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Geospatial Health

eISSN 1970-7096 - pISSN 1827-1987

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  • Satellite-based forest monitoring: spatial and temporal forecast of growing index and short-wave infrared band

    Caroline Bayr, Heinz Gallaun, Ulrike Kleb, Birgit Kornberger, Martin Steinegger, Martin Winter
    18-04-2016
    https://doi.org/10.4081/gh.2016.310
    2181
    PDF: 1187
    HTML: 2011
  • 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
    2728
    PDF: 1338
    HTML: 810
  • A geospatial analysis of cardiometabolic diseases and their risk factors considering environmental features in a midsized city in Argentina

    Micaela Natalia Campero, Carlos Matías Scavuzzo, Veronica Andreo, María Sol Mileo, Micaela Belén Franzois, María Georgina Oberto, Carla Gonzalez Rodriguez, María Daniela Defagó
    23-10-2023
    https://doi.org/10.4081/gh.2023.1212
    1164
    PDF: 604
    HTML: 13
  • Environmental and geographical factors influence malaria transmission in KwaZulu-Natal province, South Africa

    Osadolor Ebhuoma, Michael Gebreslasie, Oswaldo Villena, Ali Arab
    09-06-2025
    https://doi.org/10.4081/gh.2025.1370
    404
    PDF: 91
    HTML: 4
  • 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
    1617
    PDF: 768
    HTML: 158
  • Estimating malaria burden in Nigeria: a geostatistical modelling approach

    Nnadozie Onyiri
    04-11-2015
    https://doi.org/10.4081/gh.2015.306
    7018
    PDF: 2859
    HTML: 3246
  • Spatial autocorrelation and stratified heterogeneity in the evaluation of breast cancer risk inequity and socioeconomic factors analysis in China: Evidence from Nanchang, Jiangxi Province

    Yaqi Wang, Zhiwei Wan
    17-05-2022
    https://doi.org/10.4081/gh.2022.1078
    1433
    PDF: 750
    HTML: 115
  • Validation of a spatial liver fluke model under field conditions in Ireland

    Amalia Naranjo Lucena, María Pía Munita Corbalán, Ana María Martínez-Ibeas, Guy McGrath, Riona Sayers, Grace Mulcahy, Annetta Zintl
    07-05-2018
    https://doi.org/10.4081/gh.2018.641
    2147
    PDF: 905
    HTML: 127
  • Understanding the spatial non-stationarity in the relationships between malaria incidence and environmental risk factors using Geographically Weighted Random Forest: A case study in Rwanda

    Gilbert Nduwayezu, Pengxiang Zhao, Clarisse Kagoyire, Lina Eklund, Jean Pierre Bizimana, Petter Pilesjo, Ali Mansourian
    25-05-2023
    https://doi.org/10.4081/gh.2023.1184
    2208
    PDF: 1190
    HTML: 136
  • Immunity to poliovirus in Afghanistan: A household sampling method for serological assessment based on geographical information systems

    Amalia Mendes, Ari Whiteman, Benjamin Nygren, Brian Kaplan, Imtiaz Hussain, Sajid Soofi, Maureen Martinez, Noha H. Farag
    29-11-2022
    https://doi.org/10.4081/gh.2022.1107
    975
    PDF: 605
    HTML: 35
  • Heterogeneity in a communal cattle-farming system in a zone endemic for foot and mouth disease in South Africa

    Ockert Louis van Schalkwyk, Eva M. De Clercq, Claudia De Pus, Guy Hendrickx, Peter van den Bossche, Darryn L. Knobel
    31-05-2016
    https://doi.org/10.4081/gh.2016.338
    2389
    PDF: 1148
    HTML: 1429
  • Dengue risk-mapping in an Amazonian locality in Colombia based on regression and multi-criteria analysis

    Maria Camila Lesmes , Alvaro Ávila-Díaz, Erika Santamaría, Carlos Andrés Morales , Horacio Cadena, Patricia Fuya, Nicolas Frutos, Ximena Porcasi, Catalina Marceló-Díaz
    04-06-2025
    https://doi.org/10.4081/gh.2025.1292
    509
    PDF: 169
    HTML: 3
  • Spatial pattern analysis of the impact of community food environments on foetal macrosomia, preterm births and low birth weight

    Micaela Natalia Campero, Carlos Matías Scavuzzo, Carlos Marcelo Scavuzzo, María Dolores Román
    07-05-2024
    https://doi.org/10.4081/gh.2024.1249
    799
    PDF: 435
    Supplementary Materials: 76
    HTML: 56
  • Use of an ecologically relevant modelling approach to improve remote sensing-based schistosomiasis risk profiling

    Yvonne Walz, Martin Wegmann, Benjamin Leutner, Stefan Dech, Penelope Vounatsou, Eliézer K. N'Goran, Giovanna Raso, Jürg Utzinger
    30-11-2015
    https://doi.org/10.4081/gh.2015.398
    4128
    PDF: 1110
    HTML: 1196
  • Application of geo-spatial technology in schistosomiasis modelling in Africa: a review

    Tawanda Manyangadze, Moses John Chimbari, Michael Gebreslasie, Samson Mukaratirwa
    04-11-2015
    https://doi.org/10.4081/gh.2015.326
    4023
    PDF: 2111
    HTML: 2152
  • Correlation between normalized difference vegetation index and malaria in a subtropical rain forest undergoing rapid anthropogenic alteration

    Nicole M. Wayant, Diego Maldonado, Antonieta Rojas de Arias, Blanca Cousiño, Douglas G. Goodin
    179-190
    01-05-2010
    https://doi.org/10.4081/gh.2010.199
    3139
    PDF: 1181
  • 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
    1486
    PDF: 688
    Appendix: 102
    HTML: 30
  • Ecological niche model of Phlebotomus perniciosus, the main vector of canine leishmaniasis in north-eastern Italy

    Manuela Signorini, Rudi Cassini, Michele Drigo, Antonio Frangipane di Regalbono, Mario Pietrobelli, Fabrizio Montarsi, Anna-Sofie Stensgaard
    193-201
    01-11-2014
    https://doi.org/10.4081/gh.2014.16
    3391
    PDF: 1424
  • Seasonal relationship between normalized difference vegetation index and abundance of the Phlebotomus kala-azar vector in an endemic focus in Bihar, India

    Gouri S. Bhunia, Shreekant Kesari, Nandini Chatterjee, Rakesh Mandal, Vijay Kumar, Pradeep Das
    51-62
    01-11-2012
    https://doi.org/10.4081/gh.2012.104
    1850
    PDF: 1082
  • An early warning system for Crimean-Congo haemorrhagic fever seasonality in Turkey based on remote sensing technology

    Agustin Estrada-Peña, Zati Vatansever, Aysen Gargili, Turan Buzgan
    127-135
    01-11-2007
    https://doi.org/10.4081/gh.2007.261
    2435
    PDF: 867
  • Urban environmental clustering to assess the spatial dynamics of Aedes aegypti breeding sites

    Guillermo Albrieu-Llinás, Manuel O. Espinosa, Agustín Quaglia, Marcelo Abril, Carlos Marcelo Scavuzzo
    07-05-2018
    https://doi.org/10.4081/gh.2018.654
    2763
    PDF: 1148
    HTML: 186
  • 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
    3706
    PDF: 1272
    HTML: 1132
  • Geospatial analysis of invasion of the Asian tiger mosquito Aedes albopictus: competition with Aedes japonicus japonicus in its northern limit area in Japan

    Naoko Nihei, Osamu Komagata, Kan-ichiro Mochizuki, Mutsuo Kobayashi
    417-427
    01-05-2014
    https://doi.org/10.4081/gh.2014.30
    2498
    PDF: 1018
  • Impact of climate change and man-made irrigation systems on the transmission risk, long-term trend and seasonality of human and animal fascioliasis in Pakistan

    Kiran Afshan, Cesar A. Fortes-Lima, Patricio Artigas, M. Adela Valero, Mazhar Qayyum, Santiago Mas-Coma
    317-334
    01-05-2014
    https://doi.org/10.4081/gh.2014.22
    26284
    PDF: 2081
  • Cluster analysis of fasciolosis in dairy cow herds in Munster province of Ireland and detection of major climatic and environmental predictors of the exposure risk

    Nikolaos Selemetas, Paul Phelan, Padraig O'Kiely, Theo de Waal
    271-279
    19-03-2015
    https://doi.org/10.4081/gh.2015.349
    2540
    PDF: 1170
  • MALAREO: a user-driven project

    Michael T. Gebreslasie, Ides Bauwens
    04-11-2015
    https://doi.org/10.4081/gh.2015.329
    2625
    PDF: 1048
    HTML: 973
  • Estimating population and livestock density of mobile pastoralists and sedentary settlements in the south-eastern Lake Chad area

    Vreni Jean-Richard, Lisa Crump, Abbani Alhadj Abicho, Ali Abba Abakar, Abdraman Mahamat II, Mahamat Bechir, Sandra Eckert, Matthias Engesser, Esther Schelling, Jakob Zinsstag
    18-05-2015
    https://doi.org/10.4081/gh.2015.307
    3891
    PDF: 1464
    HTML: 1458
  • Spatial patterns of intestinal parasite infections among children and adolescents in some indigenous communities in Argentina

    Carlos Matías Scavuzzo, Micaela Natalia Campero, Rosana Elizabeth Maidana, María Georgina Oberto, María Victoria Periago, Ximena Porcasi
    28-05-2024
    https://doi.org/10.4081/gh.2024.1279
    2310
    PDF: 493
    HTML: 76
  • Influence of topography on the endemicity of Kala-azar: a study based on remote sensing and geographical information system

    Gouri S. Bhunia, Shreekant Kesari, Algarsamy Jeyaram, Vijay Kumar, Pradeep Das
    155-165
    01-05-2010
    https://doi.org/10.4081/gh.2010.197
    2105
    PDF: 805
  • The spatial distribution of Schistosoma mansoni infection in four regions of western Côte d'Ivoire

    Rufin K. Assaré, Ying-Si Lai, Ahoua Yapi, Yves-Nathan T. Tian-Bi, Mamadou Ouattara, Patrick K. Yao, Stefanie Knopp, Penelope Vounatsou, Jürg Utzinger, Eliézer K. N'Goran
    03-06-2015
    https://doi.org/10.4081/gh.2015.345
    3883
    PDF: 1608
    HTML: 1231
  • Random forest variable selection in spatial malaria transmission modelling in Mpumalanga Province, South Africa

    Thandi Kapwata, Michael T. Gebreslasie
    16-11-2016
    https://doi.org/10.4081/gh.2016.434
    3825
    PDF: 1504
    APPENDIX 1: 414
    APPENDIX 2: 401
    HTML: 1638
  • Bayesian spatial modelling and the significance of agricultural land use to scrub typhus infection in Taiwan

    Nicola A. Wardrop, Chi-Chien Kuo, Hsi-Chieh Wang, Archie C. A. Clements, Pei-Fen Lee, Peter M. Atkinson
    229-239
    01-11-2013
    https://doi.org/10.4081/gh.2013.69
    2529
    PDF: 928
  • The epidemiology and small-scale spatial heterogeneity of urinary schistosomiasis in Lusaka province, Zambia

    Christopher Simoonga, Lawrence N. Kazembe, Thomas K. Kristensen, Annette Olsen, Chris C. Appleton, Patricia Mubita, Likezo Mubila
    57-67
    01-11-2008
    https://doi.org/10.4081/gh.2008.232
    2733
    PDF: 971
  • Impacts of sample ratio and size on the performance of random forest model to predict the potential distribution of snail habitats

    Yuanhua Liu, Jun Zhang, Michael P. Ward, Wei Tu, Lili Yu, Jin Shi, Yi Hu, Fenghua Gao, Zhiguo Cao, Zhijie Zhang
    03-07-2023
    https://doi.org/10.4081/gh.2023.1151
    1744
    PDF: 667
    Supplementary Materials: 78
    HTML: 37
  • Spatial distribution and sociodemographic risk factors of malaria in Nigerian children less than 5 years old

    Chigozie Louisa J. Ugwu, Temesgen Zewotir
    29-12-2020
    https://doi.org/10.4081/gh.2020.819
    3254
    PDF: 1533
    HTML: 608
  • Earth observation in support of malaria control and epidemiology: MALAREO monitoring approaches

    Jonas Franke, Michael Gebreslasie, Ides Bauwens, Julie Deleu, Florian Siegert
    03-06-2015
    https://doi.org/10.4081/gh.2015.335
    4178
    PDF: 1446
    HTML: 1215
  • Remote sensing for predicting potential habitats of Oncomelania hupensis in Hongze, Baima and Gaoyou lakes in Jiangsu province, China

    Guo-Jing Yang, Penelope Vounatsou, Marcel Tanner, Xiao-Nong Zhou, Jürg Utzinger
    85-92
    01-11-2006
    https://doi.org/10.4081/gh.2006.283
    2279
    PDF: 961
  • Study of the variation of schistosomiasis risk in Lake Poyang in the People's Republic of China using multiple space-borne sensors for monitoring and modelling

    Kuo-Hsin Tseng, Song Liang, Motomu Ibaraki, Hyongki Lee, C. K. Shum
    353-364
    01-05-2014
    https://doi.org/10.4081/gh.2014.25
    1522
    PDF: 922
  • The spatial distribution of Anopheles gambiae sensu stricto and An. arabiensis (Diptera: Culicidae) in Mali

    N. Sogoba, P. Vounatsou, M.M. Bagayoko, S. Doumbia, G. Dolo, L. Gosoniu, S.F. Traore, Y.T. Toure, T. Smith
    213-222
    01-05-2007
    https://doi.org/10.4081/gh.2007.269
    2389
    PDF: 856
  • Baseline drivers of lymphatic filariasis in Burkina Faso

    Michelle C. Stanton, David H. Molyneux, Dominique Kyelem, Roland W. Bougma, Benjamin G. Koudou, Louise A. Kelly-Hope
    159-173
    01-11-2013
    https://doi.org/10.4081/gh.2013.63
    3137
    PDF: 1197
  • A geospatial risk assessment model for leprosy in Ethiopia based on environmental thermal-hydrological regime analysis

    Azeb Tadesse Argaw, E.J. Shannon, Abraham Assefa, Fekade Silassie Mikru, Berhane Kidane Mariam, John B. Malone
    105-113
    01-11-2006
    https://doi.org/10.4081/gh.2006.285
    2190
    PDF: 918
  • Developing operational algorithms using linear and non-linear squares estimation in Python® for the identification of Culex pipiens and Culex restuans in a mosquito abatement district (Cook County, Illinois, USA)

    Benjamin G. Jacob, Weidong Gu, Erik X. Caamano, Robert J. Novak
    157-176
    01-05-2009
    https://doi.org/10.4081/gh.2009.218
    1363
    PDF: 832
  • Spatial analyses of typhoid fever in Jiangsu province, People's Republic of China

    Yue-Jia Cheng, Fen-Yang Tang, Chang-Jun Bao, Ye-Fei Zhu, Qi Liang, Jian-Li Hu, Wen-Dong Liu, Ying Wu, Kathleen H. Reilly, Tong-Qian Shen, Yang Zhao, Zhi-Hang Peng, Rong-Bin Yu, Hua Wang, Hong-Bing Shen, Feng Chen
    279-288
    01-05-2013
    https://doi.org/10.4081/gh.2013.86
    2560
    PDF: 1023
  • Remote sensing and climate data as a key for understanding fasciolosis transmission in the Andes: review and update of an ongoing interdisciplinary project

    Márius V. Fuentes
    59-70
    01-11-2006
    https://doi.org/10.4081/gh.2006.281
    1582
    PDF: 939
  • Spatial risk profiling of Schistosoma japonicum in Eryuan county, Yunnan province, China

    Peter Steinmann, Xiao-Nong Zhou, Barbara Matthys, Yuan-Lin Li, Hong-Jun Li, Shao-Rong Chen, Zhong Yang, Weng Fan, Tie-Wu Jia, Lan-Hua Li, Penelope Vounatsou, Jürg Utzinger
    59-73
    01-11-2007
    https://doi.org/10.4081/gh.2007.255
    2370
    PDF: 770
  • Modeling the distribution of Culex tritaeniorhynchus to predict Japanese encephalitis distribution in the Republic of Korea

    Penny Masuoka, Terry A. Klein, Heung-Chul Kim, David M. Claborn, Nicole Achee, Richard Andre, Judith Chamberlin, Jennifer Small, Assaf Anyamba, Dong-Kyu Lee, Suk H. Yi, Michael Sardelis, Young-Ran Ju, John Grieco
    45-57
    01-11-2010
    https://doi.org/10.4081/gh.2010.186
    2912
    PDF: 1239
  • Operational satellite-based temporal modelling of Aedes population in Argentina

    Manuel Espinosa, Eliana Marina Alvarez Di Fino, Marcelo Abril, Mario Lanfri, Maria Victoria Periago, Carlos Marcelo Scavuzzo
    09-11-2018
    https://doi.org/10.4081/gh.2018.734
    1791
    PDF: 994
    HTML: 63
  • Malaria risk map for India based on climate, ecology and geographical modelling

    Soma Sarkar, Poonam Singh, Mercy Aparna L. Lingala, Preeti Verma, Ramesh C. Dhiman
    06-11-2019
    https://doi.org/10.4081/gh.2019.767
    6173
    PDF: 2052
    HTML: 11951
  • Towards improved, cost-effective surveillance of Ixodes ricinus ticks and associated pathogens using species distribution modelling

    Manuela Signorini, Anna-Sofie Stensgaard, Michele Drigo, Giulia Simonato, Federica Marcer, Fabrizio Montarsi, Marco Martini, Rudi Cassini
    13-05-2019
    https://doi.org/10.4081/gh.2019.745
    1844
    PDF: 1085
    HTML: 42
  • Japan Aerospace Exploration Agency's public-health monitoring and analysis platform: A satellite-derived environmental information system supporting epidemiological study

    Kei Oyoshi, Yosei Mizukami, Ryosuke Kakuda, Yusuke Kobayashi, Hiroki Kai, Takeo Tadono
    14-05-2019
    https://doi.org/10.4081/gh.2019.717
    1761
    PDF: 733
    HTML: 58
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Geospatial Health
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Vol. 20 No. 1 (2025)
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Risk discrepancies in COVID-19-related community environments based on spatiotemporal monitoring
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Geospatial Health

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