Application of Climate Envelope Model in the Control of <i>Fasciola gigantica </i>Prevalence in Nigeria

Isah Hamisu; Abdulmumin Garba Budah

doi:doi:10.11648/j.avs.20221002.14

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Application of Climate Envelope Model in the Control of Fasciola gigantica Prevalence in Nigeria

Isah Hamisu, Abdulmumin Garba Budah

Published in Animal and Veterinary Sciences (Volume 10, Issue 2)

Received: 23 September 2021 Accepted: 16 November 2021 Published: 20 April 2022

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Abstract

Mapping the potential areas for pathogen prevalence is a repetitive process and this research is an initial attempt to model the nation-wide prevalence of Fasciola gigantica in Nigeria. Data on Fasciola gigantica occurrence localities were obtained from published literature together with bioclimatic variables, the climate envelope model (MaxEnt) was utilized to analyze and predict its spatial range and to create suitable areas for Fasciola gigantica prevalence in Nigeria. The results show that the predicted areas of high risk included parts of northwestern Nigeria in Sokoto, Kebbi, Katsina, and some patches of Kano State. Likewise, Bauchi, Gombe, Borno, and large portions of Plateau State. Other areas of high risk as indicated by the model included Ekiti, Ogun, and Lagos State in the southwest. Similarly, infection risks covered the southeastern Nigeria in some parts of Rivers, Akwa Ibom and Cross rivers. The three most important variables with the highest training gain as revealed by the model are isothermality, minimum temperature of the coldest month, and precipitation seasonality. The performance of the MaxEnt model was better than a random prediction with training AUC scores of 0.891. This shows that MaxEnt is a suitable modelling technique for predicting the spatial range of fascioliasis prevalence in Nigeria based on its very good predictive accuracy.

Published in	Animal and Veterinary Sciences (Volume 10, Issue 2)
DOI	10.11648/j.avs.20221002.14
Page(s)	33-40
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2022. Published by Science Publishing Group

Keywords

Fasciola gigantica, Bioclimatic Variables, Climate Envelope, MaxEnt, AUC, Predictive Accuracy

References

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Isah Hamisu, Abdulmumin Garba Budah. (2022). Application of Climate Envelope Model in the Control of Fasciola gigantica Prevalence in Nigeria. Animal and Veterinary Sciences, 10(2), 33-40. https://doi.org/10.11648/j.avs.20221002.14

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Isah Hamisu; Abdulmumin Garba Budah. Application of Climate Envelope Model in the Control of Fasciola gigantica Prevalence in Nigeria. Anim. Vet. Sci. 2022, 10(2), 33-40. doi: 10.11648/j.avs.20221002.14

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AMA Style

Isah Hamisu, Abdulmumin Garba Budah. Application of Climate Envelope Model in the Control of Fasciola gigantica Prevalence in Nigeria. Anim Vet Sci. 2022;10(2):33-40. doi: 10.11648/j.avs.20221002.14

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@article{10.11648/j.avs.20221002.14,
  author = {Isah Hamisu and Abdulmumin Garba Budah},
  title = {Application of Climate Envelope Model in the Control of Fasciola gigantica Prevalence in Nigeria},
  journal = {Animal and Veterinary Sciences},
  volume = {10},
  number = {2},
  pages = {33-40},
  doi = {10.11648/j.avs.20221002.14},
  url = {https://doi.org/10.11648/j.avs.20221002.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.avs.20221002.14},
  abstract = {Mapping the potential areas for pathogen prevalence is a repetitive process and this research is an initial attempt to model the nation-wide prevalence of Fasciola gigantica in Nigeria. Data on Fasciola gigantica occurrence localities were obtained from published literature together with bioclimatic variables, the climate envelope model (MaxEnt) was utilized to analyze and predict its spatial range and to create suitable areas for Fasciola gigantica prevalence in Nigeria. The results show that the predicted areas of high risk included parts of northwestern Nigeria in Sokoto, Kebbi, Katsina, and some patches of Kano State. Likewise, Bauchi, Gombe, Borno, and large portions of Plateau State. Other areas of high risk as indicated by the model included Ekiti, Ogun, and Lagos State in the southwest. Similarly, infection risks covered the southeastern Nigeria in some parts of Rivers, Akwa Ibom and Cross rivers. The three most important variables with the highest training gain as revealed by the model are isothermality, minimum temperature of the coldest month, and precipitation seasonality. The performance of the MaxEnt model was better than a random prediction with training AUC scores of 0.891. This shows that MaxEnt is a suitable modelling technique for predicting the spatial range of fascioliasis prevalence in Nigeria based on its very good predictive accuracy.},
 year = {2022}
}

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TY  - JOUR
T1  - Application of Climate Envelope Model in the Control of Fasciola gigantica Prevalence in Nigeria
AU  - Isah Hamisu
AU  - Abdulmumin Garba Budah
Y1  - 2022/04/20
PY  - 2022
N1  - https://doi.org/10.11648/j.avs.20221002.14
DO  - 10.11648/j.avs.20221002.14
T2  - Animal and Veterinary Sciences
JF  - Animal and Veterinary Sciences
JO  - Animal and Veterinary Sciences
SP  - 33
EP  - 40
PB  - Science Publishing Group
SN  - 2328-5850
UR  - https://doi.org/10.11648/j.avs.20221002.14
AB  - Mapping the potential areas for pathogen prevalence is a repetitive process and this research is an initial attempt to model the nation-wide prevalence of Fasciola gigantica in Nigeria. Data on Fasciola gigantica occurrence localities were obtained from published literature together with bioclimatic variables, the climate envelope model (MaxEnt) was utilized to analyze and predict its spatial range and to create suitable areas for Fasciola gigantica prevalence in Nigeria. The results show that the predicted areas of high risk included parts of northwestern Nigeria in Sokoto, Kebbi, Katsina, and some patches of Kano State. Likewise, Bauchi, Gombe, Borno, and large portions of Plateau State. Other areas of high risk as indicated by the model included Ekiti, Ogun, and Lagos State in the southwest. Similarly, infection risks covered the southeastern Nigeria in some parts of Rivers, Akwa Ibom and Cross rivers. The three most important variables with the highest training gain as revealed by the model are isothermality, minimum temperature of the coldest month, and precipitation seasonality. The performance of the MaxEnt model was better than a random prediction with training AUC scores of 0.891. This shows that MaxEnt is a suitable modelling technique for predicting the spatial range of fascioliasis prevalence in Nigeria based on its very good predictive accuracy.
VL  - 10
IS  - 2
ER  -

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Author Information

Isah Hamisu

Department of Geography, Usmanu Danfodio University Sokoto, Sokoto, Nigeria
Abdulmumin Garba Budah

Department of Geography, Usmanu Danfodio University Sokoto, Sokoto, Nigeria

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Isah Hamisu, Abdulmumin Garba Budah. (2022). Application of Climate Envelope Model in the Control of Fasciola gigantica Prevalence in Nigeria. Animal and Veterinary Sciences, 10(2), 33-40. https://doi.org/10.11648/j.avs.20221002.14

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Isah Hamisu; Abdulmumin Garba Budah. Application of Climate Envelope Model in the Control of Fasciola gigantica Prevalence in Nigeria. Anim. Vet. Sci. 2022, 10(2), 33-40. doi: 10.11648/j.avs.20221002.14

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AMA Style

Isah Hamisu, Abdulmumin Garba Budah. Application of Climate Envelope Model in the Control of Fasciola gigantica Prevalence in Nigeria. Anim Vet Sci. 2022;10(2):33-40. doi: 10.11648/j.avs.20221002.14

Copy | Download

@article{10.11648/j.avs.20221002.14,
  author = {Isah Hamisu and Abdulmumin Garba Budah},
  title = {Application of Climate Envelope Model in the Control of Fasciola gigantica Prevalence in Nigeria},
  journal = {Animal and Veterinary Sciences},
  volume = {10},
  number = {2},
  pages = {33-40},
  doi = {10.11648/j.avs.20221002.14},
  url = {https://doi.org/10.11648/j.avs.20221002.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.avs.20221002.14},
  abstract = {Mapping the potential areas for pathogen prevalence is a repetitive process and this research is an initial attempt to model the nation-wide prevalence of Fasciola gigantica in Nigeria. Data on Fasciola gigantica occurrence localities were obtained from published literature together with bioclimatic variables, the climate envelope model (MaxEnt) was utilized to analyze and predict its spatial range and to create suitable areas for Fasciola gigantica prevalence in Nigeria. The results show that the predicted areas of high risk included parts of northwestern Nigeria in Sokoto, Kebbi, Katsina, and some patches of Kano State. Likewise, Bauchi, Gombe, Borno, and large portions of Plateau State. Other areas of high risk as indicated by the model included Ekiti, Ogun, and Lagos State in the southwest. Similarly, infection risks covered the southeastern Nigeria in some parts of Rivers, Akwa Ibom and Cross rivers. The three most important variables with the highest training gain as revealed by the model are isothermality, minimum temperature of the coldest month, and precipitation seasonality. The performance of the MaxEnt model was better than a random prediction with training AUC scores of 0.891. This shows that MaxEnt is a suitable modelling technique for predicting the spatial range of fascioliasis prevalence in Nigeria based on its very good predictive accuracy.},
 year = {2022}
}

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TY  - JOUR
T1  - Application of Climate Envelope Model in the Control of Fasciola gigantica Prevalence in Nigeria
AU  - Isah Hamisu
AU  - Abdulmumin Garba Budah
Y1  - 2022/04/20
PY  - 2022
N1  - https://doi.org/10.11648/j.avs.20221002.14
DO  - 10.11648/j.avs.20221002.14
T2  - Animal and Veterinary Sciences
JF  - Animal and Veterinary Sciences
JO  - Animal and Veterinary Sciences
SP  - 33
EP  - 40
PB  - Science Publishing Group
SN  - 2328-5850
UR  - https://doi.org/10.11648/j.avs.20221002.14
AB  - Mapping the potential areas for pathogen prevalence is a repetitive process and this research is an initial attempt to model the nation-wide prevalence of Fasciola gigantica in Nigeria. Data on Fasciola gigantica occurrence localities were obtained from published literature together with bioclimatic variables, the climate envelope model (MaxEnt) was utilized to analyze and predict its spatial range and to create suitable areas for Fasciola gigantica prevalence in Nigeria. The results show that the predicted areas of high risk included parts of northwestern Nigeria in Sokoto, Kebbi, Katsina, and some patches of Kano State. Likewise, Bauchi, Gombe, Borno, and large portions of Plateau State. Other areas of high risk as indicated by the model included Ekiti, Ogun, and Lagos State in the southwest. Similarly, infection risks covered the southeastern Nigeria in some parts of Rivers, Akwa Ibom and Cross rivers. The three most important variables with the highest training gain as revealed by the model are isothermality, minimum temperature of the coldest month, and precipitation seasonality. The performance of the MaxEnt model was better than a random prediction with training AUC scores of 0.891. This shows that MaxEnt is a suitable modelling technique for predicting the spatial range of fascioliasis prevalence in Nigeria based on its very good predictive accuracy.
VL  - 10
IS  - 2
ER  -

Copy | Download