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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

1580

PDF: 732

HTML: 157
Assessing the relationship between environmental factors and malaria vector breeding sites in Swaziland using multi-scale remotely sensed data

Sabelo Nick Dlamini, Jonas Franke, Penelope Vounatsou

03-06-2015

https://doi.org/10.4081/gh.2015.302

3848

PDF: 1540

HTML: 1738
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

2130

PDF: 1145

HTML: 2011
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

3763

PDF: 1438

APPENDIX 1: 401

APPENDIX 2: 385

HTML: 1637
Prediction of dengue cases using the attention-based long short-term memory (LSTM) approach

Mokhalad A. Majeed, Helmi Z. M. Shafri, Aimrun Wayayok, Zed Zulkafli

25-05-2023

https://doi.org/10.4081/gh.2023.1176

1576

PDF: 756

Supplementary Materials: 91

HTML: 26
Habitat suitability map of Ixodes ricinus tick in France using multi-criteria analysis

Isabelle Lebert, Séverine Bord, Christine Saint-Andrieux, Eva Cassar, Patrick Gasqui, Frédéric Beugnet, Karine Chalvet-Monfray, Sophie O. Vanwambeke, Gwenaël Vourc’h, Magalie René-Martellet

17-05-2022

https://doi.org/10.4081/gh.2022.1058

5528

PDF: 2089

Appendix: 263

HTML: 204
Mapping environmental susceptibility to Saint Louis encephalitis virus, based on a decision tree model of remotelysensed data

Camilo H. Rotela, Lorena I. Spinsanti, Mario A. Lamfri, Marta S. Contigiani, Walter R. Almiròn, Carlos M. Scavuzzo

85-94

01-11-2011

https://doi.org/10.4081/gh.2011.160

1205

PDF: 702
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

2089

PDF: 1129

HTML: 126
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

2102

PDF: 872

HTML: 127
Migration statistics relevant for malaria transmission in Senegal derived from mobile phone data and used in an agent-based migration model

Adrian M. Tompkins, Nicky McCreesh

31-03-2016

https://doi.org/10.4081/gh.2016.408

3283

PDF: 1191

HTML: 994
Spatial accessibility to basic public health services in South Sudan

Peter M. Macharia, Paul O. Ouma, Ezekiel G. Gogo, Robert W. Snow, Abdisalan M. Noor

11-05-2017

https://doi.org/10.4081/gh.2017.510

7851

PDF: 2268

APPENDIX: 5379

HTML: 1109
Mapping urban and peri-urban breeding habitats of Aedes mosquitoes using a fuzzy analytical hierarchical process based on climatic and physical parameters

Muhammad Shahzad Sarfraz, Nagesh K. Tripathi, Fazlay S. Faruque, Usama Ijaz Bajwa, Asanobu Kitamoto, Marc Souris

S685-S697

01-12-2014

https://doi.org/10.4081/gh.2014.297

10321

PDF: 2421
Optimizing denominator data estimation through a multimodel approach

Ward Bryssinckx, Els Ducheyne, Veerle Versteirt, Herwig Leirs, Guy Hendrickx

573-582

01-05-2014

https://doi.org/10.4081/gh.2014.47

1050

PDF: 755
Using object-based image analysis to map commercial poultry operations from high resolution imagery to support animal health outbreaks and events

Susan Maroney, MaryJane McCool-Eye, Andrew Fox, Christopher Burdett

10-12-2020

https://doi.org/10.4081/gh.2020.919

2242

PDF: 696

HTML: 87
Is missing geographic positioning system data in accelerometry studies a problem, and is imputation the solution?

Kristin Meseck, Marta M. Jankowska, Jasper Schipperijn, Loki Natarajan, Suneeta Godbole, Jordan Carlson, Michelle Takemoto, Katie Crist, Jacqueline Kerr

31-05-2016

https://doi.org/10.4081/gh.2016.403

2098

PDF: 1253

HTML: 889
Spatial distribution and identifying biochemical factors affecting haemoglobin levels among women of reproductive age for each province in Indonesia: A geospatial analysis

Muhammad Nur Aidi, Fitrah Ernawati, Efriwati Efriwati, Nunung Nurjanah, Rika Rachmawati, Elisa Diana Julianti, Dian Sundari, Fifi Retiaty, Anwar Fitrianto, Khalilah Nurfadilah, Aya Yuriestia Arifin

30-11-2022

https://doi.org/10.4081/gh.2022.1118

1769

PDF: 792

HTML: 56
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

3968

PDF: 2063

HTML: 2143
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

7651

PDF: 1234

HTML: 846
Spatial epidemiology of cancer: a review of data sources, methods and risk factors

Rita Roquette, Marco Painho, Baltazar Nunes

08-05-2017

https://doi.org/10.4081/gh.2017.504

7445

PDF: 2972

HTML: 2633
Understanding COVID-19: comparison of spatio-temporal analysis methods used to study epidemic spread patterns in the United States

Chunhui Liu, Xiaodi Su, Zhaoxuan Dong, Xingyu Liu, Chunxia Qiu

25-05-2023

https://doi.org/10.4081/gh.2023.1200

928

PDF: 477

HTML: 42

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Search Results

Application of decision tree for prediction of cutaneous leishmaniasis incidence based on environmental and topographic factors in Isfahan Province, Iran

Assessing the relationship between environmental factors and malaria vector breeding sites in Swaziland using multi-scale remotely sensed data

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

Random forest variable selection in spatial malaria transmission modelling in Mpumalanga Province, South Africa

Prediction of dengue cases using the attention-based long short-term memory (LSTM) approach

Habitat suitability map of Ixodes ricinus tick in France using multi-criteria analysis

Mapping environmental susceptibility to Saint Louis encephalitis virus, based on a decision tree model of remotelysensed data

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

Validation of a spatial liver fluke model under field conditions in Ireland

Migration statistics relevant for malaria transmission in Senegal derived from mobile phone data and used in an agent-based migration model

Spatial accessibility to basic public health services in South Sudan

Mapping urban and peri-urban breeding habitats of Aedes mosquitoes using a fuzzy analytical hierarchical process based on climatic and physical parameters

Optimizing denominator data estimation through a multimodel approach

Using object-based image analysis to map commercial poultry operations from high resolution imagery to support animal health outbreaks and events

Is missing geographic positioning system data in accelerometry studies a problem, and is imputation the solution?

Spatial distribution and identifying biochemical factors affecting haemoglobin levels among women of reproductive age for each province in Indonesia: A geospatial analysis

Application of geo-spatial technology in schistosomiasis modelling in Africa: a review

Mastering geographically weighted regression: key considerations for building a robust model

Spatial epidemiology of cancer: a review of data sources, methods and risk factors

Understanding COVID-19: comparison of spatio-temporal analysis methods used to study epidemic spread patterns in the United States

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