Cameroon/epidemiology; Animals; Humans; Animal Diseases/epidemiology; Animal Diseases/prevention & control; Principal Component Analysis; Cluster Analysis; Health Priorities; Public Health; Zoonoses/epidemiology; Zoonoses/prevention & control; Zoonoses/transmission; Decision Support Techniques; Animal Diseases; Cameroon; Zoonoses; Multidisciplinary
Abstract :
[en] The use of multi-criteria decision analysis (MCDA) for disease prioritization at the sub-national level in sub-Sahara Africa (SSA) is rare. In this research, we contextualized MCDA for parallel prioritization of endemic zoonoses and animal diseases in The Adamawa and North regions of Cameroon. MCDA was associated to categorical principal component analysis (CATPCA), and two-step cluster analysis. Six and seven domains made of 17 and 19 criteria (out of 70) respectively were selected by CATPCA for the prioritization of zoonoses and animal diseases, respectively. The most influencing domains were "public health" for zoonoses and "control and prevention" for animal diseases. Twenty-seven zoonoses and 40 animal diseases were ranked and grouped in three clusters. Sensitivity analysis resulted in high correlation between complete models and reduced models showing the robustness of the simplification processes. The tool used in this study can be applied to prioritize endemic zoonoses and transboundary animal diseases in SSA at the sub-national level and upscaled at the national and regional levels. The relevance of MCDA is high because of its contextualization process and participatory nature enabling better operationalization of disease prioritization outcomes in the context of African countries or other low and middle-income countries.
Disciplines :
Veterinary medicine & animal health
Author, co-author :
Mpouam, Serge Eugene ; School of Veterinary Medicine and Science, University of Ngaoundéré, Ngaoundéré, Cameroon
Ikoum, Dalida ; School of Veterinary Medicine and Science, University of Ngaoundéré, Ngaoundéré, Cameroon ; National Program for the Prevention and Fight Against Emerging and Re-emerging Zoonoses, Yaoundé, Cameroon
Hadja, Limane; School of Veterinary Medicine and Science, University of Ngaoundéré, Ngaoundéré, Cameroon
Kilekoung Mingoas, Jean Pierre; School of Veterinary Medicine and Science, University of Ngaoundéré, Ngaoundéré, Cameroon
Saegerman, Claude ; Université de Liège - ULiège > Département des maladies infectieuses et parasitaires (DMI) > Epidémiologie et analyse des risques appliqués aux sciences vétérinaires
Language :
English
Title :
Parallel multi-criteria decision analysis for sub-national prioritization of zoonoses and animal diseases in Africa: The case of Cameroon.
Jones K.E.; Patel N.G.; Levy M.A.; Storeygard A.; Balk D.; Gittleman J. L.; et al. Global trends in emerging infectious diseases. Nature 2008, 21, 990–993. https://doi.org/10.1038/nature06536 PMID: 18288193
Grace, D.; Songe, M.; Knight-Jonesambi, T. Impact of neglected diseases on animal productivity and public health in africa.–Africa–OIE Regional Commission –2015. https://www.woah.org/fileadmin/Home/eng/Publications_%26_Documentation/docs/pdf/TT/2015_AFR1_Grace_A.pdf (accessed on 15 August 2022).
Gebreyes W.A.; Dupouy-Camet J.; Newport M.J.; Oliveira C.J.; Schlesinger L.S.; Saif Y.M.; et al. The global One Health paradigm: challenges and opportunities for tackling infectious diseases at the human, animal, and environment interface in low-resource settings. PLoS Negl. Trop. Dis. 2014; 8: e3257. https://doi.org/10.1371/journal.pntd.0003257 PMID: 25393303
Motta P.; Porphyre T.; Handel I.; Hamman S.M.; Ngu Ngwa V.; Tanya V.; et al. Implications of the cattle trade network in Cameroon for regional disease prevention and control. Sci Rep. 2017, 7, 7:43932. https://doi.org/10.1038/srep43932 PMID: 28266589
Bouslikhane, M. Cross border movements of animals and animal products and their relevance to the epidemiology of animal diseases in Africa. In Proceedings of the 21st Conference of the OIE Regional Commission for Africa., Rabat, Morocco, 2015.
Republic of Cameroun, Strategy and National Action Plan for biodiversity. Version II, MINEPDED, Yaounde Cameroon, 2012.
M. Mouiche M.M.; N. Wafo E.E.; Mpouam S.E.; Moffo F.; K. Feussom J.M.; N. Ngapagna A.; Mfoppit Y.; Saegerman C.; Mamoudou A et al. Zoo-sanitary situation assessment, an initial step in country disease prioritization process: systematic review and meta-analysis from 2000 to 2020 in Cameroon. Pathogens 2023, 12(9), 1076; https://doi.org/10.3390/pathogens12091076 PMID: 37764884
Salyer S.J.; Dilver S.R.; Simone K.; Behravesh B.C. Prioritizing zoonoses for global health capacity building-themes from One Health zoonotic disease workshops in 7 countries, 2014–2016. Emerg. Infect. Dis. 2017, 23, 57–63. https://doi.org/10.3201/eid2313.170418 PMID: 29155664
Mpouam S.E.; Mingoas K.J.P.; Mouiche M.M.M.; K. Feussom J. M; Saegerman C. Critical systematic review of zoonoses and transboundary animal diseases’ prioritization in Africa. Pathogens 2021, 10(8), 976. https://doi.org/10.3390/pathogens10080976 PMID: 34451440
FAO. Good emergenecy management practices. The essentials. Edited by Nick Honhold, Ian Douglas, William Geering, ArnonShimshoniand Juan Lubroth. FAO manuel No. 11. Rome, Italy, 2011.
FAO. Manual for the management of the operations during an animal health emergency. FAO Draft version 2, Rome, Italy, 2020.
Brookes V.J.; Del Rio Vilas V.J.; Ward M.P. Disease prioritization: what is the state of the art? Epidemiol. Infect. 2015, 143, 2911–2922. https://doi.org/10.1017/S0950268815000801 PMID: 25876939
Rist C.L.; Arriola C.S.; Rubin C. Prioritizing zoonoses: A proposed One Health tool for collaborative decision-making. PLoS ONE 2014, 9(10), e109986. https://doi.org/10.1371/journal.pone.0109986 PMID: 25302612
Phylum, Listing and categorization of priority animal diseases, including those transmissible to Humans. Methodological manual, Toulouse-France, 2010.
Heffernan C. Panzootics and the poor: devising a global livestock disease prioritisation framework for poverty alleviation. OIE Rev. Sci. Tech. 2009, 28, 897–907. https://doi.org/10.20506/rst.28.3.1934 PMID: 20462148
Humblet M.-F.; Vandeputte S.; Albert A.; Gosset C.; Kirschvink N.; Haubruge E.; et al. Multidisciplinary and evidence-based method for prioritizing diseases of food-producing animals and zoonoses. Emerging. Infect. Dis. 2012, 18(4). https://doi.org/10.3201/eid1804.111151 PMID: 22469519
Bianchini J.; Humblet M.-F.; Cargnel M.; Van der Stede Y.; Koenen F.; de Clercq K.; et al. Prioritization of livestock transboundary diseases in Belgium using a multicriteria decision analysis tool based on drivers of emergence. Transbound. Emerg. Dis. 2020, 67(4). https://doi.org/10.1111/tbed.13356 PMID: 31520577
Dossa L. H.; Abdulkadir A.; Amadou H.; Sangare S.; Schlecht E. Exploring the diversity of urban and peri-urban agricultural systems in Sudano-Sahelian West Africa: An attempt towards a regional typology. Landsc urban plan. 2011, 102, 197–206. https://doi.org/10.1016/j.landurbplan.2011.04.005
Labonne, M.; Magrong P.; Oustalet, Y. Le secteur de l’élevage au Cameroun et dans les provinces du grand Nord: situtation actuelle, contraintes, enjeux et défis., 2003, 12p. https://hal.archives-ouvertes.fr/ hal-00139191.
MINEPIA (Ministry of Livestock, Fisheries & Animal Industries), National and regional reports: annual livestock productions. Tech. Rep., Ministry of Livestock, Fisheries and Animal Industries of Cameroon, Yaounde, Cameroon, 2014.
Meulman, J. J.; Heiser, W. J. SPSS Inc. (Eds.)., SPSS categories 13.0. Chicago, USA: SPSS Inc, 2004.
Linting M.; Meulman J.J.; Groenen P.J.F.; Van der Kooij A.J. Nonlinear principal components analysis: Introduction and application. Psychol. Methods 2007, 12(3), 336–358. https://doi.org/10.1037/1082989X.12.3.336 PMID: 17784798
Roessler R.; Mpouam S.E.; Muchemwa T.; Schlecht E. Emerging development pathways of urban livestock production in rapidly growing West Africa cities. Sustainability 2016, 8(11), 1199. https://doi.org/10.3390/su8111199
Costantini P., Linting M., Porzio G.C., Mining performance data through nonlinear PCA with optimal scaling. Appl. Stoch. Models Bus. Ind. 2010, 26, 85–101. https://www.academia.edu/19470789/ Mining_performance_data_through_nonlinear_PCA_with_optimal_scaling (accessed on 20 July 2021).
Dominguez-Rodrigo M.; de Juana S.; Galan A.B.; Rodriguez M. A new protocol to differentiate trampling marks from butchery cut marks. J. Archaeol. Sci. 2009, 36(12), 2643–2654. https://doi.org/10.1016/j.jas.2009.07.017
IBM Corp, 2011. IBM SPSS Statistics for Windows. Version 20.0, 2011.
Rousseeuw P. J.; Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Mathematics 1987, 20, 53–65. https://doi.org/10.1016/0377-0427(87)90125-7
Kaufman L.; Rousseeuw P. J. Finding groups in data: An introduction to cluster-analysis. Chichester, New York, Eds. Wiley, 1990.
Dossa L.H.; Sangare M.; Buerkert A.; Schlecht E. Production objectives and breeding practices of urban goat and sheep keepers in West Africa: regional analysis and implications for the development of supportive breeding programs. SpringerPlus, 2015, 4, 281. https://doi.org/10.1186/s40064-015-1075-7 PMID: 26101733
Anses. Hiérarchisation de 103 maladies animales présentes dans les filières ruminantes, équidés, porcs, volailles et lapins en France métropolitaine, France, 2012.
Cardoen S.; Van Huffel X.; Berkvens D.; Quoilin S.; Ducoffre G.; Saegerman C.; et al. Evidence-based semiquantitative methodology for prioritization of foodborne zoonoses. Foodborne Pathog. Dis. 2009, 6, 1083–1096. https://doi.org/10.1089/fpd.2009.0291 PMID: 19715429
Havelaar A.H.; van Rosse F.; Bucura C.; Toetenel M.A.; Haagsma J.A.; Kurowicka D.; et al. Prioritizing emerging zoonoses in the Netherlands. PloS One 2010, 5, e13965 (2010). https://doi.org/10.1371/journal.pone.0013965 PMID: 21085625
Daniels J.; Werner P.; Bahill T. Quantitative methods for tradeoff analyses. Syst. Eng. 2001, 4,190–211. https://www.academia.edu/34147466/Quantitative_Methods_for_Tradeoff_Analyses (accessed on 20 July 2021).
Saaty T.L. Fundamentals of decision making and priority theory with the Analytic Hierarchy Process—Vol. VI of the AHP Series. RWS Publications, Pittsburgh, 2000.
Clemen R.T.; Reilly T. Making hard decisions with decision tools. Duxbury, Pacific Grove, 2001.
McKenzie J.; Simpson H.; Langstaff I. Development of methodology to prioritise wildlife pathogens for surveillance. Prev. Vet. Med. 2007, 81, 194–210. https://doi.org/10.1016/j.prevetmed.2007.04.003 PMID: 17482697
Cox R.; Sanchez J.; Revie C.W. Multi-criteria decision analysis tools for prioritising emerging or reemerging infectious diseases associated with climate change in Canada. PLoS One 2013, 8, e68338. https://doi.org/10.1371/journal.pone.0068338 PMID: 23950868
Brookes V.J.; Hernández-Jover M.; Neslo R.; Cowled B.; Holyoake P.; Ward P.M. Identifying and measuring stakeholder preferences for disease prioritisation: a case study of the pig industry in Australia. Prev. Vet. Med. 2014, 113, 118–131 (2014). https://doi.org/10.1016/j.prevetmed.2013.10.016 PMID: 24211106
PNPLZER (Program for the Prevention and Fight against Emerging and Re-emerging Zoonozes). Zoonotic disease prioritization for inter-sectoral sectoral engagement in Cameroon. Workshop summary, Yaounde, Cameroon, 2016. https://www.cdc.gov/onehealth/pdfs/Cameroon-english-508.pdf (accessed 10/09/2020.
Hongoh V.; Gosselin P.; Michel P.; Ravel A.; Waaub J.-P.; Campagna C.; et al. Criteria for the prioritization of public health interventions for climate-sensitive vector-borne diseases in Quebec. PloS One, 2017, 12 (12): e0190049 (2017). https://doi.org/10.1371/journal.pone.0190049 PMID: 29281726
Li L.; Yang Z.; Dang Z.; Meng C.; Huang J.; Meng H.; et al. Propagation analysis and prediction of the COVID-19. Infect Dis Model. 2020, 5, 282–292. https://doi.org/10.1016/j.idm.2020.03.002 PMID: 32292868
MSHP (Ministry of Health and Public Hygiene); MESRS (Ministry of Higher Education and Scientific Research); MINADER (Ministry for Agriculture and Rural Development); MINEF (Ministry of Water and Forests); MIRAH (Ministry of Animal and Fisheries Resources); MINSEDD (Ministry of Wholesomeness, Environment and Sustainable Development), One Health zoonotic diseases prioritization for multisectoral engagement in Côte D’Ivoire. Workshop summary, Abidjan, Côte D’Ivoire, 2017. https://www.cdc.gov/onehealth/pdfs/cote-dlvoire-english-508.pdf (accessed 12/09/2020).
Mackenzie J.S.; Smith D.W. COVID-19: a novel zoonotic disease caused by a coronavirus from China: what we know and what we don’t? Microbiol. Aust. 2020, 41(1), 45. https://doi.org/10.1071/MA20013 PMID: 32226946
MINEPIA (Ministry of Livestock, Fisheries & Animal Industries), Surveillance systems for investigation and response in animal health. Directorate of Veterinary Services-Ministry of Livestock, Fisheries and Animal Industries, Yaounde, Cameroon, 2016.
Modiyinji A.F.; Atsama M.A.; Monamele G.C.; Nola M.; Njouom R. High seroprevalence of hepatitis E among pigs suggests an animal reservoir in Cameroon. J. Infect. Dev. Ctries. 2018, 31, 12(8), 676–679. https://doi.org/10.3855/jidc.10310 PMID: 31958332
S de Paula V; Wiele M.; Mbunkah A.H.; Daniel A.M.; Kingsley M.T.; Schmidt-Chanasit J. Hepatitis E virus genotype 3 strains in domestic pigs, Cameroon. Emerg Infect. Dis. 2013, 19(4), 666–8. https://doi.org/10.3201/eid1904.121634 PMID: 23751099
OIE, Maladies de la liste de l’OIE 2006: OIE—World Organisation for Animal Health, 2006. https://www.oie.int/fr/sante-animale-dans-le-monde/maladies-de-la-liste-de-loie-2006/ (accessed 7.9.20).
PNPLZER (Program for the Prevention and Fight against Emerging and Re-emerging Zoonozes), Atelier de priorization des zoonoses par zone agro-ecologique. Workshop report, PNPLZER, Yaounde, Cameroon, 2020.
Stewart T. Dealing with uncertainties in MCDA. In: Figueira J., Greco S., Ehrgott M. (Eds.), Multiple Criteria Decision Analysis: State of the Art Surveys, Springer, chapter 11, 2005.
Mingers J.; Rosenhead J. Problem structuring methods in action. Eur. J. Oper. Res. 2004, 152 (3), 530–554. https://doi.org/10.1016/S0377-2217(03)00056-0
Greco S.; Matarazzo B.; Slowinski R. Rough sets theory for multicriteria decision analysis. Eur. J. Oper. Res. 2001, 129, 1–47. https://doi.org/10.1016/S0377-2217(00)00167-3