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An Ecological Framework for Modeling the Geography Of TREE 2521 No. of Pages 14 Review An Ecological Framework for Modeling the Geography of Disease Transmission 1,3,4 2 1, Erica E. Johnson , Luis E. Escobar, and Carlos Zambrana-Torrelio * Ecological niche modeling (ENM) is widely employed in ecology to predict spe- Highlights cies’ potential geographic distributions in relation to their environmental con- Infectious diseases greatly impact human health, biodiversity, and global straints and is rapidly becoming the gold-standard method for disease risk economies, highlighting the need to mapping. However, given the biological complexity of disease systems, the understand and predict their distributions. traditional ENM framework requires reevaluation. We provide an overview of the application of ENM to disease systems and propose a theoretical framework Ecological niche modeling (ENM) was based on the biological properties of both hosts and parasites to produce reliable not originally designed to explicitly reconstruct complex biological phe- outputs resembling disease system distributions. Additionally, we discuss the nomena such as diseases or parasit- differences between biological considerations when implementing ENM for ism, requiring a reevaluation of the distributional ecology and epidemiology. This new framework will help the field traditional framework. of disease ecology and applications of biogeography in the epidemiology of We provide an integrative ENM frame- infectious diseases. work for disease systems that consid- ers suitable host availability, parasite ecologies, and different scales of modeling. Challenges and Opportunities to Map Disease Risk The recent rise of emerging infectious diseases (EIDs) (see Glossary) [1] has increased the Disease transmission is driven by fac- – burden of infectious diseases and negatively impacted the global economy [2 5]. Approxi- tors related to parasite availability and mately 60% of emerging human diseases are caused by pathogenic parasites of animal origin host exposure and susceptibility, which can be incorporated in ENM (zoonoses), particularly wildlife [6]. As human activities intensify, contact with wildlife and frameworks. exposure to novel parasites increase, potentially driving zoonotic disease emergence [1,7]. Given the threat that EIDs pose to human populations, understanding the underlying drivers of parasite geographic distribution and their spillover to humans is particularly relevant for epidemiologists, public-health practitioners, and policy makers [9]. Ecological niche modeling (ENM) has proven useful to forecast the distribution of a vast number of organisms [10–13] and is increasingly employed to predict parasite distributions locally and globally [14–17]. Despite great strides made in the implementation of ENM to forecast complex biological phenomena such as disease systems [18], traditional frameworks may 1 EcoHealth Alliance, 460 W. 34th render biologically unrealistic predictions and thus must be revised, as we show in this review. Street, New York, NY, USA We provide an overview of the current state of disease ENM and propose a framework based on 2 Department of Fish and Wildlife the biological properties of both parasites and hosts to produce reliable outputs resembling Conservation, Virginia Tech, Blacksburg, VA, USA disease systems distributions. Specifically, our theoretical framework: (i) addresses the selection 3 Current Address: Department of of an appropriate modeling approach and highlights the importance of including biologically Biology, City College of the City fi sound predictor variables; (ii) proposes the concept of a microscale parasitic niche de ned by University of New York, New York, NY 10031, USA host traits to identify relevant parasite–host associations; and (iii) integrates traditional parasite 4 Graduate Center of the City ENM with the proposed microscale niche to better understand geographic distributions and University of New York, New York, NY fi improve ne-scale predictions of disease transmission risk. 10016, USA ENM and Biotic Interactions *Correspondence: ENM estimates the distributions of species by linking their geographic occurrence with their [email protected] (C. environmental constraints, often utilizing correlative approaches (detailed explanations in [18]). Zambrana-Torrelio). Trends in Ecology & Evolution, Month Year, Vol. xx, No. yy https://doi.org/10.1016/j.tree.2019.03.004 1 © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). TREE 2521 No. of Pages 14 A plethora of algorithms is available to perform ENM (e.g., MaxEnt, Regression Trees) and Glossary methods for the development of accurate models have been described at length (see [19,20] Allee effect: correlation between for comparisons). Ecological interactions are hypothesized to affect species distributions only population size or density and the per capita growth rate (mean locally (i.e., the Eltonian noise hypothesis [10]) and are usually considered irrelevant in individual fitness) of a population. traditional coarse-scale ENM applications [10,21]. However, growing evidence suggests that Basic reproductive number (R0): biotic interactions may have a larger role in shaping broad-scale species distributions, espe- expected number of secondary cially under changing conditions (e.g., climate change) [10,22–24]. For example, continental- cases caused by a single infectious individual in a population. scale distributions of the North American warbler are better explained when coupling biotic Biotic, abiotic, and movement interactions (e.g., vegetation requirements) with abiotic factors (climate data) [23]. Biotic (BAM) framework: simplified interactors can be included in ENM by incorporating interacting species as predictor variables representation of a species’ geographic distribution determined (preprocessing), restricting the distribution of the focal species to regions where interactions by the intersection of suitable abiotic may occur (post-processing), and linking demographic population models to the final ENM conditions (A), biotic interactions (B), [10,25–27]. and the species’ dispersal capacity (M). Bridge host: host population Modeling Disease Systems capable of facilitating transmission The two most common disease distribution modeling methods are black-box and compo- between two otherwise nent-based approaches (Table 1; [18]). Black-box approaches model the overall geo- geographically disconnected graphic distribution of a final manifestation of host–parasite interactions (e.g., disease maintenance and target populations. Coinfection: simultaneous infection outbreaks), assuming that this outcome summarizes all biotic interactions involved in of a host by multiple parasite transmission [28–30]. This approach provides a pragmatic framework to generalize disease species. distributions and is useful when transmission dynamics are poorly understood and data are Disease system: set of species, limited, which is frequently the case for EIDs [31]. However, the black-box oversimplification including parasites, susceptible hosts, and vectors, involved in the may be perilous as it neglects ecological complexity (e.g., the identity of key host species for maintenance, transmission, and transmission). Alternatively, component-based approaches consider the individual ecolo- expression of a disease. gies of all species involved in disease transmission (e.g., parasites, hosts) [16,17,32]. This Ecological niche modeling (ENM): approach allows the identification of host species and prioritization of areas for disease computational method used to predict geographic species surveillance and control. Component-based approaches require in-depth knowledge of the distributions by combining factors disease system (e.g., the identity and ecologies of relevant species, transmission cycle), related to a species’ environmental requirements with those related to occurrence and dispersal. Ecophylogenetics: field in biology Table 1. Applications of ENM Frameworks to Describe and Predict Disease Distributions that focuses on the study of ecological patterns in biological Modeling approach Description Advantage Limitation communities (e.g., community Black box Considers disease Useful when information Ecologically relevant information assembly, species co-occurrence) system as an on the disease system is on disease transmission is limited; explained by the evolutionary epiphenomenon, using limited or for exploratory location of disease cases may not relationships among coexisting disease cases as analysis to identify represent site of infection, limiting species. occurrences to calibrate potential areas for transmission risk estimates Eltonian noise hypothesis: the model; for examples disease surveillance Sampling biases amplify ecological hypothesis stating that – see [28 30] inaccuracies in model predictions local-scale ecological interactions have negligible effects on a species’ Componentbased Considers key species Useful in designing Deep understanding of the natural geographic distribution. involved in disease evidence-based control history of the disease is necessary Emerging infectious disease system (e.g., hosts, strategies and detailed (e.g., all hosts are known) (EID): diseases that have increased vectors, parasite); for identification of potential Exclusion of key species when in incidence or geographic range, examples see [16,17,32] transmission areas to modeling a disease system may found in novel hosts
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