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  Spatial and temporal dynamic analysis of rabies: A review of current methodologies

  Shuaicheng Chen

  30-11-2022

  https://doi.org/10.4081/gh.2022.1139

  2222

  PDF: 1377

  HTML: 272
• 

  Climate and the distribution of vector-borne diseases: what's in store?

  Robert Bergquist

  08-05-2017

  https://doi.org/10.4081/gh.2017.549

  3133

  PDF: 1277

  HTML: 842
• 

  Comparison of complete and spatial sampling frames for estimation of the prevalence of hypertension and diabetes mellitus

  Vasna Joshua, Kamaraj Pattabi, Yuvaraj Jeyaraman, Prabhdeep Kaur, Tarun Bhatnagar, Suresh Arunachalam, Sabarinathan Ramasamy, Venkateshprabhu Janagaraj, Manoj V Murhekar

  30-11-2022

  https://doi.org/10.4081/gh.2022.1097

  2049

  PDF: 803

  HTML: 112
• 

  A comparison of temporal and location-based sampling strategies for global positioning system-triggered electronic diaries

  Tobias Törnros, Helen Dorn, Markus Reichert, Ulrich Ebner-Priemer, Hans-Joachim Salize, Heike Tost, Andreas Meyer-Lindenberg, Alexander Zipf

  21-11-2016

  https://doi.org/10.4081/gh.2016.473

  5813

  PDF: 1306

  HTML: 968
• 

  Combining process-based and correlative models improves predictions of climate change effects on Schistosoma mansoni transmission in eastern Africa

  Anna-Sofie Stensgaard, Mark Booth, Grigory Nikulin, Nicky McCreesh

  31-03-2016

  https://doi.org/10.4081/gh.2016.406

  3835

  PDF: 1391

  HTML: 1054
• 

  Spatial association between COVID-19 incidence rate and nighttime light index

  Amornrat Luenam, Nattapong Puttanapong

  18-03-2022

  https://doi.org/10.4081/gh.2022.1066

  1743

  PDF: 555

  HTML: 85
• 

  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

  2170

  PDF: 953

  HTML: 127
• 

  Spatial analysis and modelling of depression relative to social vulnerability index across the United States

  Kiara M. Rivera, Abolfazl Mollalo

  01-09-2022

  https://doi.org/10.4081/gh.2022.1132

  3886

  PDF: 1870

  HTML: 456
• 

  Impact of the COVID-19 pandemic on the number of births in Pernambuco Brazil

  Heitor Victor Veiga da Costa, Cristine Vieira do Bonfim, Wilson Fusco, Morvan de Melo Moreira, Fernando Maciano de Paula Neto

  15-06-2022

  https://doi.org/10.4081/gh.2022.1069

  995

  PDF: 463

  HTML: 50
• 

  Identifying counties vulnerable to diabetes from obesity prevalence in the United States: a spatiotemporal analysis

  Xiao Li, Amanda Staudt, Lung-Chang Chien

  21-11-2016

  https://doi.org/10.4081/gh.2016.439

  2533

  PDF: 1203

  APPENDIX: 411

  HTML: 1128
• 

  Spatial access to public hospitals during COVID-19 in Nottinghamshire, UK

  Jishuo Zhang, Meifang Li

  29-11-2022

  https://doi.org/10.4081/gh.2022.1123

  902

  PDF: 691

  HTML: 62
• 

  Post-traumatic stress in people from the interior drylands of the Maule region, Chile in the context of climate change

  Cristian Cáceres, Marcelo Leiva-Bianchi, Yony Ormazábal, Carlos Mena, Juan Carlos Cantillana

  18-05-2022

  https://doi.org/10.4081/gh.2022.1045

  1822

  PDF: 831

  HTML: 49
• 

  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

  1857

  PDF: 917

  Supplementary Materials: 133

  HTML: 47
• 

  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

  1482

  PDF: 693

  HTML: 22
• 

  Future malaria spatial pattern based on the potential global warming impact in South and Southeast Asia

  Hassan M. Khormi, Lalit Kumar

  21-11-2016

  https://doi.org/10.4081/gh.2016.416

  5175

  PDF: 2387

  HTML: 1980
• 

  Spatial access disparities to primary health care in rural and remote Australia

  Matthew Richard McGrail, John Stirling Humphreys

  04-11-2015

  https://doi.org/10.4081/gh.2015.358

  21977

  PDF: 9121

  HTML: 2318
• 

  Spatial autocorrelation pattern of COVID-19 vaccine coverage in Thailand 2021 and 2022

  Sarayu Muntaphan, Kittipong Sornlorm

  13-05-2025

  https://doi.org/10.4081/gh.2025.1368

  1138

  PDF: 216

  HTML: 51
• 

  Estimating malaria burden in Nigeria: a geostatistical modelling approach

  Nnadozie Onyiri

  04-11-2015

  https://doi.org/10.4081/gh.2015.306

  7093

  PDF: 2946

  HTML: 3252
• 

  Prehistoric human migrations: a prospective subject for modelling using geographical information systems

  Robert Bergquist

  25-05-2023

  https://doi.org/10.4081/gh.2023.1210

  1252

  PDF: 440

  HTML: 27
• 

  Spatio-temporal trends and distribution patterns of typhoid disease in Uganda from 2012 to 2017

  Kamukama Ismail, Gilbert Maiga, Denis Ssebuggwawo, Peter Nabende, Ali Mansourian

  07-01-2021

  https://doi.org/10.4081/gh.2020.860

  3011

  PDF: 1008

  HTML: 23

• Spatio-temporal and stochastic modelling of severe acute respiratory syndrome

  Poh-Chin Lai, Kim-Hung Kwong, Ho-Ting Wong

  183-192

  01-11-2013

  https://doi.org/10.4081/gh.2013.65

  1672

  PDF: 1218

• Spatial and spatio-temporal clusters of lung cancer incidence by stage of disease in Michigan, United States 1985-2018

  Qiong Zhang, Shangrui Zhu, Sue C. Grady, Anqi Wang, Hollis Hutchings, Jessica Cox, Andrew Popoff, Ikenna Okereke

  14-02-2024

  https://doi.org/10.4081/gh.2024.1219

  6332

  PDF: 951

  Supplementary Materials: 112

  HTML: 82
• 

  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

  851

  PDF: 412

  HTML: 20
• 

  Spatial variability of mother-to-child human immunodeficiency virus transmission in a province in the Brazilian Rainforest: An ecological study

  Marcus Matheus Quadros Santos, Bianca Alessandra Gomes do Carmo, Taymara Barbosa Rodrigues, Bruna Rafaela Leite Dias, Cleyton Abreu Martins, Glenda Roberta Oliveira Naiff Ferreira, Andressa Tavares Parente, Cíntia Yollete Urbano Pauxis Aben-Atha, Sandra Helena Isse Polaro, Eliã Pinheiro Botelho

  29-11-2022

  https://doi.org/10.4081/gh.2022.1101

  859

  PDF: 744

  HTML: 19
• 

  Use of soil moisture active passive satellite data and WorldClim 2.0 data to predict the potential distribution of visceral leishmaniasis and its vector Lutzomyia longipalpis in Sao Paulo and Bahia states, Brazil

  Moara de Santana Martins Rodgers, Elivelton Fonseca, Prixia del Mar Nieto, John B. Malone, Jeffery C. Luvall, Jennifer C. McCarroll, Ryan Harry Avery, Maria Emilia Bavia, Raul Guimaraes, Xue Wen, Marta Mariana Nascimento Silva, Deborah D.M.T. Carneiro, Luciana Lobato Cardim

  08-06-2022

  https://doi.org/10.4081/gh.2022.1095

  1386

  PDF: 827

  HTML: 49

• Clustering of disability caused by unintentional injury among 15- to 60-year-old: a challenge in rapidly developing countries

  Yi-Lan Liao, Jin-Feng Wang, Gong Chen, Wei Du, Xin-Ming Song, Xiao Yun, Xiao-Ying Zheng

  13-22

  01-11-2013

  https://doi.org/10.4081/gh.2013.50

  1720

  PDF: 921

• Modeling freshwater snail habitat suitability and areas of potential snail-borne disease transmission in Uganda

  A.S. Stensgaard, A. Jørgensen, N.B. Kabatereine, C. Rahbek, T.K. Kristensen

  93-104

  01-11-2006

  https://doi.org/10.4081/gh.2006.284

  2886

  PDF: 1540

• Spatial distribution and risk factors of influenza in Jiangsu province, China, based on geographical information system

  Jia-Cheng Zhang, Wen-Dong Liu, Qi Liang, Jian-Li Hu, Jessie Norris, Ying Wu, Chang-Jun Bao, Fen-Yang Tang, Peng Huang, Yang Zhao, Rong-Bin Yu, Ming-Hao Zhou, Hong-Bing Shen, Feng Chen, Zhi-Hang Peng

  429-435

  01-05-2014

  https://doi.org/10.4081/gh.2014.31

  3054

  PDF: 1191

• Susceptibility mapping of visceral leishmaniasis based on fuzzy modelling and group decision-making methods

  Mohamadreza Rajabi, Ali Mansourian, Ahad Bazmani

  37-50

  01-11-2012

  https://doi.org/10.4081/gh.2012.103

  1581

  PDF: 938

• Geospatial Health: the first five years

  Jürg Utzinger, Laura Rinaldi, John B. Malone, Stefanie J. Krauth, Thomas K. Kristensen, Giuseppe Cringoli, Robert Bergquist

  137-154

  01-11-2011

  https://doi.org/10.4081/gh.2011.166

  2814

  PDF: 1111

• Environmental inducers of schistosomiasis mansoni in Campinas, Brazil

  Francisco Anaruma Filho, João Moreno Sant'Ana, Rozely Ferreira dos Santos, Cláudio Luiz Castagna

  79-91

  01-11-2010

  https://doi.org/10.4081/gh.2010.189

  1206

  PDF: 834

• Climate-based risk models for Fasciola hepatica in Colombia

  Natalia Valencia-Lòpez, John B. Malone, Catalina Gòmez Carmona, Luz E. Velásquez

  S75-S85

  01-09-2012

  https://doi.org/10.4081/gh.2012.125

  2801

  PDF: 1427

• Mapping and prediction of schistosomiasis in Nigeria using compiled survey data and Bayesian geospatial modelling

  Uwem F. Ekpo, Eveline Hürlimann, Nadine Schur, Akinola. S. Oluwole, Eniola M. Abe, Margaret A. Mafe, Obiageli J. Nebe, Sunday Isiyaku, Francisca Olamiju, Mukaila Kadiri, Temitope O.S. Poopola, Eka I. Braide, Yisa Saka, Chiedu F. Mafiana, Thomas K. Kristensen, Jürg Utzinger, Penelope Vounatsou

  355-366

  01-05-2013

  https://doi.org/10.4081/gh.2013.92

  5255

  PDF: 1879

• Bayesian risk maps for Schistosoma mansoni and hookworm mono-infections in a setting where both parasites co-exist

  Giovanna Raso, Penelope Vounatsou, Donald P. McManus, Jürg Utzinger

  85-96

  01-11-2007

  https://doi.org/10.4081/gh.2007.257

  2356

  PDF: 816

• Climate change and the potential global distribution of Aedes aegypti: spatial modelling using geographical information system and CLIMEX

  Hassan M. Khormi, Lalit Kumar

  405-415

  01-05-2014

  https://doi.org/10.4081/gh.2014.29

  9161

  PDF: 4552

• 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

  1626

  PDF: 991

• Assessing the risk for dengue fever based on socioeconomic and environmental variables in a geographical information system environment

  Hassan M. Khormi, Lalit Kumar

  171-176

  01-05-2012

  https://doi.org/10.4081/gh.2012.135

  3099

  PDF: 1215

• Accounting for autocorrelation in multi-drug resistant tuberculosis predictors using a set of parsimonious orthogonal eigenvectors aggregated in geographic space

  Benjamin G. Jacob, Fiorella Krapp, Mario Ponce, Eduardo Gotuzzo, Daniel A. Griffith, Robert J. Novak

  201-217

  01-05-2010

  https://doi.org/10.4081/gh.2010.201

  1602

  PDF: 936
• 

  The spatial distribution of interleukin-4 (IL-4) reference values in China based on a back propagation (BP) neural network

  Zhao Rong Huang, Miao Ge, Xin Rui Pang, Pu Song, Congxia Wang

  13-09-2023

  https://doi.org/10.4081/gh.2023.1197

  1086

  PDF: 464

  HTML: 14

• Application of a degree-day model of West Nile virus transmission risk to the East Coast of the United States of America

  Sarah K. Konrad, Scott N. Miller

  15-20

  01-11-2012

  https://doi.org/10.4081/gh.2012.100

  1296

  PDF: 712

• Rapid GIS-based profiling of West Nile virus transmission: defining environmental factors associated with an urbansuburban outbreak in Northeast Ohio, USA

  A. Desiree LaBeaud, Ann-Marie Gorman, Joe Koonce, Christopher Kippes, John McLeod, Joe Lynch, Timothy Gallagher, Charles H. King, Anna M. Mandalakas

  215-225

  01-05-2008

  https://doi.org/10.4081/gh.2008.245

  2140

  PDF: 824
• 

  Modelling and analyzing spatial clusters of leptospirosis based on satellite-generated measurements of environmental factors in Thailand during 2013-2015

  Amornrat Luenam, Nattapong Puttanapong

  26-11-2020

  https://doi.org/10.4081/gh.2020.856

  3086

  PDF: 1057

  HTML: 68
• 

  Typhoid fever in Jakarta, Indonesia 2017-2023: spatial clustering and seasonality of hospitalization data to inform better intervention

  Mujiyanto Mujiyanto, Basuki Rachmat, Aris Yulianto, Made Agus Nurjana, Wawan Ridwan, Endang Puji Astuti, Doni Lasut, Pandji Wibawa Dhewantara

  15-05-2025

  https://doi.org/10.4081/gh.2025.1372

  1518

  PDF: 400

  HTML: 186
• 

  Post-pandemic COVID-19 estimated and forecasted hotspots in the Association of Southeast Asian Nations (ASEAN) countries in connection to vaccination rate

  I Gede Nyoman Mindra Jaya, Yudhie Andriyana, Bertho Tantular

  22-03-2022

  https://doi.org/10.4081/gh.2022.1070

  1308

  PDF: 416

  HTML: 32

• Virtual globes and geospatial health: the potential of new tools in the management and control of vector-borne diseases

  Anna-Sofie Stensgaard, Christopher F.L. Saarnak, Jürg Utzinger, Penelope Vounatsou, Christopher Simoonga, Gabriel Mushinge, Carsten Rahbek, Flemming Møhlenberg, Thomas K. Kristensen

  127-141

  01-05-2009

  https://doi.org/10.4081/gh.2009.216

  2053

  PDF: 1664

• Ecological study and risk mapping of leishmaniasis in an endemic area of Brazil based on a geographical information systems approach

  Alba Valéria Machado da Silva, Monica de Avelar Figueiredo Mafra Magalhães, Reginaldo Peçanha Brazil, João Carlos Araujo Carreira

  33-40

  01-11-2011

  https://doi.org/10.4081/gh.2011.155

  1614

  PDF: 930

• Environmental determinants of the distribution of Chagas disease vectors in south-eastern Guatemala

  Dulce Maria Bustamante, Maria Carlota Monroy, Antonieta Guadalupe Rodas, Jaime Abraham Juarez, John B. Malone

  199-211

  01-05-2007

  https://doi.org/10.4081/gh.2007.268

  3108

  PDF: 969

• The physical environment and health-enhancing activity during the school commute: global positioning system, geographical information systems and accelerometry

  David McMinn, Nicolas M. Oreskovic, Matt J. Aitkenhead, Derek W. Johnston, Shemane Murtagh, David A. Rowe

  569-572

  01-05-2014

  https://doi.org/10.4081/gh.2014.46

  1779

  PDF: 1163

• Geo-referencing livestock farms as tool for studying cystic echinococcosis epidemiology in cattle and water buffaloes from southern Italy

  Giuseppe Cringoli, Laura Rinaldi, Vincenzo Musella, Vincenzo Veneziano, Maria Paola Maurelli, Francesco Di Pietro, Michele Frisiello, Salvatore Di Pietro

  105-111

  01-11-2007

  https://doi.org/10.4081/gh.2007.259

  2317

  PDF: 980

• A climate distribution model of malaria transmission in Sudan

  Mohammed I. Musa, Shamarina Shohaimi, Nor R. Hashim, Isthrinayagy Krishnarajah

  27-36

  01-11-2012

  https://doi.org/10.4081/gh.2012.102

  2237

  PDF: 1049

• Modeling spatio-temporal risk changes in the incidence of dengue fever in Saudi Arabia: a geographical information system case study

  Hassan M. Khormi, Lalit Kumar, Ramze A. Elzahrany

  77-84

  01-11-2011

  https://doi.org/10.4081/gh.2011.159

  2618

  PDF: 1289

• Environmental and socio-economic risk modelling for Chagas disease in Bolivia

  Paula Mischler, Michael Kearney, Jennifer C. McCarroll, Ronaldo G.C. Scholte, Penelope Vounatsou, John B. Malone

  S59-S66

  01-09-2012

  https://doi.org/10.4081/gh.2012.123

  3511

  PDF: 1206
• 

  There is more to satellite imagery than meets the eye

  Robert Bergquist, John B. Malone

  31-05-2022

  https://doi.org/10.4081/gh.2022.1106

  2344

  PDF: 505

  HTML: 19
• 

  The impact of geographic mobility on the spread of COVID-19 in Hong Kong

  Paulina Pui Yun Wong, Chien-Tat Low, Poh-Chin Lai

  14-02-2022

  https://doi.org/10.4081/gh.2022.1022

  2897

  PDF: 463

  HTML: 116
• 

  A Bayesian spatiotemporal Poisson conditional autoregressive model for dengue haemorrhagic fever in Indonesia integrating satellite-generated environmental data

  Sukarna Sukarna, Hari Wijayanto, Yenni Angraini, Anang Kurnia

  18-07-2025

  https://doi.org/10.4081/gh.2025.1379

  436

  PDF: 201

  HTML: 11
• 

  Geographic distribution of canine heartworm (Dirofilaria immitis) infection in stray dogs of eastern Romania

  Lavinia Ciucă, Vincenzo Musella, Liviu D. Miron, Maria Paola Maurelli, Giuseppe Cringoli, Antonio Bosco, Laura Rinaldi

  21-11-2016

  https://doi.org/10.4081/gh.2016.499

  4223

  PDF: 1558

  HTML: 2252
• 

  Dynamic effect of economic growth on the persistence of suicide rates

  Tzu-Yi Yang, Yu-Tai Yang, Ssu-Han Chen, Yu-Ting Lan, Chia-Jui Peng

  25-05-2023

  https://doi.org/10.4081/gh.2023.1201

  733

  PDF: 508

  HTML: 20
• 

  Revealing spatio-temporal patterns of rabies spread among various categories of animals in the Republic of Kazakhstan, 2010-2013

  Sarsenbay K. Abdrakhmanov, Kanatzhan K. Beisembayev, Fedor I. Кorennoy, Gulzhan N. Yessembekova, Dosym B. Кushubaev, Ablaikhan S. Кadyrov

  31-05-2016

  https://doi.org/10.4081/gh.2016.455

  2288

  PDF: 1194

  HTML: 1285
• 

  Mitigating infectious disease risks through non-stationary flood frequency analysis: a case study in Malaysia based on natural disaster reduction strategy

  Nur Amalina Mat Jan, Muhammad Fadhil Marsani, Loshini Thiruchelvam, Nur Balqishanis Zainal Abidin, Ani Shabri, Sarah A'fifah Abdullah Sani

  13-11-2023

  https://doi.org/10.4081/gh.2023.1236

  1285

  PDF: 575

  HTML: 23
• 

  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

  6015

  PDF: 2291

  Appendix: 316

  HTML: 264

• Healthcare-seeking behavior and spatial variation of internal migrants with chronic diseases: a nationwide empirical study in China

  Dan Li, Dawei Gao, Masaaki Yamada, Chuangbin Chen, Liuchun Xiang, Haisong Nie

  28-05-2024

  https://doi.org/10.4081/gh.2024.1255

  2335

  PDF: 608

  HTML: 57
• 

  Dynamic risk model for Rift Valley fever outbreaks in Kenya based on climate and disease outbreak data

  David Gikungu, Jacob Wakhungu, Donald Siamba, Edward Neyole, Richard Muita, Bernard Bett

  31-05-2016

  https://doi.org/10.4081/gh.2016.377

  2992

  PDF: 1330

  HTML: 1433
• 

  Evaluating the utility of companion animal tick surveillance practices for monitoring spread and occurrence of human Lyme disease in West Virginia, 2014-2016

  Brian Hendricks, Miguella Mark-Carew, Jamison Conley

  13-11-2017

  https://doi.org/10.4081/gh.2017.582

  2433

  PDF: 765

  HTML: 743
• 

  Point process methods in epidemiology: application to the analysis of human immunodeficiency virus/acquired immunodeficiency syndrome mortality in urban areas

  Jose Antonio Quesada, Inmaculada Melchor, Andreu Nolasco

  26-05-2017

  https://doi.org/10.4081/gh.2017.506

  1673

  PDF: 894

  HTML: 1022
• 

  Epidemiological aspects and spatial distribution of human and canine visceral leishmaniasis in an endemic area in northeastern Brazil

  Roseane Campos, Márcio Santos, Gabriel Tunon, Luana Cunha, Lucas Magalhães, Juliana Moraes, Danielle Ramalho, Sanmy Lima, José Antônio Pacheco, Michael Lipscomb, Amélia Ribeiro de Jesus, Roque Pacheco de Almeida

  11-05-2017

  https://doi.org/10.4081/gh.2017.503

  4986

  PDF: 1978

  HTML: 1139
• 

  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

  1810

  PDF: 806

  HTML: 60
• 

  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

  3886

  PDF: 1634

  APPENDIX 1: 430

  APPENDIX 2: 428

  HTML: 1639
• 

  Spatiotemporal distribution and geostatistically interpolated mapping of the melioidosis risk in an endemic zone in Thailand

  Jaruwan Wongbutdee, Jutharat Jittimanee, Wacharapong Saengnill

  05-07-2023

  https://doi.org/10.4081/gh.2023.1189

  1534

  PDF: 784

  HTML: 58
• 

  Spatial analysis of the relationship between out-of-pocket expenditure and socioeconomic status in South Korea

  Young-Gyu Kwon, Man-Kyu Choi

  25-05-2023

  https://doi.org/10.4081/gh.2023.1175

  1172

  PDF: 639

  HTML: 32
• 

  A 24-year exploratory spatial data analysis of Lyme disease incidence rate in Connecticut, USA

  Abolfazl Mollalo, Jason K. Blackburn, Lillian R. Morris, Gregory E. Glass

  08-11-2017

  https://doi.org/10.4081/gh.2017.588

  2741

  PDF: 1225

  HTML: 1001
• 

  Exploring the distribution of risk factors for drop-out from Ponseti treatment for clubfoot across Bangladesh using geospatial cluster analysis

  Manon Pigeolet, Tarinee Kucchal, Matthew T. Hey, Marcia C. Castro, Angela Margaret Evans, Tarsicio Uribe-Leitz, Mohommad Mamun Hossen Chowhury, Sabrina Juran

  25-05-2023

  https://doi.org/10.4081/gh.2023.1174

  960

  PDF: 499

  HTML: 7
• 

  Prevalence of hypertension in Thailand: Hotspot clustering detected by spatial analysis

  Wongsa Laohasiriwong, Nattapong Puttanapong, Atthawit Singsalasang

  07-05-2018

  https://doi.org/10.4081/gh.2018.608

  3761

  PDF: 1835

  HTML: 575

• 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

  2394

  PDF: 616

  HTML: 77
• 

  A spatio-temporal study of state-wide case-fatality risks during the first wave of the COVID-19 pandemic in Mexico

  Ricardo Ramìrez-Aldana , Juan Carlos Gomez-Verjan, Omar Yaxmehen Bello-Chavolla , Lizbeth Naranjo

  24-03-2022

  https://doi.org/10.4081/gh.2022.1054

  1328

  PDF: 379

  Appendix: 197

  HTML: 28
• 

  Public domain small-area cancer incidence data for New York State, 2005-2009

  Francis P. Boscoe, Thomas O. Talbot, Martin Kulldorff

  18-04-2016

  https://doi.org/10.4081/gh.2016.304

  1980

  PDF: 1121

  HTML: 858
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  Comparison of single- and multi-scale models for the prediction of the Culicoides biting midge distribution in Germany

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