Combining hazard and exposure to model the spatial distribution of two zoonoses, based on human case records

Zoonoses, diseases that usually circulate among animals and that are sometimes transmitted to humans, are complex systems that involve the pathogen, the host, (the vector) and humans. Spatial distribution models are often based on human case records as they frequently are the most readily available data. These records may be seen as the tip of the iceberg, hiding undetected zoonotic cycle. A new framework is suggested to better address the issues raised by the use of human case records for modelling zoonoses. Hantavirus and tick-borne encephalitis are examined in diverse environments and at diverse scales to illustrate these concepts. The framework is based on the concept of risk assessment that is a combination of hazard (defined as pathogen circulation in the wild) and exposure (defined as people entering into infected landscape). Results suggest that the combination of hazard and exposure is needed to improve the predictive power of models and to investigate how factors are involved in the various parts of the disease transmission system. Different modelling tools, ranging from linear regression to machine learning and from the landscape to the European scale, are investigated and compared. The multilevel approach is highly advised and three scenarios of variable response are identified, which bear diverse consequences for modelling and modelling results interpretation.(SC - Sciences) -- UCL, 201

Shaping Zoonosis risk : landscape ecology vs. Landscape attractiveness. Tick-borne encephalitis in Sweden

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Shaping zoonoses risk using landscape ecology and landscape attractiveness for people, two case studies in Europe

Landscape, including land use and land cover composition and structure, are recognized as important drivers for vector-borne disease risk. Since vector-borne pathogens rely on at least one vector and one host species, the occurrence of a disease is linked to areas where habitats of these species overlap functionally. The fact that these areas do not necessarily coincide with specific vegetation types hampers the correct identification of areas at risk. In this paper, we explore the potential of a resource-based habitat concept (RBHC) in identifying ‘suitable habitats’ for vector-borne pathogens. The resource-based habitat concept has been much used in conservation ecology, but has not been used yet in disease ecology. This concept would offer a framework to systematically study the different resources that are necessary for the completion of the transmission cycle, and link these resources to landscape features and other environmental factors. We show that the RBHC can be adapted to the multi-species setting of a vector-borne pathogen and illustrate this by applying the concept to bluetongue, a midge-transmitted virus in ruminants. We discuss the usefulness of the concept for vector-borne diseases and we argue that the concept may enable us to study the functional habitats of all the relevant species (vectors as well as hosts), which will give new insight in the spatial and temporal variation in transmission opportunities and the resulting disease risk. Also, it may facilitate communication between modellers and entomologists, help in identifying knowledge gaps and data gaps. Our framework may help act as a bridge between existing bottom-up mechanistic modelling approaches, that do not include landscape factors at all, and top-down satellite image-based approaches that are based on statistical inferences only

Shaping zoonosis risk: landscape ecology vs. landscape attractiveness for people, the case of tick-borne encephalitis in Sweden

Background: In this paper, the hazard and exposure concepts from risk assessment are applied in an innovative approach to understand zoonotic disease risk. Hazard is here related to the landscape ecology determining where the hosts, vectors and pathogens are and, exposure is defined as the attractiveness and accessibility to hazardous areas. Tick-borne encephalitis in Sweden was used as a case study. Methods: Three boosted regression tree models are compared: a hazard model, an exposure model and a global model which combines the two approaches. Results: The global model offers the best predictive power and the most accurate modelling. The highest probabilities were found in easy-to-reach places with high landscape diversity, holiday houses, waterbodies and, well-connected forests of oak, birch or pine, with open-area in their ecotones, a complex shape, numerous clear-cuts and, a variation in tree height. Conclusion: While conditions for access and use of hazardous areas are quite specific to Scandinavia, this study offers promising perspectives to improve our understanding of the distribution of zoonotic and vector-borne diseases in diverse contexts

Modelling zoonotic diseases in humans: comparison of methods for hantavirus in Sweden

Because their distribution usually depends on the presence of more than one species, modelling zoonotic diseases in humans differs from modelling individual species distribution even though the data are similar in nature. Three approaches can be used to model spatial distributions recorded by points: based on presence/absence, presence/available or presence data. Here, we compared one or two of several existing methods for each of these approaches.Human cases of hantavirus infection reported by place of infection between 1991 and 1998 in Sweden were used as a case study. Puumala virus (PUUV), the most common hantavirus in Europe, circulates among bank voles (Myodes glareolus). In northern Sweden, it causes nephropathia epidemica (NE) in humans, a mild form of hemorrhagic fever with renal syndrome.Logistic binomial regression and boosted regression trees were used to model presence and absence data. Presence and available sites (where the disease may occur) were modelled using cross-validated logistic regression. Finally, the ecological niche model MaxEnt, based on presence-only data, was used.In our study, logistic regression had the best predictive power, followed by boosted regression trees, MaxEnt and cross-validated logistic regression. It is also the most statistically reliable but requires absence data. The cross-validated method partly avoids the issue of absence data but requires fastidious calculations. MaxEnt accounts for non-linear responses but the estimators can be complex. The advantages and disadvantages of each method are reviewed. © 2012 Zeimes et al.; licensee BioMed Central Ltd

Modelling zoonotic diseases in humans: comparison of methods for hantavirus in Sweden

Abstract Because their distribution usually depends on the presence of more than one species, modelling zoonotic diseases in humans differs from modelling individual species distribution even though the data are similar in nature. Three approaches can be used to model spatial distributions recorded by points: based on presence/absence, presence/available or presence data. Here, we compared one or two of several existing methods for each of these approaches. Human cases of hantavirus infection reported by place of infection between 1991 and 1998 in Sweden were used as a case study. Puumala virus (PUUV), the most common hantavirus in Europe, circulates among bank voles (Myodes glareolus). In northern Sweden, it causes nephropathia epidemica (NE) in humans, a mild form of hemorrhagic fever with renal syndrome. Logistic binomial regression and boosted regression trees were used to model presence and absence data. Presence and available sites (where the disease may occur) were modelled using cross-validated logistic regression. Finally, the ecological niche model MaxEnt, based on presence-only data, was used. In our study, logistic regression had the best predictive power, followed by boosted regression trees, MaxEnt and cross-validated logistic regression. It is also the most statistically reliable but requires absence data. The cross-validated method partly avoids the issue of absence data but requires fastidious calculations. MaxEnt accounts for non-linear responses but the estimators can be complex. The advantages and disadvantages of each method are reviewed.</p

Spatial dynamics of a zoonotic orthohantavirus disease through heterogenous data on rodents, rodent infections, and human disease

Zoonotic diseases are challenging to study from the ecological point of view as, broadly speaking, datasets tend to be either detailed on a small spatial extent, or coarse on a large spatial extent. Also, there are many ways to assess zoonotic disease transmission systems, from pathogens to hosts to humans. We explore the complementarity of datasets considering the pathogen in its host, the host and human cases in the context of Puumala orthohantavirus infection in Germany. We selected relevant environmental predictors using a conceptual framework based on resource-based habitats. This framework assesses the functions, and associated environmental resources of the pathogen and associated host. A resource-based habitat framework supports variable selection and result interpretation. Multiplying ‘keyholes’ to view a zoonotic disease transmission system is valuable, but requires a strong conceptual framework to select and interpret environmental explanatory variables. This study highlights the usefulness of a structured, ecology-based approach to study drivers of zoonotic diseases at the level of virus, host, and human - not only for PUUV but also for other zoonotic pathogens. Our results show that human disease cases are best explained by a combination of variables related to zoonotic pathogen circulation and human exposure