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Hu, Yi Li, Si Xia, Congcong Chen, Yue Lynn, Henry Zhang, Tiejun Xiong, Chenglong Chen, Gengxin He, Zonggui and Zhang, Zhijie 2017. Assessment of the national schistosomiasis control program in a typical region along the Yangtze River, China. International Journal for Parasitology, Vol. 47, Issue. 1, p. 21.
Wang, Craig Torgerson, Paul R. Höglund, Johan and Furrer, Reinhard 2017. Zero-inflated hierarchical models for faecal egg counts to assess anthelmintic efficacy. Veterinary Parasitology, Vol. 235, p. 20.
Araujo Navas, Andrea L. Hamm, Nicholas A. S. Soares Magalhães, Ricardo J. Stein, Alfred and Akogun, Oladele B. 2016. Mapping Soil Transmitted Helminths and Schistosomiasis under Uncertainty: A Systematic Review and Critical Appraisal of Evidence. PLOS Neglected Tropical Diseases, Vol. 10, Issue. 12, p. e0005208.
Houngbedji, Clarisse A. Chammartin, Frédérique Yapi, Richard B. Hürlimann, Eveline N’Dri, Prisca B. Silué, Kigbafori D. Soro, Gotianwa Koudou, Benjamin G. Assi, Serge-Brice N’Goran, Eliézer K. Fantodji, Agathe Utzinger, Jürg Vounatsou, Penelope and Raso, Giovanna 2016. Spatial mapping and prediction of Plasmodium falciparum infection risk among school-aged children in Côte d’Ivoire. Parasites & Vectors, Vol. 9, Issue. 1,
Houweling, Tanja A. J. Karim-Kos, Henrike E. Kulik, Margarete C. Stolk, Wilma A. Haagsma, Juanita A. Lenk, Edeltraud J. Richardus, Jan Hendrik de Vlas, Sake J. and Knopp, Stefanie 2016. Socioeconomic Inequalities in Neglected Tropical Diseases: A Systematic Review. PLOS Neglected Tropical Diseases, Vol. 10, Issue. 5, p. e0004546.
Stensgaard, Anna-Sofie Vounatsou, Penelope Onapa, Ambrose W. Utzinger, Jürg Pedersen, Erling M. Kristensen, Thomas K. Simonsen, Paul E. and Lammie, Patrick J. 2016. Ecological Drivers of Mansonella perstans Infection in Uganda and Patterns of Co-endemicity with Lymphatic Filariasis and Malaria. PLOS Neglected Tropical Diseases, Vol. 10, Issue. 1, p. e0004319.
Dalrymple, Ursula Mappin, Bonnie and Gething, Peter W. 2015. Malaria mapping: understanding the global endemicity of falciparum and vivax malaria. BMC Medicine, Vol. 13, Issue. 1,
Diboulo, Eric Sié, Ali Diadier, Diallo A Voules, Dimitrios A Karagiannis Yé, Yazoume and Vounatsou, Penelope 2015. Bayesian variable selection in modelling geographical heterogeneity in malaria transmission from sparse data: an application to Nouna Health and Demographic Surveillance System (HDSS) data, Burkina Faso. Parasites & Vectors, Vol. 8, Issue. 1, p. 118.
Forrer, Armelle Vounatsou, Penelope Sayasone, Somphou Vonghachack, Youthanavanh Bouakhasith, Dalouny Utzinger, Jürg Akkhavong, Kongsap Odermatt, Peter and Yang, Guo-Jing 2015. Risk Profiling of Hookworm Infection and Intensity in Southern Lao People’s Democratic Republic Using Bayesian Models. PLOS Neglected Tropical Diseases, Vol. 9, Issue. 3, p. e0003486.
Walz, Yvonne Wegmann, Martin Dech, Stefan Raso, Giovanna and Utzinger, Jürg 2015. Risk profiling of schistosomiasis using remote sensing: approaches, challenges and outlook. Parasites & Vectors, Vol. 8, Issue. 1,
Zhang, Zhi-Jie 2015. Analyzing and Modeling Spatial and Temporal Dynamics of Infectious Diseases.
Chimoyi, Lucy A and Musenge, Eustasius 2014. Spatial analysis of factors associated with HIV infection among young people in Uganda, 2011. BMC Public Health, Vol. 14, Issue. 1,
Chipeta, Michael G Ngwira, Bagrey M Simoonga, Christopher and Kazembe, Lawrence N 2014. Zero adjusted models with applications to analysing helminths count data. BMC Research Notes, Vol. 7, Issue. 1, p. 856.
Strunz, Eric C. Addiss, David G. Stocks, Meredith E. Ogden, Stephanie Utzinger, Jürg Freeman, Matthew C. and Hales, Simon 2014. Water, Sanitation, Hygiene, and Soil-Transmitted Helminth Infection: A Systematic Review and Meta-Analysis. PLoS Medicine, Vol. 11, Issue. 3, p. e1001620.
Upfill-Brown, Alexander M Lyons, Hil M Pate, Muhammad A Shuaib, Faisal Baig, Shahzad Hu, Hao Eckhoff, Philip A and Chabot-Couture, Guillaume 2014. Predictive spatial risk model of poliovirus to aid prioritization and hasten eradication in Nigeria. BMC Medicine, Vol. 12, Issue. 1,
Yapi, Richard B. Hürlimann, Eveline Houngbedji, Clarisse A. Ndri, Prisca B. Silué, Kigbafori D. Soro, Gotianwa Kouamé, Ferdinand N. Vounatsou, Penelope Fürst, Thomas N’Goran, Eliézer K. Utzinger, Jürg Raso, Giovanna and Stothard, J. Russell 2014. Infection and Co-infection with Helminths and Plasmodium among School Children in Côte d’Ivoire: Results from a National Cross-Sectional Survey. PLoS Neglected Tropical Diseases, Vol. 8, Issue. 6, p. e2913.
Alegana, Victor A. Atkinson, Peter M. Wright, Jim A. Kamwi, Richard Uusiku, Petrina Katokele, Stark Snow, Robert W. and Noor, Abdisalan M. 2013. Estimation of malaria incidence in northern Namibia in 2009 using Bayesian conditional-autoregressive spatial–temporal models. Spatial and Spatio-temporal Epidemiology, Vol. 7, p. 25.
Chammartin, Frédérique Hürlimann, Eveline Raso, Giovanna N’Goran, Eliézer K. Utzinger, Jürg and Vounatsou, Penelope 2013. Statistical methodological issues in mapping historical schistosomiasis survey data. Acta Tropica, Vol. 128, Issue. 2, p. 345.
Chipeta, Michael G. Ngwira, Bagrey Kazembe, Lawrence N. and Brooker, Simon 2013. Analysis of Schistosomiasis haematobium Infection Prevalence and Intensity in Chikhwawa, Malawi: An Application of a Two Part Model. PLoS Neglected Tropical Diseases, Vol. 7, Issue. 3, p. e2131.
Hay, S. I. Battle, K. E. Pigott, D. M. Smith, D. L. Moyes, C. L. Bhatt, S. Brownstein, J. S. Collier, N. Myers, M. F. George, D. B. and Gething, P. W. 2013. Global mapping of infectious disease. Philosophical Transactions of the Royal Society B: Biological Sciences, Vol. 368, Issue. 1614, p. 20120250.
Progress has been made in mapping and predicting the risk of schistosomiasis using Bayesian geostatistical inference. Applications primarily focused on risk profiling of prevalence rather than infection intensity, although the latter is particularly important for morbidity control. In this review, the underlying assumptions used in a study mapping Schistosoma mansoni infection intensity in East Africa are examined. We argue that the assumption of stationarity needs to be relaxed, and that the negative binomial assumption might result in misleading inference because of a high number of excess zeros (individuals without an infection). We developed a Bayesian geostatistical zero-inflated (ZI) regression model that assumes a non-stationary spatial process. Our model is validated with a high-quality georeferenced database from western Côte d'Ivoire, consisting of demographic, environmental, parasitological and socio-economic data. Nearly 40% of the 3818 participating schoolchildren were infected with S. mansoni, and the mean egg count among infected children was 162 eggs per gram of stool (EPG), ranging between 24 and 6768 EPG. Compared to a negative binomial and ZI Poisson and negative binomial models, the Bayesian non-stationary ZI negative binomial model showed a better fit to the data. We conclude that geostatistical ZI models produce more accurate maps of helminth infection intensity than the spatial negative binomial ones.
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