Nienke Hartemink

Mapping the basic reproduction number (R0) for vector-borne diseases: A case study on bluetongue virus

Geographical maps indicating the value of the basic reproduction number, R₀, can be used to identify areas of higher risk for an outbreak after an introduction. We develop a methodology to create R₀ maps for vector-borne diseases, using bluetongue virus as a case study. This method provides a tool for gauging the extent of environmental effects on disease emergence. The method involves integrating vector-abundance data with statistical approaches to predict abundance from satellite imagery and with the biologically mechanistic modelling that underlies R₀. We illustrate the method with three applications for bluetongue virus in the Netherlands: 1) a simple R₀ map for the situation in September 2006, 2) species-specific R₀ maps based on satellite-data derived predictions, and 3) monthly R₀ maps throughout the year. These applications ought to be considered as a proof-of-principle and illustrations of the methods described, rather than as ready-to-use risk maps. Altogether, this is a first step towards an integrative method to predict risk of establishment of diseases based on mathematical modelling combined with a geographic information system that may comprise climatic variables, landscape features, land use, and other relevant factors determining the risk of establishment for bluetongue as well as of other emerging vector-borne diseases.

Integrated Mapping of Establishment Risk for Emerging Vector-Borne Infections: A Case Study of Canine Leishmaniasis in Southwest France

Background Zoonotic visceral leishmaniasis is endemic in the Mediterranean Basin, where the dog is the main reservoir host. The diseases causative agent, Leishmania infantum, is transmitted by blood-feeding female sandflies. This paper reports an integrative study of canine leishmaniasis in a region of France spanning the southwest Massif Central and the northeast Pyrenees, where the vectors are the sandflies Phlebotomus ariasi and P. perniciosus. Methods Sandflies were sampled in 2005 using sticky traps placed uniformly over an area of approximately 100 by 150 km. High- and low-resolution satellite data for the area were combined to construct a model of the sandfly data, which was then used to predict sandfly abundance throughout the area on a pixel by pixel basis (resolution of c. 1 km). Using literature- and expert-derived estimates of other variables and parameters, a spatially explicit R 0 map for leishmaniasis was constructed within a Geographical Information System. R 0 is a measure of the risk of establishment of a disease in an area, and it also correlates with the amount of control needed to stop transmission. Conclusions To our knowledge, this is the first analysis that combines a vector abundance prediction model, based on remotely-sensed variables measured at different levels of spatial resolution, with a fully mechanistic process-based temperature-dependent R 0 model. The resulting maps should be considered as proofs-of-principle rather than as ready-to-use risk maps, since validation is currently not possible. The described approach, based on integrating several modeling methods, provides a useful new set of tools for the study of the risk of outbreaks of vector-borne diseases.

Elasticity analysis in epidemiology: an application to tick-borne infections.

The application of projection matrices in population biology to plant and animal populations has a parallel in infectious disease ecology when next-generation matrices (NGMs) are used to characterize growth in numbers of infected hosts (R(0)). The NGM is appropriate for multi-host pathogens, where each matrix element represents the number of cases of one type of host arising from a single infected individual of another type. For projection matrices, calculations of the sensitivity and elasticity of the population growth rate to changes in the matrix elements has generated insight into plant and animal populations. These same perturbation analyses can be used for infectious disease systems. To illustrate this in detail we parameterized an NGM for seven tick-borne zoonoses and compared them in terms of the contributions to R(0) from three different routes of transmission between ticks, and between ticks and vertebrate hosts. The definition of host type may be the species of the host or the route of infection, or, as was the case for the set of tick-borne pathogens, a combination of species and the life stage at infection. This freedom means that there is a broad range of disease systems and questions for which the methodology is appropriate.

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.

Estimating mosquito population size from mark-release-recapture data

ABSTRACT Accurate estimation of population size is key to understanding the ecology of disease vectors, as well as the epidemiology of the pathogens they carry and to plan effective control activities. Population size can be estimated through mark—release—recapture (MRR) experiments that are based on the assumption that the ratio of recaptured individuals to the total captures approximates the ratio of marked individuals released to the total population. However, methods to obtain population size estimates usually consider pooled data and are often based on the total number of marked and unmarked captures. We here present a logistic regression model, based on the principle of the well-known Fisher—Ford method, specific for MRR experiments where the information available is the number of marked mosquitoes released, the number of marked and unmarked mosquitoes caught in each trap and on each day, and the geographic coordinates of the traps. The model estimates population size, taking into consideration the distance between release points and traps, the time between release and recapture, and the loss of marked mosquitoes to death or dispersal. The performance and accuracy of the logistic regression model has been assessed using simulated data from known population sizes. We then applied the model to data from MRR experiments with Aedes albopictus Skuse performed on the campus of “Sapienza” University in Rome (Italy).

Towards a resource-based habitat approach for spatial modelling of vector-borne disease risks

Given the veterinary and public health impact of vector‐borne diseases, there is a clear need to assess the suitability of landscapes for the emergence and spread of these diseases. Current approaches for predicting disease risks neglect key features of the landscape as components of the functional habitat of vectors or hosts, and hence of the pathogen. Empirical–statistical methods do not explicitly incorporate biological mechanisms, whereas current mechanistic models are rarely spatially explicit; both methods ignore the way animals use the landscape (i.e. movement ecology). We argue that applying a functional concept for habitat, i.e. the resource‐based habitat concept (RBHC), can solve these issues. The RBHC offers a framework to identify systematically the different ecological resources that are necessary for the completion of the transmission cycle and to relate these resources to (combinations of) landscape features and other environmental factors. The potential of the RBHC as a framework for identifying suitable habitats for vector‐borne pathogens is explored and illustrated with the case of bluetongue virus, a midge‐transmitted virus affecting ruminants. The concept facilitates the study of functional habitats of the interacting species (vectors as well as hosts) and provides new insight into spatial and temporal variation in transmission opportunities and exposure that ultimately determine disease risks. It may help to identify knowledge gaps and control options arising from changes in the spatial configuration of key resources across the landscape. The RBHC framework may act as a bridge between existing mechanistic and statistical modelling approaches.

Modelling the potential spatial distribution of mosquito species using three different techniques

BackgroundModels for the spatial distribution of vector species are important tools in the assessment of the risk of establishment and subsequent spread of vector-borne diseases. The aims of this study are to define the environmental conditions suitable for several mosquito species through species distribution modelling techniques, and to compare the results produced with the different techniques.MethodsThree different modelling techniques, i.e., non-linear discriminant analysis, random forest and generalised linear model, were used to investigate the environmental suitability in the Netherlands for three indigenous mosquito species (Culiseta annulata, Anopheles claviger and Ochlerotatus punctor). Results obtained with the three statistical models were compared with regard to: (i) environmental suitability maps, (ii) environmental variables associated with occurrence, (iii) model evaluation.ResultsThe models indicated that precipitation, temperature and population density were associated with the occurrence of Cs. annulata and An. claviger, whereas land surface temperature and vegetation indices were associated with the presence of Oc. punctor. The maps produced with the three different modelling techniques showed consistent spatial patterns for each species, but differences in the ranges of the predictions. Non-linear discriminant analysis had lower predictions than other methods. The model with the best classification skills for all the species was the random forest model, with specificity values ranging from 0.89 to 0.91, and sensitivity values ranging from 0.64 to 0.95.ConclusionsWe mapped the environmental suitability for three mosquito species with three different modelling techniques. For each species, the maps showed consistent spatial patterns, but the level of predicted environmental suitability differed; NLDA gave lower predicted probabilities of presence than the other two methods. The variables selected as important in the models were in agreement with the existing knowledge about these species. All model predictions had a satisfactory to excellent accuracy; best accuracy was obtained with random forest. The insights obtained can be used to gain more knowledge on vector and non-vector mosquito species. The output of this type of distribution modelling methods can, for example, be used as input for epidemiological models of vector-borne diseases.

Acarological Risk of Borrelia burgdorferi Sensu Lato Infections Across Space and Time in The Netherlands

A longitudinal investigation on tick populations and their Borrelia infections in the Netherlands was undertaken between 2006 and 2011 with the aim to assess spatial and temporal patterns of the acarological risk in forested sites across the country and to assess variations in Borrelia genospecies diversity. Ticks were collected monthly in 11 sites and nymphs were examined for Borrelia infections. Tick populations expressed strong seasonal variations, with consistent and significant differences in mean tick densities between sites. Borrelia infections were present in all study sites, with a site-specific mean prevalence per month ranging from 7% to 26%. Prevalence was location-dependent and was not associated with tick densities. Mean Borrelia prevalence was lowest in January (4%), gradually increasing to reach a maximum (24%) in August. Borrelia afzelii represented 70% of all infections, with Borrelia burgdorferi sensu stricto, Borrelia garinii, and Borrelia valaisiana represented with 4%, 8%, and 10%, respectively. The density of infected nymphs and the proportional distribution of the four Borrelia genospecies, were significantly different between sites. The results show a consistent and significant spatial and temporal difference in acarological risk across the Netherlands.

R0 for vector-borne diseases: impact of the assumption for the duration of the extrinsic incubation period

Mathematical modeling and notably the basic reproduction number R0 have become popular tools for the description of vector-borne disease dynamics. We compare two widely used methods to calculate the probability of a vector to survive the extrinsic incubation period. The two methods are based on different assumptions for the duration of the extrinsic incubation period; one method assumes a fixed period and the other method assumes a fixed daily rate of becoming infectious. We conclude that the outcomes differ substantially between the methods when the average life span of the vector is short compared to the extrinsic incubation period.

Tick – Borrelia interactions: burden or benefit?

A key factor in the success of parasites is the ability to move between hosts. Some parasites make use of an intermediate arthropod host to move between their primary hosts. Several examples exist where such parasites manipulate their intermediate host to enhance their transmission, but examples for ticks are scarce. In this chapter, we describe how Borrelia burgdorferi sensu lato, the causative agent of Lyme borreliosis, is associated with changes in the behaviour, physiology and survival of Ixodes ticks. Such changes can lead to more effective host finding for the tick and better colonisation of new hosts by Borrelia. We discuss how these changes may lead to an increased transmission (risk) of Borrelia. A next-generation matrix approach is applied to model potential effects of increased tick survival on the basic reproduction number R 0 of Borrelia. Using this approach, we show that Borrelia-associated increased survival of ticks can have a profound effect on the circulation of spirochaetes, and hence on Lyme borreliosis risk. Future studies would ideally resolve the mechanisms behind the described changes, and establish experimentally whether Borrelia can enhance its circulation between hosts.