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Modelling of malaria incidence in Akure, Nigeria: negative binomial approach

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Abstract

The connections between malaria incidence and climate variability have been studied in recent time using some mathematical and statistical models. Many of the statistical models in literature focused on time series approach based on Box–Jenkins methodology. However, fitting time series model based on the Box–Jenkins methodology may be challenging. Most malaria incidence data are count and are over-dispersed. In this study, negative binomial models were formulated for fitting malaria incidence in Akure—one of the epidemic cities in Nigeria. In particular, negative binomial models were formulated for each of the number of outpatient individuals, number of inpatient individuals and mortality count as a function of some climate variables. It was found that an increase in minimum temperature and relative humidity at lag 1 significantly increased the chance of malaria transmission and thereby leads to an increase in the number of inpatient and outpatient individuals, as well as the total number of malaria cases. The minimum temperature, rainfall amount and relative humidity of the study area have a significant impact on the increase of number of inpatient and outpatient individuals while mortality count depends on the total number of reported malaria cases. The findings from this study is to offer in-depth understanding on climate-malaria incidence linkages in Akure, Nigeria.

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(Source: Ayeni 2011)

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Acknowledgements

This project was supported by the Fogarty International Center of the National Institutes of Health (NIH) under Award Number D43TW009343 and the University of California Global Health Institute (UCGHI). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH or UCGHI.

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Authors and Affiliations

  1. Department of Statistics, Federal University of Technology, P.M.B 704, Akure, Nigeria

    Olusola Samuel Makinde & Oluwasegun Taiwo Ojo

  2. Department of Mathematics, Southern Methodist University, Dallas, TX, 75275, USA

    Gbenga Jacob Abiodun

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  1. Olusola Samuel Makinde
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  2. Gbenga Jacob Abiodun
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  3. Oluwasegun Taiwo Ojo
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Correspondence to Olusola Samuel Makinde.

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Makinde, O.S., Abiodun, G.J. & Ojo, O.T. Modelling of malaria incidence in Akure, Nigeria: negative binomial approach. GeoJournal 86, 1327–1336 (2021). https://doi.org/10.1007/s10708-019-10134-x

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