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Random field calibration with data on irregular grid for regional analyses: A case study on the bare carrying capacity of bats in Africa

Many applications in science and engineering involve data defined at specific geospatial locations, which are often modeled as random fields. The modeling of a proper correlation function is essential for the probabilistic calibration of the random fields, but traditional methods were developed with...

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Autores principales: Mursel, Sena, Conus, Daniel, Huang, Wei‐Min, Buceta, Javier, Bocchini, Paolo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10493194/
https://www.ncbi.nlm.nih.gov/pubmed/37701021
http://dx.doi.org/10.1002/ece3.10489
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author Mursel, Sena
Conus, Daniel
Huang, Wei‐Min
Buceta, Javier
Bocchini, Paolo
author_facet Mursel, Sena
Conus, Daniel
Huang, Wei‐Min
Buceta, Javier
Bocchini, Paolo
author_sort Mursel, Sena
collection PubMed
description Many applications in science and engineering involve data defined at specific geospatial locations, which are often modeled as random fields. The modeling of a proper correlation function is essential for the probabilistic calibration of the random fields, but traditional methods were developed with the assumption to have observations with evenly spaced data. Available methods dealing with irregularly spaced data generally require either interpolation or computationally expensive solutions. Instead, we propose a simple approach based on least square regression to estimate the autocorrelation function. We first tested our methodology on an artificially produced dataset to assess the performance of our method. The accuracy of the method and its robustness to the level of noise in the data indicate that it is suitable for use in realistic problems. In addition, the methodology was used on a major application, the modeling of animal species connected with zoonotic diseases. Understanding the population dynamics of reservoirs of zoonotic diseases, such as bats, is a crucial first step to predict and prevent potential spillover of deadly viruses like Ebola. Due to the limited data on bats across Africa, their density and migrations can only be studied with probabilistic numerical models based on samples of the ecological bare carrying capacity ([Formula: see text]). For this purpose, the bare carrying capacity was modeled as a random field and its statistics calibrated with the available data. The bare carrying capacity of bats was found to be denser in central Africa. This is because climatic and environmental conditions are more suitable for the survival of bats. The proposed methodology for random field calibration was shown to be a promising approach, which can cope with large gaps in data and with complex applications involving large geographical areas and high resolution.
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spelling pubmed-104931942023-09-12 Random field calibration with data on irregular grid for regional analyses: A case study on the bare carrying capacity of bats in Africa Mursel, Sena Conus, Daniel Huang, Wei‐Min Buceta, Javier Bocchini, Paolo Ecol Evol Research Articles Many applications in science and engineering involve data defined at specific geospatial locations, which are often modeled as random fields. The modeling of a proper correlation function is essential for the probabilistic calibration of the random fields, but traditional methods were developed with the assumption to have observations with evenly spaced data. Available methods dealing with irregularly spaced data generally require either interpolation or computationally expensive solutions. Instead, we propose a simple approach based on least square regression to estimate the autocorrelation function. We first tested our methodology on an artificially produced dataset to assess the performance of our method. The accuracy of the method and its robustness to the level of noise in the data indicate that it is suitable for use in realistic problems. In addition, the methodology was used on a major application, the modeling of animal species connected with zoonotic diseases. Understanding the population dynamics of reservoirs of zoonotic diseases, such as bats, is a crucial first step to predict and prevent potential spillover of deadly viruses like Ebola. Due to the limited data on bats across Africa, their density and migrations can only be studied with probabilistic numerical models based on samples of the ecological bare carrying capacity ([Formula: see text]). For this purpose, the bare carrying capacity was modeled as a random field and its statistics calibrated with the available data. The bare carrying capacity of bats was found to be denser in central Africa. This is because climatic and environmental conditions are more suitable for the survival of bats. The proposed methodology for random field calibration was shown to be a promising approach, which can cope with large gaps in data and with complex applications involving large geographical areas and high resolution. John Wiley and Sons Inc. 2023-09-10 /pmc/articles/PMC10493194/ /pubmed/37701021 http://dx.doi.org/10.1002/ece3.10489 Text en © 2023 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Articles
Mursel, Sena
Conus, Daniel
Huang, Wei‐Min
Buceta, Javier
Bocchini, Paolo
Random field calibration with data on irregular grid for regional analyses: A case study on the bare carrying capacity of bats in Africa
title Random field calibration with data on irregular grid for regional analyses: A case study on the bare carrying capacity of bats in Africa
title_full Random field calibration with data on irregular grid for regional analyses: A case study on the bare carrying capacity of bats in Africa
title_fullStr Random field calibration with data on irregular grid for regional analyses: A case study on the bare carrying capacity of bats in Africa
title_full_unstemmed Random field calibration with data on irregular grid for regional analyses: A case study on the bare carrying capacity of bats in Africa
title_short Random field calibration with data on irregular grid for regional analyses: A case study on the bare carrying capacity of bats in Africa
title_sort random field calibration with data on irregular grid for regional analyses: a case study on the bare carrying capacity of bats in africa
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10493194/
https://www.ncbi.nlm.nih.gov/pubmed/37701021
http://dx.doi.org/10.1002/ece3.10489
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