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Modelling of malaria incidence in Akure, Nigeria

Negative binomial approach

Bibliographic Data

ID11984155
AuthorsOlusola Samuel Makinde (0000-0003-3786-354X, Federal University of Technology, corresponding author), Gbenga J Abiodun (0000-0002-6805-2128, Southern Methodist University), Oluwasegun Ojo (0000-0001-9629-6990, Federal University of Technology), Oluwasegun Taiwo Ojo
Year2020
Volume86
Issue3
Pages1327-1336
Publication date2020-01-07
Open AccessYes
TypeARTICLE
VenueGeoJournal (JOURNAL)
Journal identifiersISSN: 0343-2521 • E-ISSN: 1572-9893
PublisherSpringer Science+Business Media (PUBLISHER • DE)
DOI10.1007/s10708-019-10134-x
OpenAlexW2999775081
LanguageEN
Citations received2
References cited30

Count data · Geography · Incidence (geometry · Malaria · Negative binomial distribution · Statistics · COVID-19 epidemiological studies · Demography · Malaria Research and Control · Mathematics · Medicine

  • Generalized Linear Models to Forecast Malaria Incidence in Three Endemic Regions of Senegal

    Open Access•Ousmane Diao, P-A Absil et al.•International Journal of…•2023

  • Geospatial clustering and hot spot detection of malaria incidence in Bahawalpur district of Pakistan

    Open Access•Munazza Fatima, Ibtisam Butt et al.•GeoJournal•2021

  • A Dynamical and Zero-Inflated Negative Binomial Regression Modelling of Malaria Incidence in Limpopo Province, South Africa

    Open Access•Gbenga J Abiodun, Olusola Samuel Makinde et al.•International Journal of…•2019

Citations per year0.4
Citation span2021 - 2023 (3)
Highly citedNo

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