A retrospective study and predictive modelling of Newcastle disease trends among rural poultry of eastern Zambia

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dc.contributor.author Mubamba, C.
dc.contributor.author Ramsay, G.
dc.contributor.author Abolnik, Celia
dc.contributor.author Dautu, G.
dc.contributor.author Gummow, Bruce
dc.date.accessioned 2017-03-17T10:21:02Z
dc.date.issued 2016-10
dc.description.abstract Newcastle disease (ND) is a highly infectious disease of poultry that seriously impacts on food security and livelihoods of livestock farmers and communities in tropical regions of the world. ND is a constant problem in the eastern province of Zambia which has more than 740 000 rural poultry. Very few studies give a situational analysis of the disease that can be used for disease control planning in the region. With this background in mind, a retrospective epidemiological study was conducted using Newcastle Disease data submitted to the eastern province headquarters for the period from 1989 to 2014. The study found that Newcastle Disease cases in eastern Zambia followed a seasonal and cyclic pattern with peaks in the hot dry season (Overall Seasonal Index 1.1) as well as cycles every three years with an estimated provincial incidence range of 0.16 to 1.7% per year. Annual trends were compared with major intervention policies implemented by the Zambian government, which often received donor support from the international community during the study period. Aid delivered through government programmes appeared to have no major impact on ND trends between 1989 and 2014 and reasons for this are discussed. There were apparent spatial shifts in districts with outbreaks over time which could be as a result of veterinary interventions chasing outbreaks rather than implementing uniform control. Data was also fitted to a predictive time series model for ND which could be used to plan for future ND control. Time series modelling showed an increasing trend in ND annual incidence over 25 years if existing interventions continue. A different approach to controlling the disease is needed if this trend is to be halted. Conversely, the positive trend may be a function of improved reporting by farmers as a result of more awareness of the disease. en_ZA
dc.description.department Production Animal Studies en_ZA
dc.description.embargo 2017-10-31
dc.description.librarian hb2017 en_ZA
dc.description.sponsorship Australian Awards (OASIS ID: STOOOK8), James Cook University, Australia with contributions from the National Research Foundation in Pretoria, South Africa. en_ZA
dc.description.uri http://www.elsevier.com/locate/prevetmed en_ZA
dc.identifier.citation Mubamba, C, Ramsay, G, Abolnik, C, Dautu, G & Gummow, B 2016, 'A retrospective study and predictive modelling of Newcastle disease trends among rural poultry of eastern Zambia', Preventive Veterinary Medicine, vol. 133, pp. 97-107. en_ZA
dc.identifier.issn 0167-5877 (print)
dc.identifier.issn 1873-1716 (online)
dc.identifier.other 10.1016/j.prevetmed.2016.09.017
dc.identifier.uri http://hdl.handle.net/2263/59466
dc.language.iso en en_ZA
dc.publisher Elsevier en_ZA
dc.rights © 2016 Elsevier B.V. All rights reserved. Notice : this is the author’s version of a work that was accepted for publication in Preventive Veterinary Medicine. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. A definitive version was subsequently published in Preventive Veterinary Medicine, vol. 133, pp. 97-107, 2016. doi : 10.1016/j.prevetmed.2016.09.017. en_ZA
dc.subject Rural poultry en_ZA
dc.subject Trends en_ZA
dc.subject Modelling en_ZA
dc.subject Newcastle disease (ND) en_ZA
dc.subject.other Veterinary science articles SDG-01 en_ZA
dc.subject.other Veterinary science articles SDG-02 en_ZA
dc.subject.other SDG-02: Zero hunger
dc.subject.other SDG-01: No poverty
dc.title A retrospective study and predictive modelling of Newcastle disease trends among rural poultry of eastern Zambia en_ZA
dc.type Postprint Article en_ZA


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