Modeling Distributional Potential of Infectious Diseases

Samy, Abdallah M.; Yáñez‐Arenas, Carlos; Jaeschke, Anja; Cheng, Yanchao; Thomas, Stephanie Margarete

doi:10.1007/978-3-030-71377-5_18

Cited by 1 publication

(1 citation statement)

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“…We constructed ENM using the maximum entropy algorithm implemented in the kuenm R package [53]. While there are various model algorithms available for estimating niche models, PLOS NEGLECTED TROPICAL DISEASES such as generalized linear models (GLM), generalized additive models (GAM), and boosted regression trees (BRT), each designed for different types of distribution data and modeling purposes [54], we selected Maxent because of its efficiency in handling complex interactions between response and predictor variables [55]. A total of 1972 candidate models (i.e., these models refer to the different model configurations or combinations of predictor variables considered during modeling) were built with four distinct sets of environmental and socioeconomic variables.…”

Section: Ecological Niche Modelingmentioning

confidence: 99%

Mapping the potential distribution of the principal vector of Crimean-Congo haemorrhagic fever virus Hyalomma marginatum in the Old World

Celina,

Černý,

Samy

2023

PLoS Negl Trop Dis

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Crimean-Congo haemorrhagic fever (CCHF) is the most widely distributed tick-borne viral disease in humans and is caused by the Crimean-Congo haemorrhagic fever virus (CCHFV). The virus has a broader distribution, expanding from western China and South Asia to the Middle East, southeast Europe, and Africa. The historical known distribution of the CCHFV vector Hyalomma marginatum in Europe includes most of the Mediterranean and the Balkan countries, Ukraine, and southern Russia. Further expansion of its potential distribution may have occurred in and out of the Mediterranean region. This study updated the distributional map of the principal vector of CCHFV, H. marginatum, in the Old World using an ecological niche modeling approach based on occurrence records from the Global Biodiversity Information Facility (GBIF) and a set of covariates. The model predicted higher suitability of H. marginatum occurrences in diverse regions of Africa and Asia. Furthermore, the model estimated the environmental suitability of H. marginatum across Europe. On a continental scale, the model anticipated a widespread potential distribution encompassing the southern, western, central, and eastern parts of Europe, reaching as far north as the southern regions of Scandinavian countries. The distribution of H. marginatum also covered countries across Central Europe where the species is not autochthonous. All models were statistically robust and performed better than random expectations (p < 0.001). Based on the model results, climatic conditions could hamper the successful overwintering of H. marginatum and their survival as adults in many regions of the Old World. Regular updates of the models are still required to continually assess the areas at risk using up-to-date occurrence and climatic data in present-day and future conditions.

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Section: Ecological Niche Modelingmentioning

confidence: 99%