Predicting the environmental suitability for onchocerciasis in Africa as an aid to elimination planning

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dc.contributor.author Ahmadpour, Ehsan
dc.contributor.author Beshir Ahmed, Muktar
dc.contributor.author Akalu, Temesgen Yihunie
dc.contributor.author Al- Aly, Ziyad
dc.contributor.author Alanezi, Fahad Mashhour
dc.contributor.author Alanzi, Turki M.
dc.contributor.author Alipour, Vahid
dc.contributor.author Andrei, Catalina Liliana
dc.contributor.author Ansari, Fereshteh
dc.contributor.author Ansha, Mustafa Geleto
dc.contributor.author Anvari, Davood
dc.contributor.author Appiah, Seth Christopher Yaw
dc.contributor.author Arabloo, Jalal
dc.contributor.author Arnold, Benjamin F.
dc.contributor.author Ausloos, Marcel
dc.contributor.author Ayanore, Martin Amogre
dc.contributor.author Baig, Atif Amin
dc.contributor.author Banach, Maciej
dc.contributor.author Barac, Aleksandra
dc.contributor.author Barnighausen, Till Winfried
dc.contributor.author Bayati, Mohsen
dc.contributor.author Bhattacharyya, Krittika
dc.contributor.author Bhutta, Zulfiqar A.
dc.contributor.author Bibi, Sadia
dc.contributor.author Bijani, Ali
dc.contributor.author Bohlouli, Somayeh
dc.contributor.author Bohluli, Mahdi
dc.contributor.author Brady, Oliver J.
dc.contributor.author Bragazzi, Nicola Luigi
dc.contributor.author Butt, Zahid A.
dc.contributor.author Carvalho, Felix
dc.contributor.author Chatterjee, Souranshu
dc.contributor.author Chattu, Vijay Kumar
dc.contributor.author Chattu, Soosanna Kumary
dc.contributor.author Cormier, Natalie Maria
dc.contributor.author Dahlawi, Saad M.A.
dc.contributor.author Damiani, Giovanni
dc.contributor.author Daoud, Farah
dc.contributor.author Darwesh, Aso Mohammad
dc.contributor.author Daryani, Ahmad
dc.contributor.author Deribe, Kebede
dc.contributor.author Dharmaratne, Samath Dhamminda
dc.contributor.author Diaz, Daniel
dc.contributor.author Do, Hoa Thi
dc.contributor.author Zaki, Maysaa El Sayed
dc.contributor.author Tantawi, Maha El
dc.contributor.author Elemineh, Demelash Abewa
dc.contributor.author Faraj, Anwar
dc.contributor.author Harandi, Majid Fasihi
dc.contributor.author Fatahi, Yousef
dc.contributor.author Feigin, Valery L.
dc.contributor.author Fernandes, Eduarda
dc.contributor.author Foigt, Nataliya A.
dc.contributor.author Foroutan, Masoud
dc.contributor.author Franklin, Richard Charles
dc.contributor.author Gubari, Mohammed Ibrahim Mohialdeen
dc.contributor.author Guido, Davide
dc.contributor.author Guo, Yuming
dc.contributor.author Haj-Mirzaian, Arvin
dc.contributor.author Abdullah, Kanaan Hamagharib
dc.contributor.author Hamidi, Samer
dc.contributor.author Herteliu, Claudiu
dc.contributor.author De Hidru, Hagos Degefa
dc.contributor.author Higazi, Tarig B.
dc.contributor.author Hossain, Naznin
dc.contributor.author Hosseinzadeh, Mehdi
dc.contributor.author Househ, Mowafa
dc.contributor.author Ilesanmi, Olayinka Stephen
dc.contributor.author Ilic, Milena D.
dc.contributor.author Ilic, Irena M.
dc.contributor.author Iqbal, Usman
dc.contributor.author Irvani, Seyed Sina Naghibi
dc.contributor.author Jha, Ravi Prakash
dc.contributor.author Joukar, Farahnaz
dc.contributor.author Jozwiak, Jacek Jerzy
dc.contributor.author Kabir, Zubair
dc.contributor.author Kalankesh, Leila R.
dc.contributor.author Kalhor, Rohollah
dc.contributor.author Matin, Behzad Karami
dc.contributor.author Karimi, Salah Eddin
dc.contributor.author Kasaeian, Amir
dc.contributor.author Kavetskyy, Taras
dc.contributor.author Kayode, Gbenga A.
dc.contributor.author Karyani, Ali Kazemi
dc.contributor.author Kelbore, Abraham Getachew
dc.contributor.author Keramati, Maryam
dc.contributor.author Khalilov, Rovshan
dc.contributor.author Khan, Ejaz Ahmad
dc.contributor.author Khan, Md Nuruzzaman Nuruzzaman
dc.contributor.author Khatab, Khaled
dc.contributor.author Khater, Mona M.
dc.contributor.author Kianipour, Neda
dc.contributor.author Kibret, Kelemu Tilahun
dc.contributor.author Kim, Yun Jin
dc.contributor.author Kosen, Soewarta
dc.contributor.author Krohn, Kris J.
dc.contributor.author Kusuma, Dian
dc.contributor.author La Vecchia, Carlo
dc.contributor.author Lansingh, Charles
dc.contributor.author Lee, Paul H.
dc.contributor.author LeGrand, Kate E.
dc.contributor.author Li, Shanshan
dc.contributor.author Longbottom, Joshua
dc.contributor.author Abd El Razek, Hassan Magdy
dc.contributor.author Abd El Razek, Muhammed Magdy
dc.contributor.author Maleki, Afshin
dc.contributor.author Mamun, Abdullah A.
dc.contributor.author Manafi, Ali
dc.contributor.author Manafi, Navid
dc.contributor.author Mansournia, Mohammad Ali
dc.contributor.author Martins- Melo, Francisco Rogerlandio
dc.contributor.author Mazidi, Mohsen
dc.contributor.author McAlinden, Colm
dc.contributor.author Meharie, Birhanu Geta
dc.contributor.author Mendoza, Walter
dc.contributor.author Mengesha, Endalkachew Worku
dc.contributor.author Mengistu, Desalegn Tadese
dc.contributor.author Mereta, Seid Tiku
dc.contributor.author Mestrovic, Tomislav
dc.contributor.author Miller, Ted R.
dc.contributor.author Miri, Mohammad
dc.contributor.author Moghadaszadeh, Masoud
dc.contributor.author Mohammadian-Hafshejani, Abdollah
dc.contributor.author Mohammadpourhodki, Reza
dc.contributor.author Mohammed, Shafiu
dc.contributor.author Mohammed, Salahuddin
dc.contributor.author Moradi, Masoud
dc.contributor.author Moradzadeh, Rahmatollah
dc.contributor.author Moraga, Paula
dc.contributor.author Mosser, Jonathan F.
dc.contributor.author Naderi, Mehdi
dc.contributor.author Nagarajan, Ahamarshan Jayaraman
dc.contributor.author Naik, Gurudatta
dc.contributor.author Negoi, Ionut
dc.contributor.author Nguyen, Cuong Tat
dc.contributor.author Nguyen, Huong Lan Thi
dc.contributor.author Nguyen, Trang Huyen
dc.contributor.author Nikbakhsh, Rajan
dc.contributor.author Oancea, Bogdan
dc.contributor.author Olagunju, Tinuke O.
dc.contributor.author Olagunju, Andrew T.
dc.contributor.author Bali, Ahmed Omar
dc.contributor.author Onwujekwe, Obinna E.
dc.contributor.author Pana, Adrian
dc.contributor.author Pourjafar, Hadi
dc.contributor.author Rahim, Fakher
dc.contributor.author Rahman, Mohammad Hifz Ur
dc.contributor.author Rathi, Priya
dc.contributor.author Rawaf, Salman
dc.contributor.author Rawaf, David Laith
dc.contributor.author Rawassizadeh, Reza
dc.contributor.author Resnikoff, Serge
dc.contributor.author Reta, Melese Abate
dc.contributor.author Rezapour, Aziz
dc.contributor.author Rubagotti, Enrico
dc.contributor.author Rubino, Salvatore
dc.contributor.author Sadeghi, Ehsan
dc.contributor.author Saghafipour, Abedin
dc.contributor.author Sajadi, S. Mohammad
dc.date.accessioned 2022-12-14T04:46:33Z
dc.date.available 2022-12-14T04:46:33Z
dc.date.issued 2021-07-28
dc.description SUPPORTING INFORMATION : FIGURE S1. Data coverage by year. Here we visualise the volume of data used in the analysis by country and year. Larger circles indicate more data inputs. ‘NA’ indicates records for which no year was reported (eg, ‘pre-2000’). https://doi.org/10.1371/journal.pntd.0008824.s001 en_US
dc.description FIGURE S2. Illustration of covariate values for year 2000. Maps were produced using ArcGIS Desktop 10.6. https://doi.org/10.1371/journal.pntd.0008824.s002 en_US
dc.description FIGURE S3. Environmental suitability of onchocerciasis including locations that have received MDA for which no pre-intervention data are available. This plot shows suitability predictions from green (low = 0%) to pink (high = 100%), representing those areas where environmental conditions are most similar to prior pathogen detections. Countries in grey with hatch marks were excluded from the analysis based on a review of national endemicity status. Areas in grey only represent locations masked due to sparse population. Maps were produced using ArcGIS Desktop 10.6 and shapefiles to visualize administrative units are available at https://espen.afro.who.int/tools-resources/cartography-database. https://doi.org/10.1371/journal.pntd.0008824.s003 en_US
dc.description FIGURE S4. Environmental suitability prediction uncertainty including locations that have received MDA for which no pre-intervention data are available. This plot shows uncertainty associated with environmental suitability predictions colored from blue to red (least to most uncertain). Countries in grey with hatch marks were excluded from the analysis based on a review of national endemicity status. Areas in grey only represent locations masked due to sparse population. Maps were produced using ArcGIS Desktop 10.6 and shapefiles to visualize administrative units are available at https://espen.afro.who.int/tools-resources/cartography-database. https://doi.org/10.1371/journal.pntd.0008824.s004 en_US
dc.description FIGURE S5. Environmental suitability of onchocerciasis excluding morbidity data. This plot shows suitability predictions from green (low = 0%) to pink (high = 100%), representing those areas where environmental conditions are most similar to prior pathogen detections. Countries in grey with hatch marks were excluded from the analysis based on a review of national endemicity status. Areas in grey only represent locations masked due to sparse population. Maps were produced using ArcGIS Desktop 10.6 and shapefiles to visualize administrative units are available at https://espen.afro.who.int/tools-resources/cartography-database. https://doi.org/10.1371/journal.pntd.0008824.s005 en_US
dc.description FIGURE S6. Environmental suitability prediction uncertainty excluding morbidity data. This plot shows uncertainty associated with environmental suitability predictions colored from blue to red (least to most uncertain). Countries in grey with hatch marks were excluded from the analysis based on a review of national endemicity status. Areas in grey only represent locations masked due to sparse population. https://doi.org/10.1371/journal.pntd.0008824.s006 en_US
dc.description FIGURE S7. Covariate Effect Curves for all onchocerciasis occurrences (measures of infection prevalence and disability). On the right set of axes we show the frequency density of the occurrences taking covariate values over 20 bins of the horizontal axis. The left set of axes shows the effect of each on the model, where the mean effect is plotted on the black line and its uncertainty is represented by the upper and lower confidence interval bounds plotted in dark grey. The figures show the fit per covariate relative to the data that correspond to specific values of the covariate. https://doi.org/10.1371/journal.pntd.0008824.s007 en_US
dc.description FIGURE S8. Covariate Effect Curves for all onchocerciasis occurrences (measures of infection prevalence and disability). On the right set of axes we show the frequency density of the occurrences taking covariate values over 20 bins of the horizontal axis. The left set of axes shows the effect of each on the model, where the mean effect is plotted on the black line and its uncertainty is represented by the upper and lower confidence interval bounds plotted in dark grey. https://doi.org/10.1371/journal.pntd.0008824.s008 en_US
dc.description FIGURE S9. ROC analysis for threshold. Results of the area under the receiver operating characteristic (ROC) curve analysis are presented below, with false positive rate (FPR) on the x-axis and true positive rate (TPR) on the y-axis. The red dot on the curve represents the location on the curve that corresponds to a threshold that most closely agreed with the input data. For each of the 100 BRT models, we estimated the optimal threshold that maximised agreement between occurrence inputs (considered true positives) and the mean model predictions as 0·71. https://doi.org/10.1371/journal.pntd.0008824.s009 en_US
dc.description TABLE S1. Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER) checklist. https://doi.org/10.1371/journal.pntd.0008824.s010 en_US
dc.description TABLE S2. Total number of occurrence data classified as point and polygon inputs by diagnostic. We present the total number of occurrence points extracted from the input data sources by diagnostic type. ‘Other diagnostics’ include: DEC Patch test; Knott’s Method (Mazotti Test); 2 types of LAMP; blood smears; and urine tests. https://doi.org/10.1371/journal.pntd.0008824.s011 en_US
dc.description TABLE S3. Total number of occurrence data classified as point and polygon inputs by location. https://doi.org/10.1371/journal.pntd.0008824.s012 en_US
dc.description TABLE S4. Covariate information. https://doi.org/10.1371/journal.pntd.0008824.s013 en_US
dc.description TEXT S1. Details outlining construction of occurrence dataset. https://doi.org/10.1371/journal.pntd.0008824.s014 en_US
dc.description TEXT S2. Covariate rationale. https://doi.org/10.1371/journal.pntd.0008824.s015 en_US
dc.description TEXT S3. Boosted regression tree methodology additional details. https://doi.org/10.1371/journal.pntd.0008824.s016 en_US
dc.description APPENDIX S1. Country-level maps and data results. Maps were produced using ArcGIS Desktop 10.6 and shapefiles to visualize administrative units are available at https://espen.afro.who.int/tools-resources/cartography-database. https://doi.org/10.1371/journal.pntd.0008824.s017 en_US
dc.description.abstract Recent evidence suggests that, in some foci, elimination of onchocerciasis from Africa may be feasible with mass drug administration (MDA) of ivermectin. To achieve continental elimination of transmission, mapping surveys will need to be conducted across all implementation units (IUs) for which endemicity status is currently unknown. Using boosted regression tree models with optimised hyperparameter selection, we estimated environmental suitability for onchocerciasis at the 5 × 5-km resolution across Africa. In order to classify IUs that include locations that are environmentally suitable, we used receiver operating characteristic (ROC) analysis to identify an optimal threshold for suitability concordant with locations where onchocerciasis has been previously detected. This threshold value was then used to classify IUs (more suitable or less suitable) based on the location within the IU with the largest mean prediction. Mean estimates of environmental suitability suggest large areas across West and Central Africa, as well as focal areas of East Africa, are suitable for onchocerciasis transmission, consistent with the presence of current control and elimination of transmission efforts. The ROC analysis identified a mean environmental suitability index of 071 as a threshold to classify based on the location with the largest mean prediction within the IU. Of the IUs considered for mapping surveys, 502% exceed this threshold for suitability in at least one 5 × 5-km location. The formidable scale of data collection required to map onchocerciasis endemicity across the African continent presents an opportunity to use spatial data to identify areas likely to be suitable for onchocerciasis transmission. National onchocerciasis elimination programmes may wish to consider prioritising these IUs for mapping surveys as human resources, laboratory capacity, and programmatic schedules may constrain survey implementation, and possibly delaying MDA initiation in areas that would ultimately qualify. en_US
dc.description.department Medical Microbiology en_US
dc.description.librarian am2022 en_US
dc.description.sponsorship This work was primarily supported by a grant from the Bill & Melinda Gates Foundation OPP1132415 (SIH). Financial support from the Neglected Tropical Disease Modelling Consortium (https://www.ntdmodelling.org/), which is funded by the Bill & Melinda Gates Foundation (grants No. OPP1184344 and OPP1186851), and joint centre funding (grant No. MR/R015600/1) by the UK Medical Research Council (MRC) and the UK Department for International Development (DFID) under the MRC/DFID Concordat agreement which is also part of the EDCTP2 programme supported by the European Union (MGB). en_US
dc.description.sponsorship The Neglected Tropical Disease Modelling Consortium which is funded by the Bill & Melinda Gates Foundation, the UK Medical Research Council (MRC) and the UK Department for International Development (DFID) under the MRC/DFID Concordat agreement which is also part of the EDCTP2 programme supported by the European Union (MGB). en_US
dc.description.uri http://www.plosNTDS.org en_US
dc.identifier.citation Cromwell, E.A., Osborne, J.C.P., Unnasch, T.R., Basáñez, M.-G., Gass, K.M., Barbre, K.A., et al. (2021) Predicting the environmental suitability for onchocerciasis in Africa as an aid to elimination planning. PLoS Neglected Tropical Diseases 15(7): e0008824. https://DOI.org/10.1371/journal.pntd.0008824. en_US
dc.identifier.issn 1935-2735 (print)
dc.identifier.issn 1935-2727 (online)
dc.identifier.other 10.1371/journal.pntd.0008824
dc.identifier.uri https://repository.up.ac.za/handle/2263/88775
dc.language.iso en en_US
dc.publisher Public Library of Science en_US
dc.rights The work is made available under the Creative Commons CC0. en_US
dc.subject Foci en_US
dc.subject Africa en_US
dc.subject Transmission en_US
dc.subject Mass drug administration (MDA) en_US
dc.subject Ivermectin en_US
dc.subject Implementation units (IUs) en_US
dc.subject Elimination of transmission en_US
dc.subject Receiver operating characteristic (ROC) en_US
dc.title Predicting the environmental suitability for onchocerciasis in Africa as an aid to elimination planning en_US
dc.type Article en_US


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