All manuscripts presented in this thesis ultimately refer to the following question: “How does the environment affect the risks associated with certain MBVD at a certain point in space and time?” It is thus worth considering what the word “risk” means in this context in the first place. Intuitively, the answer may seem obvious, but in practice a useful definition heavily depends on the context. Within the over-arching topic of Natural Hazards, Marre (2013) lists a collection of 23 different definitions of the term, from a multitude of disciplines (covering disaster relief, natural and social science, engineering and the insurance industry, among others). The basic concept, that has also been adopted by the United Nations (2016), is that the risk posed by a certain threat is governed by three major aspects. First, there is the hazard, an existing phenomenon or substance with the potential to cause harm. Second, there is vulnerability, an indication for how susceptible an individual, population or entire society is towards the hazard. Third, exposure describes the points of contact between the hazard and those that are potentially affected by it. In an over-simplified example, a pothole on a road can illustrate hazard as a potential threat for cyclists, and that hazard may increase as the pothole deepens over time. Vulnerability towards this hazard varies among cyclists: while a healthy biker may easily cope with it, a visually impaired or elderly person may be more likely to fall and get injured. Finally, exposure is much higher for the group of commuters that make daily use of the street than it is for mountain bikers who prefer the forest over city roads. Based on this, risk can (theoretically) be quantified and expressed as 1) the probability that a hazard will have harmful consequences or 2) the expected number of losses (lives, livelihoods, property, etc.) caused by a hazard (Marre, 2013).
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While this underlying concept based on hazard, vulnerability and exposure certainly applies to epidemiology, definitions of risk still vary considerably within the field. For instance, the Dictionary of Epidemiology defines risk broadly as “the probability of an adverse or beneficial event in a defined popu-lation over a specified time interval” (Porta, 2014). The Handbook of Epidemiology, on the other hand, focuses on the individual by defining risk as
“the probability that an individual who is initially disease-free will develop a given disease over a specified time or age interval” (Ahrens & Pigeot, 2007).
Following this definition, the personal risk for a specific individual can indeed be calculated for “simple” diseases where risk is governed by a limited number of well-understood factors. An example for this is breast cancer, where the personal risk of an individual can indicate whether prophylactic medication should be considered (Ahrens & Pigeot, 2007).
In the context of MBVD, however, the term “risk” is predominantly used at the scale of populations (or “typical” or “average” members thereof) rather than individual persons. As MBVD are transmitted among the human population through mosquitoes, factors on individual level – such as genetic predispositions or dietary habits – play a minor (if any) role in the transmission cycle. Consequently, there is limited value in calculating risk for specific individuals. Furthermore, the transmission of MBVD depends on complex interactions between multiple factors (environmental, biological and societal), and the knowledge about these factors is often incomplete (compare Manuscripts 3 & 5). As a consequence, simplifications and generalizations have to be made that dictate a more population-focused view.
Moreover, different factors affect MBVD risk at different spatial and temporal scales. In terms of risk assessment, the importance of each factor varies depending on the status of the respective disease in a given area as well. For example, while long-term climatic conditions govern whether a species of mosquito vectors can sustain a local population in general, short- to medium-term weather conditions affect how large the population will be in a given year.
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As long as no vector species occurs locally, risk assessment for a human MBVD will focus on the likelihood of mosquitoes being introduced and establishing local populations, while even large numbers of infected travelers carrying the virus into the area would not affect the risk for the local population. The conditions of course change dramatically as soon as an established vector population exists. This demonstrates how different situations call for different modes of risk assessment, where certain divers of risk are investigated more or less thoroughly, depending on the current needs. For this, different kinds of tools and models have been developed that focus on different aspects of risk and can be useful for different purposes and scenarios.
Risk maps are an important tool in epidemiology, as they can be used to illustrate and analyze geographical patterns of disease-related risks. For the reasons mentioned in the previous sections, and despite the name, these maps typically do not show risk in the strict sense of any of the above definitions.
Instead, they often focus on one or more risk factors that can be used as an indicator or proxy for the actual risk. One rather simple example are the continental dengue risk maps by Jentes et al. (2016), where countries were classified into three classes of risk based on past incidence and expert opinion.
Maps of actual or potential distributions of vector species are commonly used as an indicator for disease transmission risk from global to regional scales. On a very local scale, You et al. (2013) used socio-environmental characteristics to create a map of cholera risk for individual neighborhoods in Kolkata, India.
The two most commonly applied methods for creating such risk maps for MBVD based on environmental factors originate from two very different scientific disciplines: Correlative Ecological Niche Models are a standard tool used in biogeography and ecology for assessing species’ distributions, while mechanistic disease transmission models are a core tool in epidemiology.
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