Hence, the functional habitat of the pathogen ultimately needs to integrate the functional habitats of the vector s and the host s. The general approach is visualized in Fig. Rather than focusing on vectors or hosts, we start by taking the pathogen as the focal organism, for which we identify the ecological resources related to key biological functions across its life cycle.
Title: APPLICATION OF REMOTE SENSING TO VECTOR ARTHROPOD SURVEILLANCE AND CONTROL
We recognize three main functions: i replication in a host, ii replication in a vector, and iii successful transmission between host and vector. In the next step, we apply the same approach to the vector and host species. The host and vector species each have their own specific ecological resources e. For both vector and host species, the functional habitat can then be determined by integrating knowledge on the mobility of the species with information on the distribution of resources, e.
This determines whether an area can be considered a suitable habitat from a functional perspective. Identifying specific habitat components directly from remotely sensed digital spatial data may not always be possible, especially when the habitat components are smaller than the pixel size of images e. Outlining the various functions and needs of the organisms involved, as is done in the RBHC framework, can help in interpreting results when indirect proxies have to be used, either in relation to the difficulty in collecting representative data on the vector, the host, the pathogen itself, or habitat features.
By focusing at the outset on species resources, the RBHC may give much better insight into the proximate factors driving disease transmission and generate more transferable models. The RBHC framework offers a method to distinguish the areas where these requirements are met. Pathogens may rely on several species of hosts and vectors such that the functional habitat of each must be considered.
Regional variation in vector species, as described in Section 4 , leads to a number of epidemiologically different situations. In Section 5 , we apply the RBHC approach to three different situations and explore possible control measures. Bluetongue virus BTV infects a wide range of ruminants but mainly affects sheep and cattle Maclachlan et al. The biological vectors are adult female Culicoides spp. Virus transmission takes place during the blood meals that female midges take to obtain proteins for egg production.
Suitable thermal conditions, and hence sufficiently warm microclimates, are essential for viral replication in the infected midge Carpenter et al. Domestic ruminants are a special case of hosts, since their requirements in terms of resources are provided by farmers. Culicoides spp. Recent analyses of blood feeding patterns indicate that most of these mammophilic species feed opportunistically on a wide range of hosts including domestic and wild ruminants, people and even birds Garros et al.
For oviposition and larval development, the availability of breeding sites is required. Although European Culicoides species have been found to breed in a wide array of potential sites reviewed by Harrup et al. Important breeding sites are organically enriched moist soil, mud at the soil—water interface, intact dung of large mammals and leaf litter. Furthermore, some species can tolerate shading of their breeding sites, such as C. Male Culicoides spp. Suitable thermal conditions, and hence sufficiently warm microclimates, are essential for the vectors to mature their eggs and go on to take further blood meals Mullens et al.
There is geographic variation in the vectors involved in transmission and their ecological resources; we illustrate the use of the RBHC for several geographic regions, and discuss its advantages. The main vector in southern Europe is an African—Asian species C.
In some cooler and wetter areas of southern Europe around the northern range margins of C. Using the case of bluetongue virus, we illustrate how different landscape configurations may affect transmission risk by differentially promoting interactions between ruminant hosts and midges with diverse breeding habitat requirements.
We consider three different epidemiological scenarios with differing midge community compositions, loosely corresponding to the situation in northern Europe with Palaearctic species only Fig. These represent somewhat simplified situations [we assume for example that alternative breeding sites for these species such as silage and dung pats inside animal housing Zimmer et al. We also assume that the key difference between C.
All potentially relevant species are considered as well as their overlapping functional habitats. This overcomes the problem of oversimplification of the system, e. It allows consideration of potential disease risk in advance of a pathogen arriving.
Modelling interactions between vector borne diseases and environment …
In Fig. For the same landscape, in Fig. Figure 2 C shows a situation where the moist soil breeder occurs alone e. Conte et al. Removing the flooded area around the water trough should reduce the transmission risk Fig. Figure 2 E displays a mixed population of moist soil breeders, one that prefers open conditions and a forest species that breeds in leaf litter or can tolerate shading of development sites. Domestic hosts are exposed to both and pathogens can again be transferred between domestic and wild ruminants.
Measures accounting for functional features and movement, such as eliminating flooding around the water trough and restricting grazing to areas away from the forest, could reduce potential transmission Fig. These examples on bluetongue, based on the species described in Table 1 , illustrate that the degree of spatial and temporal overlap between sets of pathogen resources is influenced by the spatial configuration of the resources of the vector here mostly the breeding sites and the host, but also by the dispersal capacity of the organisms, which for the midges is determined by their flight ranges and for the domestic hosts by the fence around the pasture.
These illustrations show how the RBHC can be useful in differentiating between situations in which transmission is possible or not, in similar landscapes, depending on whether movement capacity of the vector and host and the location of the different resources allow for the completion of the life cycle of the pathogen. An explicit focus on the mobility of the vectors and hosts was adopted, considering local movements between resources.
Animal movement also constitutes a key aspect of the spread of emerging diseases, since the permeability of a landscape for a pathogen will depend on whether vectors and hosts can disperse.
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Dispersal movements, and the way they are facilitated or not by landscape structure, may differ from routine types of movements to exploit resources locally e. Note that even if the precise resources that are actually found in the proximity of the forest are not yet known the RBHC helps in identifying relevant landscape characteristics that present clusters of resource sets, such as pasture next to forest. The importance of forest in determining midge vector species composition has been suggested previously Conte et al. Indeed, pasture and forest habitats represent key landscape elements where vectors feed on wild ruminants including roe deer Capreolus capreolus and red deer Cervus elaphus Linden et al.
Mixtures of pastures and forests can be easily identified on maps by looking at fragmentation indices, without detailed information on individual breeding sites or trees. Hence, even if we do not have full access to detailed autecological data, there is still scope to interpret vegetation maps from the functional viewpoint of the host, the vector and their interactions.
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Tell us if something is incorrect. This is especially relevant for pathogens maintained exclusively or commonly in vector-human transmission cycles e. The classical example from Europe of anophelism without malaria Fantini , Jetten and Takken is in parts of the United States mirrored by "aedism without dengue" and "aedism without yellow fever".
In the cases of dengue and yellow fever, the latter of which caused major epidemics in the United States in the latter part of the s Caldwell Crosby , this phenomenon undoubtedly in large part results from that improved housing quality has reduced overall human contact with the endophagic Ae. The importance of including information on pathogen infection rate, which commonly requires time-consuming and costly pathogen detection efforts and ideally should be based on multiyear data, to create models for abundance of infected vectors rather than just vector abundance need to be assessed on a disease-by-disease basis.
This is not critical for Lyme disease because B. Inclusion of pathogen infection rate to determine abundance of infected vectors will be most critical in situations where the pathogen is expected to occur only within a small percentage of local vector populations. This often is the case for WNV in mosquito vector populations, especially during years with low enzootic virus activity Bernard et al.
However, In some situations, the benefit of including pathogen data in the risk model may be counteracted by false negatives, resulting from vector populations of low abundance where it is not feasible to collect an adequate number of specimens to reliably determine a low infection rate versus pathogen absence. Another problem with vector modeling was discussed above as an advantage of risk modeling based on epidemiological data: abundance of host-seeking infected vectors may not necessarily result in increased levels of human pathogen exposure.
Finally, plague and tularemia present intriguing problems with regards to vector modeling because the causative agents can be transmitted by nonvector-borne routes such as handling of infected animals plague, tularemia , respiratory droplets plague , and ingestion of infected water tularemia Hopla , Jellison , Poland and Barnes , Barnes This will undoubtedly weaken the association between vector abundance and human pathogen exposure. Because spatial risk models based on epidemiological versus arthropod vector data have complementary strengths and weaknesses, it seems a logical solution to generate risk maps including both types of independently derived risk measures.
However, we have found this to be a rare occurrence. Indeed, we presented in Eisen et al. This approach demonstrated that isolated areas in southern California with elevated epidemiological risk typically were accompanied by areas of high risk of vector exposure. Either risk assessment could, when viewed alone, be met with skepticism but the co-occurrence of local areas with high epidemiological risk and high risk of vector exposure strongly suggests that elevated local risk of vector-borne disease do occur.
A similar approach also proved useful for WNV disease in Larimer County, where a spatial risk model for the mosquito vector Cx. The current lack of subcounty level spatial models combining epidemiological risk and vector exposure risk can be attributed to 1 the specialized GIS software and statistical skills and the cost of field vector sampling needed to generate the vector risk model and 2 that epidemiological data sets need to be acquired through collaboration with individual state health agencies.
Additional limitations include lack of reliable epidemiological data for diseases that are not nationally notifiable. To move the field of spatial risk modeling of vector-borne diseases forward we need to do the following:. Improve practices for determination and reporting of likely pathogen exposure sites for vector-borne disease cases. This is critical to facilitate spatial risk modeling based on epidemiological data. Change the practice of presenting epidemiological risk at the county scale to subcounty scales census tract, zip code.
Implement active epidemiological surveillance programs in strategic "sentinel counties" to gain a better understanding of spatial pathogen exposure patterns. Develop a nationwide systematic sampling approach that yields high-quality field vector data sets. This should focus especially on small but environmentally diverse areas likely to yield spatial vector abundance models with high potential for producing accurate results when "scaled up" to larger areas. Use simulation or analytical models to assess critical vector abundance thresholds required for enzootic pathogen maintenance.
Develop subcounty level spatial risk models maps combining epidemiological and arthropod vector data for vector borne diseases. We thank W. Winters for reviewing web-based information sources on spatial risk of WNV disease, and B. Bolling and C. Moore for assistance with development of the Cx.
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Google Scholar. Lars Eisen. Cite Citation. Permissions Icon Permissions. Abstract Understanding spatial patterns of human risk of exposure to arthropod vectors and their associated pathogens is critical for targeting limited prevention, surveillance, and control resources e. Open in new tab Download slide. Population dynamics of indirectly transmitted disease agents: the vector component, pp.
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