Moran’s I and Geary’s C: investigation of the effects of spatial weight matrices for assessing the distribution of infectious diseases

Sarah Isnan; Ahmad Fikri bin Abdullah; Abdul Rashid Shariff; Iskandar Ishak; Sharifah Norkhadijah Syed Ismail; Maheshwara Rao Appanan; Jihong Zhang; Guohua Yin; Qiuhua Zhang; Juan Fang; Duo Jiang; Chao Yang; Na Sun; Alireza Mohammadi; Bardia Mashhoodi; Ali Shamsoddini; Elahe Pishgar; Robert Bergquist; Wentao Yang; Fengjie Wang; Yihan You; Zhixiong Fang; Xing Wang; Xiaoming Mei; Sang Min Lee; Dong Woo Huh; Young Gyu Kwon; Peter Nezval; Takeshi Shirabe; Jonas Schoo; Frank Schüssler; Mai Liu; Yin Zhang; Enríque Ibarra-Zapata; Darío Gaytán-Hernández; Yolanda Terán-Figueroa; Verónica Gallegos-García; Carmen del Pilar Suárez-Rodríguez; Sergio Zarazúa Guzmán; Omar Parra Rodríguez; Worrayot Darasawang; Wongsa Laohasiriwong; Kittipong Sornlorm; Warangkana Sungsitthisawad; Roshan Kumar Mahato; Mintesnot Tenkir Teni; Travis Loux; Ness Sandoval; Anne Sebert Kuhlmann; Iuria Betco; Ana Isabel Ribeiro; David S. Vale; Luis Encalada-Abarca; Cláudia M. Viana; Jorge Rocha; Adel Al-Huraibi; Sherif Amer; Justine Blanford; Néstor DelaPaz-Ruíz; Ellen-Wien Augustijn; Mahdi Farnaghi; Sheheen A. Abdulkareem; Raul Zurita Milla

doi:10.4081/gh.2025.1277

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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

2667

PDF: 1256

HTML: 1046
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

2497

PDF: 909

HTML: 915
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

1501

PDF: 735

Appendix: 116

HTML: 30
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

1633

PDF: 805

HTML: 158
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, AfrÃ¢nio Lima Carvalho

18-05-2015

https://doi.org/10.4081/gh.2015.314

2766

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HTML: 810
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

1850

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HTML: 36
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

5421

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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

1995

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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

1139

PDF: 550

Supplementary Materials: 67

HTML: 12
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

2546

PDF: 977

HTML: 47
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

785

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HTML: 19
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

1428

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HTML: 22
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

1444

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Appendix: 92

HTML: 68
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

240

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HTML: 4
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

3385

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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

7833

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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

3744

PDF: 1355

HTML: 1132

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

Spatial analysis of cutaneous leishmaniasis in an endemic area of Iran based on environmental factors

Ecological characterization of a cutaneous leishmaniasis outbreak through remotely sensed land cover changes

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

American cutaneous leishmaniasis cases in the metropolitan region of Manaus, Brazil: association with climate variables over time

Hyperendemicity, heterogeneity and spatial overlap of leprosy and cutaneous leishmaniasis in the southern Amazon region of Brazil

Risk map for cutaneous leishmaniasis in Ethiopia based on environmental factors as revealed by geographical information systems and statistics

Exploring recent spatial patterns of cutaneous leishmaniasis and their associations with climate in some countries of the Middle East using geographical information systems

Intra-urban differences underlying leprosy spatial distribution in central Brazil: geospatial techniques as potential tools for surveillance

Place and health infrastructure in the Gulf Cooperation Council: A systematic scoping review of GIS applications in health

Spatial epidemiology and meteorological risk factors of COVID-19 in Fars Province, Iran

From Snow’s map of cholera transmission to dynamic catchment boundary delineation: current front lines in spatial analysis

Spatiotemporal heterogeneity of SARS-CoV-2 diffusion at the city level using geographically weighted Poisson regression model: The case of Bologna, Italy

Urban gentrification and infectious diseases: an interdisciplinary narrative review

Modelling the presence of disease under spatial misalignment using Bayesian latent Gaussian models

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

Predicting the spatial distribution of Biomphalaria straminea, a potential intermediate host for Schistosoma mansoni, in China

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