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Geospatial Health

eISSN 1970-7096 - pISSN 1827-1987

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Editor-in-Chief: Behzad Kiani

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1.5
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  • Impact of climate variability on the occurrence of cutaneous leishmaniasis in Khuzestan Province, southwestern Iran

    Farideh Azimi, Sadegh Shirian, Saranaz Jangjoo, Arman Ai, Tahereh Abbasi
    08-05-2017
    https://doi.org/10.4081/gh.2017.478
    3208
    PDF: 1524
    HTML: 1082
  • Spatial analysis of cutaneous leishmaniasis in an endemic area of Iran based on environmental factors

    Roghieh Ramezankhani, Nooshin Sajjadi, Roya Nezakati Esmaeilzadeh, Seyed Ali Jozi, Mohammad Reza Shirzadi
    08-11-2017
    https://doi.org/10.4081/gh.2017.578
    3062
    PDF: 1234
    HTML: 958
  • Ecological characterization of a cutaneous leishmaniasis outbreak through remotely sensed land cover changes

    Verónica Andreo, Juan Rosa, Karina Ramos, O. Daniel Salomón
    06-05-2022
    https://doi.org/10.4081/gh.2022.1033
    1964
    PDF: 1111
    Appendix: 215
    HTML: 42
  • Application of decision tree for prediction of cutaneous leishmaniasis incidence based on environmental and topographic factors in Isfahan Province, Iran

    Roghieh Ramezankhani, Nooshin Sajjadi, Roya Nezakati Esmaeilzadeh, Seyed Ali Jozi, Mohammad Reza Shirzadi
    08-05-2018
    https://doi.org/10.4081/gh.2018.664
    1896
    PDF: 1076
    HTML: 178
  • American cutaneous leishmaniasis cases in the metropolitan region of Manaus, Brazil: association with climate variables over time

    Rodrigo Augusto Ferreira de Souza, Rita Valéria Andreoli, Mary Toshie Kayano, Afrânio Lima Carvalho
    18-05-2015
    https://doi.org/10.4081/gh.2015.314
    3165
    PDF: 1609
    HTML: 843
  • Hyperendemicity, heterogeneity and spatial overlap of leprosy and cutaneous leishmaniasis in the southern Amazon region of Brazil

    Amanda Gabriela de Carvalho, João Gabriel Guimarães Luz, João Victor Leite Dias, Anuj Tiwari, Peter Steinmann, Eliane Ignotti
    29-12-2020
    https://doi.org/10.4081/gh.2020.892
    2248
    PDF: 1119
    HTML: 56
  • Risk map for cutaneous leishmaniasis in Ethiopia based on environmental factors as revealed by geographical information systems and statistics

    Ahmed Seid, Endalamaw Gadisa, Teshome Tsegaw, Adugna Abera, Aklilu Teshome, Abate Mulugeta, Merce Herrero, Daniel Argaw, Alvar Jorge, Asnakew Kebede, Abraham Aseffa
    377-387
    01-05-2014
    https://doi.org/10.4081/gh.2014.27
    7645
    PDF: 3000
  • Exploring recent spatial patterns of cutaneous leishmaniasis and their associations with climate in some countries of the Middle East using geographical information systems

    Salahuddin M. Jaber, Jwan H. Ibbini, Nawal S. Hijjawi, Nafn M. Amdar, Mohammed J. Huwail, Khalid Al-Aboud
    143-158
    01-11-2013
    https://doi.org/10.4081/gh.2013.62
    2409
    PDF: 1140
  • Intra-urban differences underlying leprosy spatial distribution in central Brazil: geospatial techniques as potential tools for surveillance

    Amanda G. Carvalho, Carolina Lorraine H. Dias, David J. Blok, Eliane Ignotti, João Gabriel G. Luz
    06-10-2023
    https://doi.org/10.4081/gh.2023.1227
    1694
    PDF: 783
    Supplementary Materials: 149
    HTML: 26
  • Place and health infrastructure in the Gulf Cooperation Council: A systematic scoping review of GIS applications in health

    Dari Alhuwail, Saad AlSharrah, Neil T Coffee, Faisal H Al-Refaei, Mark Daniel
    29-12-2020
    https://doi.org/10.4081/gh.2020.887
    3259
    PDF: 1211
    HTML: 81
  • Spatial epidemiology and meteorological risk factors of COVID-19 in Fars Province, Iran

    Marjan Zare, Ali Semati, Alireza Mirahmadizadeh, Abdulrasool Hemmati, Mostafa Ebrahimi
    08-06-2022
    https://doi.org/10.4081/gh.2022.1065
    1238
    PDF: 663
    HTML: 48
  • From Snow’s map of cholera transmission to dynamic catchment boundary delineation: current front lines in spatial analysis

    Behzad Kiani, Colleen Lau, Robert Bergquist
    26-10-2023
    https://doi.org/10.4081/gh.2023.1247
    2271
    PDF: 976
    HTML: 47
  • Spatiotemporal heterogeneity of SARS-CoV-2 diffusion at the city level using geographically weighted Poisson regression model: The case of Bologna, Italy

    Addisu Jember Zeleke, Rossella Miglio, Pierpaolo Palumbo, Paolo Tubertini, Lorenzo Chiari, Bologna MODELS4COVID Study Group of the University of Bologna and the National Institute for Nuclear Physics (INFN)
    01-12-2022
    https://doi.org/10.4081/gh.2022.1145
    1903
    PDF: 1036
    Appendix: 166
    HTML: 85
  • Urban gentrification and infectious diseases: an interdisciplinary narrative review

    Behzad Kiani, Gabriel Parker, Senobar Naderian, Colleen L. Lau, Benn Sartorius
    07-07-2025
    https://doi.org/10.4081/gh.2025.1388
    1578
    PDF: 674
    HTML: 116
  • Modelling the presence of disease under spatial misalignment using Bayesian latent Gaussian models

    Xavier Barber, David Conesa, Silvia Lladosa, Antonio Lòpez-Quílez
    18-04-2016
    https://doi.org/10.4081/gh.2016.415
    3875
    PDF: 2011
    HTML: 2265
  • Mastering geographically weighted regression: key considerations for building a robust model

    Behzad Kiani, Benn Sartorius, Colleen L. Lau, Robert Bergquist
    29-02-2024
    https://doi.org/10.4081/gh.2024.1271
    8765
    PDF: 1705
    HTML: 893
  • Predicting the spatial distribution of Biomphalaria straminea, a potential intermediate host for Schistosoma mansoni, in China

    Mohamed R. Habib, Yun-Hai Guo, Shan Lv, Wen-Biao Gu, Xiao-Heng Li, Xiao-Nong Zhou
    29-11-2016
    https://doi.org/10.4081/gh.2016.453
    4174
    PDF: 1636
    HTML: 1151
1 - 17 of 17 items

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vhealths

vHealth Section

Geospatial Health
opens the new vHealth section. Discover vHealth

Vol. 21 No. 2 (2026)
Bayesian hierarchical spatial models for disease mapping in the presence of missing covariates
Sami Ullah et al.
20 August 2026

Keywords

Geospatial Health

is an Open Access, peer-reviewed journal published by PAGEPress®, Pavia, Italy. All credits and honors to PKP for their OJS.

pISSN: 1827-1987
eISSN: 1970-7096

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