Multidisciplinary studies, systems approaches and parasite eco-epidemiology: something old, something new

Parasite requires an understanding of complex transmission systems where individual, population and environmental factors and their interactions can hardly been considered separately. Moreover, the importance of space and time in host population and parasite transmission processes is increasingly recognised. The present review illustrates how epidemiology and transmission ecology have evolved in a multidisciplinary framework to a systems approach that includes both spatial and temporal dimensions. Focusing on population processes, three significant challenges are discussed: (i) integration of landscape ecology concepts and modelling across time-space scales, (ii) development of molecular methods that permits easy parasite/host identification and process tracking (e.g. host and parasite movements), and (iii) integration of sociology methods to estimate zoonotic risk and exposure.     

Spatial epidemiology: an emerging (or re-emerging) discipline

Spatial epidemiology is the study of spatial variation in disease risk or incidence. Several ecological processes can result in strong spatial patterns of such risk or incidence: for example, pathogen dispersal might be highly localized, vectors or reservoirs for pathogens might be spatially restricted, or susceptible hosts might be clumped. Here, we briefly describe approaches to spatial epidemiology that are spatially implicit, such as metapopulation models of disease transmission, and then focus on research in spatial epidemiology that is spatially explicit, such as the creation of risk maps for particular geographical areas. Although the spatial dynamics of infectious diseases are the subject of intensive study, the impacts of landscape structure on epidemiological processes have so far been neglected. The few studies that demonstrate how landscape composition (types of elements) and configuration (spatial positions of those elements) influence disease risk or incidence suggest that a true integration of landscape ecology with epidemiology will be fruitful.     

Infection dynamics of endemic malaria in a wild bird population: parasite species-dependent drivers of spatial and temporal variation in transmission rates

1.?Investigating the ecological context in which host-parasite interactions occur and the roles of biotic and abiotic factors in forcing infection dynamics is essential to understanding disease transmission, spread and maintenance. 2.?Despite their prominence as model host-pathogen systems, the relative influence of environmental heterogeneity and host characteristics in influencing the infection dynamics of avian blood parasites has rarely been assessed in the wild, particularly at a within-population scale. 3.?We used a novel multievent modelling framework (an extension of multistate mark-recapture modelling) that allows for uncertainty in disease state, to estimate transmission parameters and assess variation in the infection dynamics of avian malaria in a large, longitudinally sampled data set of breeding blue tits infected with two divergent species of Plasmodium parasites. 4.?We found striking temporal and spatial heterogeneity in the disease incidence rate and the likelihood of recovery within this single population and demonstrate marked differences in the relative influence of environmental and host factors in forcing the infection dynamics of the two Plasmodium species. 5.?Proximity to a permanent water source greatly influenced the transmission rates of P.?circumflexum, but not of P.?relictum, suggesting that these parasites are transmitted by different vectors. 6.?Host characteristics (age/sex) were found to influence infection rates but not recovery rates, and their influence on infection rates was also dependent on parasite species: P.?relictum infection rates varied with host age, whilst P.?circumflexum infection rates varied with host sex. 7.?Our analyses reveal that transmission of endemic avian malaria is a result of complex interactions between biotic and abiotic components that can operate on small spatial scales and demonstrate that knowledge of the drivers of spatial and temporal heterogeneity in disease transmission will be crucial for developing accurate epidemiological models and a thorough understanding of the evolutionary implications of pathogens.     

Advances in satellite remote sensing of pheno-climatic features for epidemiological applications

Geographical Information Systems (GIS) and Remote Sensing (RS) technologies are being used increasingly to study the spatial and temporal patterns of diseases. They can be used to complement conventional ecological monitoring and modelling techniques, and provide a means to portray complex relationships in the ecology of diseases with strong environmental determinants. In particular, satellite technology has been extraordinarily improved during recent years, providing new parameters useful to understand the epidemiology of parasites, such as vegetation indices, land surface temperatures, soil moisture and rainfall indices. In the present review, Normalized Difference Vegetation Index (NDVI) is primarily considered, since it is the index characterizing vegetation that is most used in epidemiological studies. Multi-temporal study of RS data allows collection of bio-climatic information about risk area distribution, along with predictive studies and anticipatory models of diseases, at different geographic scales ranging from global to local. The main physical and technological basis of a mathematical model, effective at different scales, for identification of landscape pheno-climatic features is described in the current paper.     

The effect of landscape heterogeneity on host–parasite dynamics

Environmental heterogeneity has been shown to have a profound effect on population dynamics and biological invasions, yet the effect of its spatial structure on the dynamics of disease invasion in a spatial host–parasite system has received little attention. Here we explore the effect of environment heterogeneity using the pair approximation and the stochastic spatially explicit simulation in which the lost patches are clustered in a fragmented landscape. The intensity of fragmentation is defined by the amount and spatial autocorrelation of the lost habitat. More fragmented landscape (high amount of habitat loss, low clustering of lost patches) was shown to be detrimental to the parasitic disease invasion and transmission, which implies that the potential of using artificial disturbances as a disease-control agency in biological conservation and management. Two components of the spatial heterogeneity (the amount and spatial autocorrelation of the lost habitat) formed a trade-off in determining the host–parasite dynamics. An extremely high degree of habitat loss was, counter-intuitively, harmful to the host. These results enrich our understanding of eco-epidemiological, host–parasite systems, and suggest the possibility of using the spatial arrangement of habitat patches as a conservation tool for guarding focal species against parasitic infection and transmission.     

Estimation of the transmission dynamics of Theileria equi and Babesia caballi in horses

For the evaluation of the epidemiology of Theileria equi and Babesia caballi in a herd of 510 horses in SW Mongolia, several mathematical models of the transmission dynamics were constructed. Because the field data contain information on the presence of the parasite (determined by PCR) and the presence of antibodies (determined by IFAT), the models cater for maternal protection with antibodies, susceptible animals, infected animals and animals which have eliminated the parasite and also allow for age-dependent infection in susceptible animals. Maximum likelihood estimation procedures were used to estimate the model parameters and a Monte Carlo approach was applied to select the best fitting model. Overall, the results are in line with previous experimental work, and add evidence that the epidemiology of T. equi differs from that of Babesia spp. The presented modelling approach provides a useful tool for the investigation of some vector-borne diseases and the applied model selection procedure avoids asymptotical assumptions that may not be adequate for the analysis of epidemiological field data.     

Consequences of landscape fragmentation on Lyme disease risk: a cellular automata approach

The abundance of infected Ixodid ticks is an important component of human risk of Lyme disease, and various empirical studies have shown that this is associated, at least in part, to landscape fragmentation. In this study, we aimed at exploring how varying woodland fragmentation patterns affect the risk of Lyme disease, through infected tick abundance. A cellular automata model was developed, incorporating a heterogeneous landscape with three interactive components: an age-structured tick population, a classical disease transmission function, and hosts. A set of simplifying assumptions were adopted with respect to the study objective and field data limitations. In the model, the landscape influences both tick survival and host movement. The validation of the model was performed with an empirical study. Scenarios of various landscape configurations (focusing on woodland fragmentation) were simulated and compared. Lyme disease risk indices (density and infection prevalence of nymphs) differed considerably between scenarios: (i) the risk could be higher in highly fragmented woodlands, which is supported by a number of recently published empirical studies, and (ii) grassland could reduce the risk in adjacent woodland, which suggests landscape fragmentation studies of zoonotic diseases should not focus on the patch-level woodland patterns only, but also on landscape-level adjacent land cover patterns. Further analysis of the simulation results indicated strong correlations between Lyme disease risk indices and the density, shape and aggregation level of woodland patches. These findings highlight the strong effect of the spatial patterns of local host population and movement on the spatial dynamics of Lyme disease risks, which can be shaped by woodland fragmentation. In conclusion, using a cellular automata approach is beneficial for modelling complex zoonotic transmission systems as it can be combined with either real world landscapes for exploring direct spatial effects or artificial representations for outlining possible empirical investigations.     

Identifying Malaria Transmission Foci for Elimination Using Human Mobility Data

Humans move frequently and tend to carry parasites among areas with endemic malaria and into areas where local transmission is unsustainable. Human-mediated parasite mobility can thus sustain parasite populations in areas where they would otherwise be absent. Data describing human mobility and malaria epidemiology can help classify landscapes into parasite demographic sources and sinks, ecological concepts that have parallels in malaria control discussions of transmission foci. By linking transmission to parasite flow, it is possible to stratify landscapes for malaria control and elimination, as sources are disproportionately important to the regional persistence of malaria parasites. Here, we identify putative malaria sources and sinks for pre-elimination Namibia using malaria parasite rate (PR) maps and call data records from mobile phones, using a steady-state analysis of a malaria transmission model to infer where infections most likely occurred. We also examined how the landscape of transmission and burden changed from the pre-elimination setting by comparing the location and extent of predicted pre-elimination transmission foci with modeled incidence for 2009. This comparison suggests that while transmission was spatially focal pre-elimination, the spatial distribution of cases changed as burden declined. The changing spatial distribution of burden could be due to importation, with cases focused around importation hotspots, or due to heterogeneous application of elimination effort. While this framework is an important step towards understanding progressive changes in malaria distribution and the role of subnational transmission dynamics in a policy-relevant way, future work should account for international parasite movement, utilize real time surveillance data, and relax the steady state assumption required by the presented model.     

On the dynamics of dengue epidemics from large-scale information

A model for the spatial and temporal dynamics of dengue fever is proposed in this article. The vector population dynamics is derived from a diffusion equation that is based on environmental parameters at the scale of a remote-sensing image. Vectors and hosts populations are then classically divided into compartments corresponding to their respective disease status. The transmission processes between hosts and vectors are described by a set of differential equations. The link between the vector population diffusion model and the compartmental model enables one to describe both the spatial and temporal dynamics of the disease. Simulations in artificial and actual landscapes show the advantage of using remotely sensed and complementary meteorological data for modelling in a realistic way the geographic spread of a vector-borne disease such as dengue fever.     

Co-occurrence Patterns of the Dengue Vector Aedes aegypti and Aedes mediovitattus, a Dengue Competent Mosquito in Puerto Rico

Aedes aegypti is implicated in dengue transmission in tropical and subtropical urban areas around the world. Ae. aegypti populations are controlled through integrative vector management. However, the efficacy of vector control may be undermined by the presence of alternative, competent species. In Puerto Rico, a native mosquito, Ae. mediovittatus, is a competent dengue vector in laboratory settings and spatially overlaps with Ae. aegypti. It has been proposed that Ae. mediovittatus may act as a dengue reservoir during inter-epidemic periods, perpetuating endemic dengue transmission in rural Puerto Rico. Dengue transmission dynamics may therefore be influenced by the spatial overlap of Ae. mediovittatus, Ae. aegypti, dengue viruses, and humans. We take a landscape epidemiology approach to examine the association between landscape composition and configuration and the distribution of each of these Aedes species and their co-occurrence. We used remotely sensed imagery from a newly launched satellite to map landscape features at very high spatial resolution. We found that the distribution of Ae. aegypti is positively predicted by urban density and by the number of tree patches, Ae. mediovittatus is positively predicted by the number of tree patches, but negatively predicted by large contiguous urban areas, and both species are predicted by urban density and the number of tree patches. This analysis provides evidence that landscape composition and configuration is a surrogate for mosquito community composition, and suggests that mapping landscape structure can be used to inform vector control efforts as well as to inform urban planning.     

Towards an eco‐phylogenetic framework for infectious disease ecology

Identifying patterns and drivers of infectious disease dynamics across multiple scales is a fundamental challenge for modern science. There is growing awareness that it is necessary to incorporate multi‐host and/or multi‐parasite interactions to understand and predict current and future disease threats better, and new tools are needed to help address this task. Eco‐phylogenetics (phylogenetic community ecology) provides one avenue for exploring multi‐host multi‐parasite systems, yet the incorporation of eco‐phylogenetic concepts and methods into studies of host pathogen dynamics has lagged behind. Eco‐phylogenetics is a transformative approach that uses evolutionary history to infer present‐day dynamics. Here, we present an eco‐phylogenetic framework to reveal insights into parasite communities and infectious disease dynamics across spatial and temporal scales. We illustrate how eco‐phylogenetic methods can help untangle the mechanisms of host–parasite dynamics from individual (e.g. co‐infection) to landscape scales (e.g. parasite/host community structure). An improved ecological understanding of multi‐host and multi‐pathogen dynamics across scales will increase our ability to predict disease threats.     

Spatial Epidemiology and GIS in Marine Mammal Conservation Medicine and Disease Research

The use of spatial epidemiology and geographical information systems (GIS) facilitates the incorporation of spatial relationships into epidemiological investigations of marine mammal diseases and conservation medicine. Spatial epidemiology is the study of the spatial variation in disease risk or incidence and explicitly addresses spatial structures and functions that factor into disease. The GIS consists of input, management, analysis, and presentation of spatial disease data and can act as an integrative tool so that a range of varied data sources can be combined to describe different environmental aspects of wild animals and their diseases. The use of modern spatial analyses and GIS is becoming well developed in the field of marine mammal ecology and biology, but has just recently started to gain more use in disease research. The use of GIS methodology and spatial analysis in nondisease marine mammal studies is briefly discussed, while examples of the specific uses of these tools in mapping, surveillance and monitoring, disease cluster detection, identification of environmental predictors of disease in wildlife populations, risk assessment, and modeling of diseases, is presented. Marine mammal disease investigations present challenges, such as less consistent access to animals for sampling, fewer baseline data on diseases in wild populations, and less robust epidemiologic study designs, but several recommendations for future research are suggested. Since location is an integral part of investigating disease, spatial epidemiology and GIS should be incorporated as a data management and analysis tool in the study of marine mammal diseases and conservation medicine.     

Landscape heterogeneity shapes host‐parasite interactions and results in apparent plant–virus codivergence

Knowledge on how landscape heterogeneity shapes host–parasite interactions is central to understand the emergence, dynamics and evolution of infectious diseases. However, this is an underexplored subject, particularly for plant–virus systems. Here, we analyse how landscape heterogeneity influences the prevalence, spatial genetic structure, and temporal dynamics of Pepper golden mosaic and Pepper huasteco yellow vein begomoviruses infecting populations of the wild pepper Capsicum annuum glabriusculum (chiltepin) in Mexico. Environmental heterogeneity occurred at different nested spatial scales (host populations within biogeographical provinces), with levels of human management varying among host population within a province. Results indicate that landscape heterogeneity affects the epidemiology and genetic structure of chiltepin‐infecting begomoviruses in a scale‐specific manner, probably related to conditions favouring the viruses' whitefly vector and its dispersion. Increased levels of human management of the host populations were associated with higher virus prevalence and erased the spatial genetic structure of the virus populations. Also, environmental heterogeneity similarly shaped the spatial genetic structures of host and viruses. This resulted in the congruence between host and virus phylogenies, which does not seem to be due to host‐virus co‐evolution. Thus, results provide evidence of the key role of landscape heterogeneity in determining plant–virus interactions.     

Human movement data for malaria control and elimination strategic planning

ABSTRACT: Recent increases in funding for malaria control have led to the reduction in transmission in many malaria endemic countries, prompting the national control programmes of 36 malaria endemic countries to set elimination targets. Accounting for human population movement (HPM) in planning for control, elimination and post-elimination surveillance is important, as evidenced by previous elimination attempts that were undermined by the reintroduction of malaria through HPM. Strategic control and elimination planning, therefore, requires quantitative information on HPM patterns and the translation of these into parasite dispersion. HPM patterns and the risk of malaria vary substantially across spatial and temporal scales, demographic and socioeconomic sub-groups, and motivation for travel, so multiple data sets are likely required for quantification of movement. While existing studies based on mobile phone call record data combined with malaria transmission maps have begun to address within-country HPM patterns, other aspects remain poorly quantified despite their importance in accurately gauging malaria movement patterns and building control and detection strategies, such as cross-border HPM, demographic and socioeconomic stratification of HPM patterns, forms of transport, personal malaria protection and other factors that modify malaria risk. A wealth of data exist to aid filling these gaps, which, when combined with spatial data on transport infrastructure, traffic and malaria transmission, can answer relevant questions to guide strategic planning. This review aims to (i) discuss relevant types of HPM across spatial and temporal scales, (ii) document where datasets exist to quantify HPM, (iii) highlight where data gaps remain and (iv) briefly put forward methods for integrating these datasets in a Geographic Information System (GIS) framework for analysing and modelling human population and Plasmodium falciparum malaria infection movements.     

Application of satellite data for investigation of dynamic processes in inland water bodies: Lake Shira (Khakasia, Siberia), a case study

This work describes avenues to use satellite information to analyse dynamic processes in aquatic ecosystems. Information for this analysis, was retrieved from AVHRR satellite sensor data. This information consisteds of time series of images of radiation temperature and turbidity. We expect this information will be of great value in analysing inland water bodies. Methods to process satellite information using original software and data processing techniques are proposed. For the investigation of the process and analyses of satellite information Shira Lake (Khakasia, Siberia) was used as a case study. To study the variability of the surface temperature and turbidity of the Lake in summer, the satellite and ground-truth data of the lake was applied. This study represents the first evaluation of the dynamic processes for Lake Shira based on satellite, ground-truth and modelling data. We developed algorithms and software to process satellite images to enable the reconstruction of time dependence of temperature and spectral reflectance of water bodies in the visible range, and to make computer-animated films visualising the spatial and temporal dynamics of the study parameters. The analyses of morphometric, meteorological and hydrological characteristics of Lake Shira have provided a realistic opportunity for processing the satellite information and to develop numerical models of variability of the hydrological regime of the lake. The results obtained demonstrate the feasibility of systematically retrieving the spatial information from the satellite data on the dynamics of the surface water temperature and of the suspended matter in the lake.     

Unmanned Aircraft Systems for Studying Spatial Abundance of Ungulates: Relevance to Spatial Epidemiology

Complex ecological and epidemiological systems require multidisciplinary and innovative research. Low cost unmanned aircraft systems (UAS) can provide information on the spatial pattern of hosts’ distribution and abundance, which is crucial as regards modelling the determinants of disease transmission and persistence on a fine spatial scale. In this context we have studied the spatial epidemiology of tuberculosis (TB) in the ungulate community of Doñana National Park (South-western Spain) by modelling species host (red deer, fallow deer and cattle) abundance at fine spatial scale. The use of UAS high-resolution images has allowed us to collect data to model the environmental determinants of host abundance, and in a further step to evaluate their relationships with the spatial risk of TB throughout the ungulate community. We discuss the ecological, epidemiological and logistic conditions under which UAS may contribute to study the wildlife/livestock sanitary interface, where the spatial aggregation of hosts becomes crucial. These findings are relevant for planning and implementing research, fundamentally when managing disease in multi-host systems, and focusing on risky areas. Therefore, managers should prioritize the implementation of control strategies to reduce disease of conservation, economic and social relevance.     

Mapping of Ebola virus spillover: Suitability and seasonal variability at the landscape scale

The unexpected Ebola virus outbreak in West Africa in 2014 involving the Zaire ebolavirus made clear that other regions outside Central Africa, its previously documented niche, were at risk of future epidemics. The complex transmission cycle and a lack of epidemiological data make mapping areas at risk of the disease challenging. We used a Geographic Information System-based multicriteria evaluation (GIS-MCE), a knowledge-based approach, to identify areas suitable for Ebola virus spillover to humans in regions of Guinea, Congo and Gabon where Ebola viruses already emerged. We identified environmental, climatic and anthropogenic risk factors and potential hosts from a literature review. Geographical data layers, representing risk factors, were combined to produce suitability maps of Ebola virus spillover at the landscape scale. Our maps show high spatial and temporal variability in the suitability for Ebola virus spillover at a fine regional scale. Reported spillover events fell in areas of intermediate to high suitability in our maps, and a sensitivity analysis showed that the maps produced were robust. There are still important gaps in our knowledge about what factors are associated with the risk of Ebola virus spillover. As more information becomes available, maps produced using the GIS-MCE approach can be easily updated to improve surveillance and the prevention of future outbreaks.     

Host–parasite determinants of parasite population structure: lessons from bats and mites on the importance of time

By definition, parasitic organisms are strongly dependant on their hosts, and for a great majority, this dependence includes host-to-host transmission. Constraints imposed by the host's spatial distribution and demography, in combination with those of the parasite, can lead to a metapopulation structure, where parasite populations are highly stochastic (i.e. prone to frequent extinctions and re-colonizations) and where drift becomes a major force shaping standing genetic variation. This, in turn, will directly affect the observed population structure, along with the ability of the parasite to adapt (or co-adapt) to its host. However, only a specific consideration of temporal dynamics can reveal the extent to which drift shapes parasite population structure; this is rarely taken into account in population genetic studies of parasitic organisms. The study by Bruyndonckx et al. in this issue of Molecular Ecology does just this and, in doing so, illustrates how a comparison of host–parasite co-structures in light of temporal dynamics can be particularly informative for understanding the ecological and evolutionary constraints imposed by the host. More specifically, the authors examine spatial and temporal population genetic data of a parasitic mite Spinturnix bechsteini that exclusively exploits the Bechstein's bat Myotis bechsteinii and consider these data in relation to host–parasite life histories and the population structure of the host.