Johnson EE, Escobar LE, Zambrana-Torrelio C. An Ecological Framework for Modeling the Geography of Disease Transmission.
Trends Ecol Evol 2019;
34:655-668. [PMID:
31078330 PMCID:
PMC7114676 DOI:
10.1016/j.tree.2019.03.004]
[Citation(s) in RCA: 69] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2018] [Revised: 03/01/2019] [Accepted: 03/18/2019] [Indexed: 01/10/2023]
Abstract
Ecological niche modeling (ENM) is widely employed in ecology to predict species’ potential geographic distributions in relation to their environmental constraints and is rapidly becoming the gold-standard method for disease risk mapping. However, given the biological complexity of disease systems, the traditional ENM framework requires reevaluation. We provide an overview of the application of ENM to disease systems and propose a theoretical framework based on the biological properties of both hosts and parasites to produce reliable outputs resembling disease system distributions. Additionally, we discuss the differences between biological considerations when implementing ENM for distributional ecology and epidemiology. This new framework will help the field of disease ecology and applications of biogeography in the epidemiology of infectious diseases.
Infectious diseases greatly impact human health, biodiversity, and global economies, highlighting the need to understand and predict their distributions.
Ecological niche modeling (ENM) was not originally designed to explicitly reconstruct complex biological phenomena such as diseases or parasitism, requiring a reevaluation of the traditional framework.
We provide an integrative ENM framework for disease systems that considers suitable host availability, parasite ecologies, and different scales of modeling.
Disease transmission is driven by factors related to parasite availability and host exposure and susceptibility, which can be incorporated in ENM frameworks.
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