypic traits of plant pathogens in response to host resistance. The model was used to analyse how the characteristics of the disease resistance, and the method of deployment, affect the size and duration of the gain. The gain obtained from growing a resistant cultivar, compared to a susceptible cultivar, was quantified as the increase in green canopy area resulting from control of foliar disease, integrated over many years-termed 'Healthy Area Duration (HAD) Gain'. Previous work has suggested that the effect of crop ratio (the proportion of land area occupied by the resistant crop) on the gain from qualitative (gene-for-gene) resistance is negligible. Increasing the crop ratio increases the area of uninfected host, but the resistance is more rapidly broken; these two effects counteract each other. We tested the hypothesis that similar counteracting effects would occur for quantitative, multi-genic resistance, but found that the HAD Gain increased at higher crop ratios. Then we tested the hypothesis that the gain from quantitative host resistance could differ depending on the life-cycle component (sporulation rate or infection efficiency) constrained by the resistance. For the patho-system considered, a quantitative resistant cultivar that reduced the infection efficiency gave a greater HAD Gain than a cultivar that reduced sporulation rate, despite having equivalent transmission rates.

Spatial scaling relationships for spread of disease caused by a wind-dispersed plant pathogen

Spatial scale is of great importance to understanding the spread of organisms exhibiting long-distance dispersal (LDD). We tested whether epidemics spread in direct proportion to the size of the host population and size of the initial disease focus. This was done through analysis of a previous study of the effects of landscape heterogeneity variables on the spread of accelerating epidemics of wheat (Triticum aestivum) stripe rust, caused by the fungus Puccinia striiformis f. sp. tritici. End-of-season disease gradients were constructed by estimating disease prevalence at regular distances from artificially inoculated foci of different sizes, in field plots of different dimensions. In one set of comparisons, all linear dimensions (plot width and length, focus width and length, and distance between observation points) differed by a factor of four. Disease spread was substantially greater in large plot/large focus treatments than in small plot/small focus treatments. However, when disease gradients were plotted using focus width as the unit distance, they were found to be highly similar, suggesting a proportional relationship between focus or plot size and disease spread. A similar relationship held when comparing same-size plots inoculated with different-sized foci, an indication that focus size is the driver of this proportionality. Our results suggest that power law dispersal of LDD organisms results in scale-invariant relationships, which are useful for better understanding spatial spread of biological invasions, extrapolating results from small-scale experiments to invasions spreading over larger scales, and predicting speed and pattern of spread as an invasion expands.

An ecological and comparative perspective on the control of bovine tuberculosis in Great Britain and the Republic of Ireland

Disease ecology involves a systematic approach to understanding the interactions and evolution of host-pathogen systems at the population level, and is essential for developing a comprehensive understanding of the reasons for disease persistence and the most likely means of control. This systems or ecological approach is being increasingly recognised as a progressive method in disease control and is exploited in diverse fields ranging from obesity management in humans to the prevention of infectious disease in animal populations. In this review we discuss bovine tuberculosis (bTB) in Great Britain (GB) within a disease ecology context, and suggest how a comparative ecological perspective helps to reconcile apparent conflicts with the evidence on the effectiveness of badger culling to assist in the control of bTB in GB and the Republic of Ireland (ROI). Our examination shows that failure of past measures to control bTB and the disparity in outcomes of badger culling experiments are the result of a complex relationship amongst the agent, host and environment, i.e. the episystem, of bTB. Here, we stress the role of distinctive bTB episystems and badger culling trial design in the ambiguity and resulting controversy associated with badger culling in GB and ROI. We argue this episystem perspective on bTB control measures in cattle and badger populations provides a useful and informative perspective on the design and implementation of future bTB management in GB, particularly at a time when both scientific and lay communities are concerned about the ongoing epidemic, the cost of current control measures and the execution of future control procedures.

Contrasting landscape epidemiology of two sympatric rabies virus strains

Viral strain evolution and disease emergence are influenced by anthropogenic change to the environment. We investigated viral characteristics, host ecology, and landscape features in the rabies-striped skunk disease system of the central Great Plains to determine how these factors interact to influence disease emergence. We amplified portions of the N and G genes of rabies viral RNA from 269 samples extracted from striped skunk brains throughout the distribution of two different rabies strains for which striped skunks were the reservoir. Because the distribution of these two strains overlapped on the landscape and were present in the same host population, we could evaluate how viral properties influenced epidemiological patterns in the area of sympatry. We found that South Central Skunk rabies (SCSK) exhibited intense purifying selection and high infectivity, which are both characteristics of an epizootic virus. Conversely, North Central Skunk rabies (NCSK) exhibited relaxed purifying selection and comparatively lower infectivity, suggesting the presence of an enzootic virus. The host population in the area of sympatry was highly admixed, and skunks among allopatric and sympatric areas had similar effective population sizes. Spatial analysis indicated that landscape features had minimal influence on NCSK movement across the landscape, but those same features were partial barriers to the spread of SCSK. We conclude that NCSK and SCSK have different epidemiological properties that interact differently with both host and landscape features to influence rabies spread in the central Great Plains. We suggest a holistic approach for future studies of emerging infectious diseases that includes studies of viral properties, host characteristics, and spatial features.

Synthesizing within-host and population-level selective pressures on viral populations: the impact of adaptive immunity on viral immune escape

The evolution of viruses to escape prevailing host immunity involves selection at multiple integrative scales, from within-host viral and immune kinetics to the host population level. In order to understand how viral immune escape occurs, we develop an analytical framework that links the dynamical nature of immunity and viral variation across these scales. Our epidemiological model incorporates within-host viral evolutionary dynamics for a virus that causes acute infections (e.g. influenza and norovirus) with changes in host immunity in response to genetic changes in the virus population. We use a deterministic description of the within-host replication dynamics of the virus, the pool of susceptible host cells and the host adaptive immune response. We find that viral immune escape is most effective at intermediate values of immune strength. At very low levels of immunity, selection is too weak to drive immune escape in recovered hosts, while very high levels of immunity impose such strong selection that viral subpopulations go extinct before acquiring enough genetic diversity to escape host immunity. This result echoes the predictions of simpler models, but our formulation allows us to dissect the combination of within-host and transmission-level processes that drive immune escape.

Importance of autoinfection to the epidemiology of polycyclic foliar disease

Autoinfection (infection resulting from inoculum produced on the same host unit) can result in strongly clustered disease at the local scale. In contrast, much epidemiological theory incorporates the simplification of spatially random or uniform infection. Earlier studies suggested only low to moderate levels of autoinfection, especially when the host unit is small. However, several studies published within the last 5 years suggest that autoinfection rates may be substantially higher than previously indicated. I discuss the potential importance of accounting for high autoinfection rates in example epidemiological processes that occur at different spatial scales: microbial interactions on the phylloplane, temporal disease progression in plant populations, and spatiotemporal disease spread at the landscape scale. Accounting for high autoinfection rates can have important qualitative and quantitative consequences for epidemiological processes, and further studies of autoinfection will contribute significantly to our understanding of epidemics.

Key determinants of resistance durability to plant viruses: insights from a model linking within- and between-host dynamics

The emergence of new genotypes of parasites involves several evolutionary, epidemiological and ecological processes whose individual effects and interactions are difficult to disentangle using experimental approaches. Here, a model is proposed to investigate how these processes lead to the emergence of plant viral genotypes breaking down qualitative resistance genes. At the individual plant scale, selection, drift and mutation processes shape the evolution of viral populations from a set of differential equations. The spatial segregation of virus genotypes in their hosts is also considered. At the host population scale, the epidemiological dynamics is given by an individual-based algorithm. Global sensitivity analyses allowed ranking the ten demo-genetic and epidemiological parameters of the model according to their impact on the mean and variance of the risk of breakdown of a plant resistance. Demo-genetic parameters (number and nature of mutations involved in breakdown, fitness of mutant genotypes) had the largest impact on the mean breakdown risk, whereas epidemiological parameters had more influence on its standard deviation. It is discussed how these results can be used to choose the potentially most durable resistance genes among a pool of candidates. Finally, our analyses point out the parameters which should be estimated more precisely to improve durability predictions.